Cloud Computing Big Data

Glassdoor (One Of The World’s Biggest Job and Recruiting Sites) To Partner With Cloud Investor Battery Ventures (A Global Investment Firm) for Second Year to Determine and Reveal Top Highest-Rated Private and Highest-Rated Public Cloud-Computing Companies – Where You Can Find The Highest Levels Of Satisfaction At Work In 2018.


With cloud computing on the rise, exceptional cloud growth, and cloud computing companies among the hottest in tech, the most common question among employers is “Which publicly and privately held companies are really the best to work for in 2018? You might be wondering: “Is workplaces’ culture and employee happiness in the current, ultra-competitive tech economy crucial?” Very important! The higher levels of satisfaction at work, the better results of their work will be shown.

With some cloud companies are growing much faster than others in the cloud computing market, it takes time to identify top best cloud computing companies and CEOs to work for this year. This list uncovering the Highest Rated Public and Private Cloud Computing Companies to Work For in 2018 represent those where employees’ satisfaction at work is reported at its highest. In this article, I will tell you more about the most excellent companies.

Glassdoor’s Suggestion On The Best Cloud Computing Companies In 2018

Mendix

While some researchers consider Amazon Web Services the best cloud computing company, Glassdoor says that title actually belongs to Mendix - a low-code software platform founded in the Netherlands in 2005 - in the PaaS and hosted private cloud segment.

As the fastest and easiest high-productivity platform as a service (PaaS) with its membership in the Cloud Foundry Foundation to create and continuously improve multi-channel applications at scale, Mendix promises business and IT to have such a pleasant time working together. Also, the speed at which they can realize value is superior.

Reltio

 As a multi-tenant cloud PaaS platform, Reltio supplies highly customized products and services to help with machine learning across all industries. The super modern data management platform of Reltio solves the hardest data management problems for any industry use case. Regardless of the size of the enterprise, Reltio’s products promise to use a wide variety of anonymized data to help them to grow faster, reduce the expense spent on IT, and remain scalable.

Zoho

Zoho provides your entire business with more than just a single product or a tightly favorite apps’ integration. The cloud services will aid to cope with such business processes across your organization as 40+ integrated applications hosting and running, business intelligence, database management, manage all day-to-day activities, eCommerce hosting, boost sales, email hosting, ERP, step up productivity and collaboration, web content management, and website hosting.

Google Cloud Platform

A PaaS (Platform as a Service) platform, Google Cloud Platform – the most valuable brand in the world as of 2017 provides tools and services to build and deploy cloud-ready sample apps, extensions while still helping business users to minimize the operational costs. Also, Google Cloud Platform helps to increase their productivity by opening integration to connect any digital products regardless of who developed them and where they are.

Among 108 offering services and products, users are spoilt for choices to operate compute process, network (CDN, VPN), storage (Cloud Storage, Persistent Disk, Cloud Storage for Firebase, Cloud Filestore), management, security, data management (SQL, MySQL, PostgreSQL, and NoSQL) as well as analytics and machine learning (Apache Airflow), AI, Internet of Things (IoT), mobile, Blockchain, integrations and migration systems.

SAP Cloud Platform

SAP Cloud Platform

An in-memory data platform-as-a-service (PaaS) by SAP SE, SAP Cloud Platform allows connection and integration with other data and business processes in a secure cloud computing environment.

SAP HANA database management system - a strong brand that has received widespread recognition in the business and technology world rapidly became the #1 growing technology solution among all cloud computing companies including Asian Paints, Coca-cola, Accenture, Mercedes-AMG, eBay, Lenovo, Infosys, Colgate Palmolive, Sandisk, Unilever, and Cisco. Starting from its inception on October 16, 2012, and being deployed as an on-premise appliance or in the cloud on May 13, 2013, SAP HANA now has more than 815,000 active customers, who consider SAP HANA as their most popular cloud tools.

Their services cover mobile services, storage, database and storage, integration & orchestration, migration, developer tools, management tools, media services, digital experience, security, data management & analytics, machine learning, app integration, solution extensibility, customer engagement, SAP Cloud Platform Internet of Things (IoT), business productivity spheres.

Netskope Security Cloud

Netskope Security Cloud

Best known as “cloud-security platform for the enterprise,” the Netskope Security Cloud Platform offers the world’s largest organizations unmatched vantage points. Also, it provides fast and intuitive visibility into sanctioned or unsanctioned cloud experience for any individual and application over any web without sacrificing security. Thus, 100 percent of users on-premises, mobile, and remote as well as a lot of companies including Levi’s, ILM, Toyota, and nVidia can deliver superior digital experiences and successfully migrate billions of transactions across thousands of services.

Outsystems

Outsystems Cloud provides PaaS (Platform as a Service), all to accelerate business transformation without the hassle of managing physical software infrastructure. A perfect solution to build web applications like Outsystems is expected to get much more success in the cloud computing sphere in the near future.

Meta description: The skill namely data analytics maybe something extremely advanced and macro to various people. Here’re some simple ways to conquer this skill.

Introduction

Good data analytics is the key to your success, especially in the increasingly developing world today. Therefore, improve this essential strategy will make you more competitive in increasing demands of the current labor market.


How often do you assess your data analytics? If you are still not confident enough to answer this question, it’s time to read this entire article provided with the simplest methods for effective data analytics.

If you are not good at data analytics, learn more about the proper training to help you improve your analytical skills right. Although good data analysis can’t be learned and enhanced overnight, it can be boosted faster with daily practicing.

We gave out top 7 habits to improve highly effective data analytics that can support your work, business, and life matters better. Let’s check it out!

How effective data analytics important to humans

Strength in the effective analytical skills will assist people in both your business and daily life. Regarding work, effective analytical skills will aid you to manage and execute your projects, get new ideas, and solve technical problems well.

If you have been spending years to find out the way for productive business without the little hope of success, take a look back at your data analytics. This skill was lowly appreciated and paid attention to in your life and work.

Think again to see if you did not invest properly in good data analytics or not. And, read through the lists of what an effective analytical skill will offer you as mentioned below.
Good analysis skills are extremely important to the human in:

• Good information gathering and reporting 
• Solutions for both simple and complicated  problems
• Support the making of important decisions
• Summary of big statistical data
• Determine the trends of the issue
• Streamline the workflow well 
• Increase the project effectiveness

What decides the success of people does not depend on the emotions but the right decisions. The better the decision is, the closer you are to succeed. Moreover, people with good analytical skills are closer to success than others.

These successful individuals can’t be influenced by their emotions, passions, or external effects. Instead, they know how to apply their analytical skills to overcome the challenges, no matter how complex or critical the difficulties are.

Top simplest ways to practice effective data analytics  

It is obvious that the right decisions will create the positive results whether you are running a business yourself or simply trying to gain promotion at work. Having sharp analytical skills will make your plans change in a whole new way.

There are a number of effective tricks to increase the ability to analyze data in studying and business. Enhancing your analytical skills will create more opportunities for your future development.

That’s why you need to seek yourself more aggressive and active data analytics proactively. Here are some suggestions on how to improve your data analytics.

Start reading more books

It is time to begin your reading adventures today and read books as much as you can. You may wonder if the real effects of book reading to the improvement of your data analytics because it is an extremely simple trick to apply.

However, we’re sure that you will be amazed by the hidden miracles inside this method. By asking several questions about what you’ve learned when reading books, you are progressing your brain thinking more than usual.

Another useful technique to enhance the ability of data analytics is to ask others questions and compare the writer’s views with your thoughts. Take note new ideas, thoughts, and achievements that you achieve after reading any book.

This active reading method will encourage your brain to acknowledge new information as well as allow you to think in several new ways.

Take a walk outside

You should better go out daily for a walk because this simple activity will help you improve your data analytical skills incredibly. Always remember to put your mind to the active state by observing carefully what you can see around.

And, think of things surrounding into detail too. Try to use your senses as much as possible and ask proper questions about what’s going on surround you.

How much information can you remember after a walk outside? Try to remember what you observed the previous day and rewrite them on your textbook the next day.

Practice Math-related games.

As much as we know, Math is the best subject for perfectly logical thinking and data analyzing skills. You should play some simple Math-related games such as Sudoku and logical, analytical games regularly.

Those fun game are good at speeding up the improvement of your data analytics.

Play games with the highly logical thinking skills

Add some games that require highly logical thinking skills on your favorite game list is also effective for better data analytics. Chess, Bridge card-game, and other brainstorming games with strategic moves are simple tricks for your mind.

Also, both Treasure hunts and Quizzes are extremely useful to increase your data analytical skill. These games are entertaining, interesting without any requirement to take too much effort for them.
Moreover, they help bring mild joy to the whole family with the great cohesion power if you play with your beloved family members.

Focus more on daily conversations. 

When talking to anyone face to face, you should ask him more questions and try to learn something brand new from them. New skills can be the best ways to deal with stress at work, how to develop a successful career or parenting experiences.

Asking questions regularly while talking is essential to keep you engaged actively in the conversation. You can also develop your better communication skills as well as making numerous new friends.

Conclusion

By improving and developing your data analytics, you will improve the completely different mind with new personal abilities. It is the direct way to add new knowledge, handle complex information, and give effective solutions for a successful career.

Thanks to data analytics’ benefits, we synthesize top simplest tricks to practice the skill for you. Remember to practice regularly because if you skip practicing regularly, failure may welcome you soon.

Meta description: To support the business success with effective data analytics is a big question to all entrepreneurs. Our article will give you the exact answer.

Introduction

Today, most of the businesses are aware of the importance of data analytics, nearly 90% of the business leaders think it will change their businesses in a right direction to success.


Most of the investors are purchasing Big Data projects to gain competitive advantages in developing customer relationships, redefining product development, and changing the way the business operation.

However, to make an effective investment in data analytics is not just easy like that. If your goal is to apply data analysis to your business, you should have a clear understanding of Big data and data analytics to make use of them.

After having a good understanding of these essential things, it’s time for you to apply them in running your business effectively. There are various tips that you wish you knew before to drive your company in the right way to gain the achievements.

Use data analytics to support the business success

Making use of data analytics is never an easy issue to the entrepreneurs, especially the leaders. Here are some suggested ways for you.

Always be ready to attract the customers 

The very first step in exploiting the effective data analytics to improve the customer loyalty is to identify the business model of your company. Two main formats include registered and non-registered members.

Membership who attended as the registered members can be easy to identify as a target customer group. For the unregistered business model, the entrepreneurs need to have the plans to identify the target customers among them regularly.

Ways to attract the loyal customers include customer services to such as offering special offers through phone care, phone applications, or sending discount offers.

Control or evaluate the customer satisfaction through customer surveys in the business websites, poles or questionnaires. Moreover, you should have long-term plans to reduce the obstacles for the customers in the easier purchasing process.

Analyze the suitability of the products or services with the needs of customers to understand what the customers are interested in [1]. This tip will help you find the right path to adjust and produce the amazing products that fit their needs and tastes.

Understand the decision-making process

While your ideas and emotions are easy to change, data analytics is the basis for empowering the leaders to make rational decisions. However, a business does not have to need data analytics to be perfect and successful.

It is extremely crucial to learn to ask the correct questions and then, get into the problems well to find the right solutions that can be supported by effective data analytics.

The simplest way to identify the right problems and questions is to identify the most important decisions of a leader. Based on the available data, you can analyze the wrong operation in the company to adjust the new plans for your business.

Understand the wrong strategies in business can help an entrepreneur create the more careful plans for the next projects and a more suitable rational business operation.

Never complicate the data analysis

A talented data scientist will not handle every business problems. They know clearly how to choose and focus only on the main issues that influence greatly on the value of the business and think of the effective solutions for that problems.

Think simply on the data analytics to support the successful activities of a business. The key point is to simplify the analysis to make those data usable and provide full advantages for you to plan for the right way of the business management.

Find the relevant factors

To solve a problem of a business, it is essential to find out the dependent variables to solve the problem and analyze the independent variables.

Independent variables are not always clear while doing any researches for data analyzing. The initial requirements of a data analysis project can cause the misidentification of the business goals or business metrics to be evaluated well.

For example, if the directors asked you to increase the number of customers, you should understand that the leaders want to increase the total revenue. And, the rate of return is the main indicator that the director cares about.

Therefore, to find the important index that needs for the data analysis, a discussion with all the related people of the project should be organized. During the discussion, try to identify the right things concerned by the business leaders.

Set suitable standards

Set the standards that help to show team effectiveness and the goals for the whole team to work on. To do this, you need to collect the old data, internal and external impacts to find out an accurate standard.

First, you need to search for the current benchmark of the peer companies to compare yours with that one. Combine it with the information of your business resources to get an overview of your business effectiveness to others.

Thanks to that activities, you can set the most suitable standards that fit your company.

Make plans for periodic reports

Once you set up the measurable standards and objectives for your company, you need to plan your implementation for periodic reports to achieve the final goals. This activity supports the leaders in accessing the working effectiveness of individuals.

Periodic meetings allow you to have the right adjustment for the plans to help increase the efficiency of your work operation. Each team or department in the company with high effectiveness will boost the business success faster

Monthly reports illustrate the whole performance of all departments not only helps all the staffs use the data more in their work but also uses data analytics more wisely to increase the team effectiveness.

Conclusion

Using data analytics to aim at success for your business is not very easy, but you can learn to improve it gradually. We hope that you can have the best methods to keep your business going on the right track with our suggestions.

In the modern life, the big data analytics’ significant impacts and benefits are undeniable. If you’ve had no idea on what big data analytics is, please keep reading our article to stay updated.

Introduction

Big data analytics investigates a huge amount of data to reveal the hidden correlations, insights, and patterns. With the fast-paced development of technology, it’s easy as a piece of cake to analyze your data and find out the answers from it. Understanding big data analytics thoroughly will be a big advantage as it could help you to develop your business faster than ever. If you want to grasp more useful information about big data analytics, this article is written for you. So keep calm and read our article.

Big data analytics’ history and evolution

The concept of big data has been known for years; all organizations understand clearly that if they could collect all the data that pours into their businesses, they could analyze the data and get considerable value from it and drive profits. In the 1950s, decades before anyone expresses the term “big data,” enterprises were making use of basic analytics to discover trends and insights.


The new advantages that big data brings to the table are speed and effectiveness. While in the past, a business gathered, analyzed and gave out information that could be beneficial and used for future decisions, nowadays, business could pinpoint insights for prompt decisions. The strong capability to work faster gives companies a severe competition that they didn’t have before.

Why is big data analytics pivotal?

Big data analytics allows enterprises to exploit their data and make use of it to create new opportunities. Hence, it leads to more effective operations, smarter business moves, more satisfied customers and of course, higher profits. In the recent report of Tom Davenport – IIA Director of Research, so-called Big Data in Big Companies, interviews around 50 enterprises to comprehend how they make use of big data. And here is what he found:

Cost reduction

Regarding storing big amounts of data, big data technologies like Hadoop and cloud-based analytics create a considerable cost advantage. Also, they could detect more effective ways of doing business.

Faster and better decision making

With the fast speed of in-memory analytics and Hadoop, came along with the capability of analyzing new data sources, enterprises are capable of analyzing information instantly and make decisions based on what they got from the data analytics.

New products and services

With the capability to discover customer demands and evaluate customer satisfaction through analytics comes the power to provide customers with exactly what they want. Davenport also points out that thanks to big data analytics, more businesses are generating new products that meet the customers’ needs and requirements.

Big data analytics in the modern world

The majority of companies have big data. Also, they understand the huge need for collecting that data and take advantage of it.

High – performance analytics help you do things that you had never thought about before as the data volumes were just too big. For example, you could get well-timed insights to make decisions about wink opportunities, get accurate and detailed answers for difficult – to – solve problems and find out new growth opportunities.

If you are looking for an analytic solution for your business, I highly recommend In-Memory Analytics which is from SAS Event Stream Processing to SAS Visual Analytics. In – memory will help you get instant insights from your data. Plus, it is used for different types of businesses:

Travel and hospitality

Keeping customers satisfied and happy plays an important role in all fields, especially travel and hotel industry. However, measuring the customer satisfaction index promptly is such a challenging task. Big data analytics give travel and hospitality businesses the capability of collecting customer data, applying analytics to identify the potential problems instantly and have timely actions.

Healthcare

Big data provides the healthcare industries various benefits. Analyzing and extracting deep insights into health plans, patient records, insurance information and tons of other information will be easier than ever with the support of In-memory Analytics. That is the reason why big data analytics technology is extremely vital to the stable development of health care’s organizations. By analyzing quickly loads of information in both structured and unstructured, healthcare suppliers could offer lifesaving diagnoses and appropriate treatment options immediately.

Government

Tightening the budget without decreasing quality and productivity seems to be the most challenging obstacles for all government agencies. This is particularly trouble with law enforcement agencies which are working hard to reduce the crime rate. That is the reason why a big number of government agencies make use of big data analytics. The technologies would reorganize and update the operations while providing the agencies with a more holistic observation and perspective on criminal activity.

Retail

Customer service has developed in the past years as the shoppers expect the retailers to comprehend exactly what and when they want and need it. And of course, with the strong support of big data analytics, the retailers could meet all those demands in just short time. With analyzed numerous amounts of data from buying habits, customer loyalty programs, and other sources, retailers could not only grasp the deep insights of their customers but also predicts upcoming trends and recommend new products, hence, boost profitability.

Currently, there are a lot of organizations using SAS to support their businesses, such as Royal Bank of Scotland. In fact, SAS has changed completely the way every enterprise did their business and helped them to grow sustainably.

Conclusion

On the whole, big data analytics is highly important for all fields nowadays. With the solid support of big data analytics, we could shorten our process, create significant values and drive improvements. Hopefully, after reading this article, you could have grasped some background knowledge on big data analytics. Keep calm and learning, I strongly believe that you will become an excellent analyst in the near future. Wish you all the best.

“Keep up with the latest trend of 2018 regarding database!”


Database has proven to be very effective and beneficial for financial Institutions around for years and few would dispute the benefits it brings to organizations that have more data stored than ever before. There’s a reason why the market size of database is predicted to almost certainly break past the $40 billion mark in 2018. It is strong, thriving, and constantly evolving.

1. Cognitive technologies – the real opportunities for business are on the rise

A powerful, open, and connected tool set to increasingly do tasks that once required humans, boost analytical capabilities, increase automation opportunities, and enhance the investment decision-making process, cognitive technologies, in the eyes of many leaders, are the most disruptive forces on the horizon.

Gone are the days when the computing systems only capture, move, and store unstructured data -- without understanding it: Cognitive solutions in 2018 will not only understand different types of data such as lab values in a structured database or the text of a scientific publication but drive huge transaction volumes that are hard to achieve otherwise.

Artificial intelligence-based systems trained to understand technical, industry-specific content, cognitive technologies extend the power of information technology to tasks traditionally performed by humans. And by using the advanced reasoning, predictive modeling, and machine learning techniques to advance research faster, they can enable organizations to break prevailing trade-offs between speed, cost, and quality.

2. There will be more growth in prescriptive analytics

Prescriptive analytics involves predicting the spending habits of each customer by analyzing consumers’ interaction with an online retailer when they do “site search”– transactions, web browsing, social media activity, interests, demographics, transforming it into meaningful trends, and thus, improving your customer relationship.

Applied to numerous industries and other facets of business, database is basically a roadmap to better business. Combining database with predictive analytics can be a huge benefit to any organization. Your business can connect the dots and uncover trends in your sales and customer behavior and you will be able to make really quick strategic decisions with the data you have.

For example: Predictive Analytics enable IBM’s business leaders to increase profitability, prevent fraud, and even measure the social media impact of marketing campaigns. And Microsoft — by integrating predictive analysis into their sales process, they are getting more accurate sales predictions from top down.

3. Machines Learning Speed Up Remediation

One of the main limitations of being human is simply our own bodies—and brains. But the days when people did almost everything manually are long gone. We are living in the world where a lot of human-like tasks are performed by machines. As computing power, data collection, and storage capabilities increase, Machine learning is being applied more broadly across industries and applications than ever before. And in the future, humans are expected to augment ourselves with computers and enhance many of our own natural abilities.

With the ability to generalize knowledge from data to perform tasks that human beings do naturally on a daily basis, ML, which is now being used to measure social sentiment and gauge a stock’s value before earnings reports come out, is probably the method people are most excited about right now.

4. Artificial intelligence (AI) will improve cybersecurity

Anyone running a business and the cybersecurity industry itself are not happy with the increasingly common cyber-attacks and cyber-hacks. But with the integration of AI into security systems can stem the growing and evolving cybersecurity risk facing global businesses.

As AI can process and analyze unlabeled data captured to understand new trends and details without any need for human supervision, when it comes to cybersecurity, it can quickly identify and analyze new exploits and weaknesses to help mitigate further attacks.

5. IoT - the next technological revolution will have a big impact on database

As digital and mobile technologies become part of daily life, the Internet of Things (IoT) – the emerging third wave in the development of the internet has been a major influence on the database landscape and will become a large part of database analysis in 2018.

According to Gartner – Global IT Research and Advisory Firm (excerpted from Forecast: The Internet of Things, Worldwide, 2013, published December of 2013): The Internet of Things will include 26 billion units installed and the revenue generated from IoT products and services will be exceeded $300 billion by 2020.

By generating an unprecedented amount of data, IoT is claimed to come to change the society we live in, as well as create entirely new business opportunities for companies by both academic researches and consulting firms. Both consumers and businesses alike have benefited from sensor-based analytics. An example: Customers can take advantage of a system that plays their favorite TV program as soon as they enter the room while UPS (United Parcel Service of America) - one of the largest and most successful logistics and shipping companies in the world uses sensors in its vehicles to improve delivery performance and cut costs.

With database being the axis of all important decisions made in every business, various Industries have experienced a big boom. How has database affected your business?

“Wondering how to choose the best NoSQL database, we’ve got you covered”

These days, NoSQL databases become a good choice for big data and analytics projects because of working effectively with large sets of distributed data. In this article, we will give you deeper insight about solutions like MongoDB, Elasticsearch, OrientDB, Hadoop and Cassandra.

1. MongoDB

Famous for being the most prevalent NoSQL database management system (DBMS), MongoDB is document-oriented and coded in C++. Invented to support high volumes of data, MongoDB carries on a logic of horizontal scalability with sharding and assists to implement a MapReduce system.


One of noticeable features of MongoDB in its 3rd version is that it allows to conduct  advanced research such as geospatial, faceted search, do research on some text as well as define the language, ignore “stop words” (“and”, “or”, “the”…in English for example). Besides, documents are stored in BSON (Binary + JSON) on computer, resulting in some disk space and a better performance.
Only accessing it through the protocol because of no API REST interface is a main downside of using this method. However, to narrow the gap, some external projects give a measure acting as the interface between an API REST and the protocol on the other side. It is possible for  Full-text search yet not in depth. It is can be inconvenience for users because of the lack of  some functionalities such as “ More like this” which is to help users to search for related documents.

2. Elasticsearch

Elasticsearch is another Another well-known cloud-based NoSQL database programmed in Java using Lucene. It is of plugins and tools that you have to pay for.

Elasticsearch has the ability  to implement complicated search on high volumes of data. Horizontal scalability becomes more effortless  since  you merely need to establish a new service. The invention of Elasticsearch has intention to prompt a ‘no SPOF’ (no Single Point Of Failure) engine -i.e. in a cluster of several Elasticsearch.  The data would be kept and the service would continue to work in case that a node would turn off. Without matching a schema,  users can store flat documents like JSON objects.

Put it in another way, using Elasticsearch as a main database system  is not good because  it’s a search engine but a database. It takes users some time before the data would be ready to work. Unlike MongoDB, Elasticsearch will do two queries to handle several documents.

3. OrientDB

Released in 2010 and a 2.0 version in 2015, OrientDB, open source and free for any use, emphasizes on graph-document.

It has no any leader nor any election between nodes from the cluster so OrientDB . In order to be more tolerant towards node failure without interrupting the service or data loss, the data is copied exactly and shared between the various nodes. By being scalable, OrientDB has set up some clusters at the class level to be more efficient. This enables you to search in the User class to find back all the users or to search in one of the clusters to limit the number of results. Besides, OrientDB helps you find quickly relationships with a native function especially when using a social network to find and suggest to users the friends of friends at different stages.

In spite of promises OrientDB made, we do only find few user feedback from a production with a large amount of data. The community isn’t quite big around this tool, which can be quite frightening if a problem might occur.

4. Hadoop & Hive

Hadoop is a Java framework helping some tools from the same ecosystem connect onto it. Thanks to MapReduce jobs, Hadoop abstracts the fact that the load is handed out and run as if the data was stored on one disk. And, Hive- a Java software, will connect itself onto Hadoop and run queries close to SQL syntax

Related:
How to Choose a Cloud Database Provider Correctly
How To Choose A Good Cloud Database
NoSQL Database In The Modern Technology

In the process of working, Hadoop aims to analyse a enormous volume of data shared through some servers. Take it as an example. To retrieving all the tweets with a particular hashtag to analyse the level of satisfaction towards a brand is one of useful functions of it.
One of downsides of this solution is that SQL queries are compiled in MapReduc job for a small-sized data or this tool is not suitable for many servers. Due to not being a search engine, Hive does not undertakes a ‘full-text’ search or faceted search.

5. Cassandra

Invented by Facebook and released in 2008, Cassandra is column-oriented and open source. It is the preference’ s big companies: eBay, Netflix, Github, Spotify, Instagram…

One of great functions of Cassandra is that it can help a strong scalability and to guarantee a high availability. The power is enhanced in proportion to nodes added as users add a Cassandra node within a cluster. Put it in another way, no need to worry about adding a node as it can be the case with other DBMS.

The schema is supposed to be specified in advance since the system remains column-oriented. In duration of retrieval, it is harsh. Also, Retrieval is not exhaustive, no like, no ‘full-text’ or faceted search.

To sum up

Each tool is given to deal with issues arising on specific projects. Combining NoSQL DBMS and/or also add a SQL solution like MySQL or PostgreSQL would be the best solution.  From all given information mentioned  above,  you will select the best fit for the desired task.

What do you know about big data and machine learning? How can they help us? Keep reading my today article.


It is undeniable that in our modern technology, machine learning, and big data have gradually been an indispensable part. In the near future, I think that these above Artificial Intelligence products will dominate all structures in technology. Of course, when machine learning cooperates with big data, it will bring surprising effects in tackling different complicated issues. My today article will help you insight into this field.

Background of big data and machine learning

There is no doubt that combining big data and machine learning is very important in the modern technology. They also bring a lot of opportunities for businessmen.

In fact, the term “machine learning” emerged in IT from the 1990s, however, it has just shown hidden potentials when being used in some applications.

At the meantime, big data is a new product of Artificial Intelligence from 2013. According to a recent report, 90% of the global data was produced by big data in 2015 and 2016.

So, the question is why combining big data and machine learning can produce beneficial outcomes. I will show you better understandings about this issue in the following part.

Why combining big data and machine learning can bring great benefits

1. Facilitating customer segmentation

Determining a group of distinct individuals among those who have common similarities is a normal task in business. To some extent, this seems to be an essential step for all companies and corporations.

It comes as a no surprise that machine learning is very good at implementing this task. High level of accuracy of algorithms in machine learning can ensure the ability to indicate clearly similarities and differences.

Thus, your companies can take advantage of the above power of combining machine learning and big data. To fulfill this, you need to take some crucial steps:

First of all, you should determine what advantages the combination of machine learning and big data can bring to your business. If you are sure that, this implementation can produce fruitful results, it is time for you to focus more on data analytics.

Of course, machine learning does not help to solve all problems exactly but it can build segmentation infrastructure.

2. Targeting feasible and effective

It is also obvious that when you find the way to determine distinct needs for applying the combination of big data and machine learning, you will sooner or later certainly have some good results for your companies.

In some cases, you should spend time for researching into customers’ intention as well. This will help you collect more information and realistic experiences. Big data and machine learning can do this task well.

The way that Google utilizes the combination of big data and machine learning is a very striking example. Thanks to this implementation, Google can handle all complex issues with algorithms.

Link:
Why Is Your Business Data Treated With Such Little Regard?
Big Data Analytics In The Global Market
Big Data Tools Apache Spark And Azure

Ads are also specific targets of big data and machine learning. For instance, Netflix and Pixar have invested effectively in this filed and had a large amount of revenue.

3. Fostering predicative analysis

After collecting and finding information related to customer’s preferred choices and behavior, it is also necessary for companies and corporations to have possible predictions. This can ensure that the combination of big data and machine learning can produce fruitful results.

To make these anticipations feasible, you also need to apply different kinds of algorithms effectively.

4. Providing Foundations for Risk Analysis and Regulation

In many situations, big data creates chances for machine learning to analyze and synthesize quickly. The American Express used this method in finding fraud cases. They took advantage of big data and machine learning in analyzing both previous and present events.

It is highly recommended that your company or corporation can utilize this combination to cut down cases of financial fraud or deficit. IBM is a good example of this action. This organization makes use of big data and machine learning to predict financial risks and manage all revenues.

Conclusion

General speaking, big data and machine learning are two key elements in the modern technology. They will certainly play a superior role in the near future. Of course, if companies and corporations take advantage of these IT products, they can avoid risks and gain more benefits. There are four main reasons why combining big data and machine learning can bring great advantages for us. I do hope that my today article will provide a helpful source for you in this field.

What do you know about big data? Will it increase in the future? Keep reading my today article.

Introduction:

In recent years, big data has been gradually a familiar term to a lot of IT users on modern technology. According to a new report from global big data, this field can make an overwhelming profit in the world with roughly 47 billion US dollars in 2018. So, how big data helps businesses. In other words, what benefits it brings for us. My today article will help you understand this issue thoroughly.

Background of big data

In reality, big data is regarded as a big deal. It can help businesses to cut down costs and make informed choices. Besides, making use of big data analytics also meet the demand of customers.
The simple reason why big data world will surge in 2018 is the modern technology is changing significantly. Technological revolutions are happening day by day. My today article will show you possible variances of big data in the coming decade.

The future trend of big data:

1. Cognitive technology is growing appreciably

It is apparent that one of the most striking changes to big data world is a revolution of cognitive technologies. Undoubtedly, there have been more and more breakthroughs of big data in identifying individuals’ faces and fingerprints.


In addition, automating mechanism is also utilized in various applications.
There are also some certain differences between computing systems in previous times and present cognitive technologies. They are not restricted by some structures like before.

2. It will be an increase in prescriptive analytics

It is undeniable that the analytics mechanism is having more and more progress. Thanks to the application of this feature, companies, and corporations can enhance their effectiveness and work better. All tasks also become easier.

Prescriptive analytics has the main purpose of dealing with stemming problems from a specific situation. In 2018, there will be a significant revolution in this field. Thus, I do hope that cognitive technologies are raising its level and abilities.

Obviously, prescriptive analytics are able to find viable solutions to an issue effectively and quickly as much as possible. There will be more beneficial results if companies and corporations know how to create a mild combination of predictive and prescriptive analytics. Thanks to this, businessmen can make better options to their own companies.

Of course, making use of prescriptive analytics and analyze data at the simultaneous time will help the entrepreneur have faster and more exact choices. Thus, a rosy picture of future business will appear sooner or later.

3. Machine learning will grow faster  

It comes as a no surprise that when we invest more money in developing all aspects of machines, their abilities to learn and do repeated things and algorithms continuously also increase. There is no doubt that machine learning has been rising at an unprecedented level in recent years.

Therefore, its abilities to analyze all large data and complicated documents also become better than ever. What made this appreciable change?

Link:
Big Data Analytics In The Global Market
Understanding The Difference Between Data & Big Data
Big Data Tools Apache Spark And Azure

Many IT specialists claim that an unbelievable growth of hardware and algorithms is the main reason for the above change of machine learning. They even assert that this trend will continue in the future.
Many people will wonder machine learns from where. This is a not difficult question because, in fact, today machines have huge references from both past and present time. They also contain numerous kinds of pictures, videos, texts, and so on.

4. Cyber security has more improvements

Thanks to the progress of Artificial Intelligence in dealing with issues, machines sooner or later will have abilities to make possible predictions. They also can cover all data in such a fast way. Therefore, security will be more OK.

5. IoT will create more influences on big data

It is anticipated that IoT will be an important part of data analytics in 2018. Unquestionably, when more and more people use smartphones, computers, Ipads, and other social devices, there will be a growing demand for collecting big data.

Thus, large companies and corporations should focus more on apply or enhancing their own IoT system as soon as possible.

Conclusion:

In general, big data has been a more familiar term in technology in recent years. When understanding the nature of big data, you will have more potential in controlling companies and gain benefits. Many IT specialists claim that there will be a significant change to big data in the near future. Certainly, it will grow rapidly. I hope that my today article can help you see clearly about this trend.

What do you know about big data analytics? Why is it important in the world market? Keep reading this article.

Introduction

It is undeniable that public clouds will become a promising method for analyzing a huge amount of data in the near future. Applying it in today technology ensures a concrete platform for the value. Admittedly, the work of analyzing big data at present time is so far different from that of previous days. Many specialists do hope that there will be a significant change in this field in coming years. I will help you understand more about this issue in my today article.

A significant change of big data analytics

In a recent report conducted by the Global Industry, the analytics of big data in 2017 tended to increase by more or less 25% compared with the previous year. This figure shocks a lot of people as it happened faster than others predict.


Furthermore, many companies and enterprises are investing their money in analyzing big data. Wikibon has just predicted that there will be an 11% growth of global data analytics in 2027. This number can peak at around 103 billion US dollars at the global level as well. Some specialists strongly believe that the global market may be kept stable thanks to the application of big data analytics.

There is no doubt that a big shift will happen in the upcoming decade. In this field, the public cloud is expanding its influence in the world big data analytics. Nowadays, it appears that 3 main big data analytics providers including Amazon Web Services, Microsoft Azure, and Google Cloud Platform. Their revenue and fame have pervaded to all corners of the world in recent years.

These large providers are changing their own organization and enhance the technology by applying more and more updated applications. They can account for roughly 60% of global big data market in the future.

Additionally, benefits that public cloud brings for users apparently overtake those that private one does. The reasons for this are public clouds always updating their systems and they also solve all problems in a quick way and exactly.

Link:
Big Data Revenues Hit $46 Billion In 2016
Why Is Your Business Data Treated With Such Little Regard?
Big Data Vs The Cloud

At present times, users prefer to use multivendor big data analytics rather than traditional methods in some ecosystems. In fact, they are applying superior features of big data analytics to enhance the effectiveness and avoid commercial risks on the global market.

Databases also are being fixed to be suitable for new applications. Of course, the old form of database in previous times seems to be out of date. And, it can become a menace to our systems. To avoid being affected negatively, big data analytics providers are experimenting new applications.

In some cases, vendors also implement feasible approaches to deal with this weak point. They try to restructure principal databases’ abilities. If successfully, the data analytics will open more new opportunities.

Challenges to big data analytics

Even though a noticeable change in global data analytics in the coming decade, there are also some challenges to this field.

One of the biggest hindrances to global data analytics is complexity. It is undeniable that the nature of data analytics and its applications seem to be still very complicated. This creates a long process for resolving stemming issues. Therefore, vendors have to concentrate on simple applications such as interface, architecture, and other essential elements.

Another weakness of global data analytics is that this filed has a cumbersome process. To some extent, it is still expensive, inconvenient, and ineffective. Thus, vendors should have their own solutions to reduce some difficulties.

Long time for pipelines also is a certain obstacle for the global big data analytics. There is no doubt that this service is important for current trends in the modern global market. However, the way that it analyzes big data wastes a lot of time.

Wikibon highly recommends users to take advantage of public clouds in analyzing big data. This will certainly help you ease some problems of global big data analytics and save up your time as well as money as much as possible.

Conclusion:

By and large, global big data analytics has been changing so fast in recent years. It is foreseen that in the near future, this area will account for a large proportion of the world market. If you know how to take advantage of big data analytics, you can avoid some risks and difficulties of the global market. I do hope that my today article can help you insight into this field.

As I'm sure you are aware, data is gathering and processing information and software in a timely manner.  Big data is information that is enormous in volume and very complex.  This big data cannot be collected, managed, or processed in a timely manner.


There is no clear line between what is considered Big Data but it's usually in multiples of petabytes and enormous projects in exabytes.

By rule of thumb, big data is defined by the 3 V's:

Volume:   an extreme level of data
Variety:   the number of different kinds of data
Velocity: the amount of data that must be processed and analyzed.

Data that is established as big data comes from various places including social media, websites, mobile and desktop apps, scientific data, IoT (other devices in the internet of things, and sensors.

Big data comes from a variety of components that allow a business to put their data to practical use and solve any number of problems.  This can include the IT infrastructure for supporting data, applied analytics, the technology for projects, various skills, and the actual use to make big data clear.

Analytics & Big Data:

Businesses must collect the analytics applied to data, otherwise, it's just tons of data with limited use.  When a company applies analytics to big data, they can see the benefits such as an increase in sales, improved customer service, a greater level of efficiency, and an increase in competitiveness.

This data must be analyzed to gain insight or come to a conclusion about what the data has to offer, such as future activity predictions or the latest trends.

When analyzing data, a business can make better choices in their business decisions, when and where to start a marketing campaign.  This data will also help them understand when they should introduce a new product or service onto the market.

Analytics is also known as a basic business intelligence application or productive analytics that is often used by scientists.  Other advanced kinds of analytics are known as data mining which evaluates large sets of data to understand patterns, trends, and relationships.

Exploratory data analysis not only identifies patterns and relationships but confirms data through statistical techniques to discover if a set of data is true.  Another distinction is the analysis of numerical data that is measured on a numeric sale.  Interval and ratio scales are quantitative, i.e. a country's population.  A qualitative data analysis focuses on nonnumerical data including images, text, etc.

IT Infrastructure Support For Big Data

In order for big data to work properly, a business must have an infrastructure in place to collect and house this data and provide access to it.  The information must be secure while in storage and in transit.  At a higher level, the storage systems and servers must be designed for big data, data management, and integration software.  Also, your business intelligence and data analytics software and applications must be in place.

The majority of infrastructures will be on-premises for companies to continue leveraging their data center investments.  More and more, businesses rely on cloud computing services to take care of their big data needs.

In order to collect data, there must be sources in place to carry it out.  This includes web applications, social media channels, mobile apps, and email archives. As IoT has become more established, businesses should have sensors on all their devices, vehicles, and products to gather their data.  Also, new applications that generate user data will become more critical.  IoT big data analytics provide their own specialized tools and techniques.

In order to store all this incoming data, a business must have good storage in place such as traditional data warehouses, data lakes, and cloud-based storage.  Security infrastructure tools might include data encryption, user authentication, other access controls, monitoring systems, firewalls, enterprise mobility management, and other products to protect their systems and data.

Technologies For Big Data:

There are various technologies that focus on big data and your infrastructure must support:

Hadoop Ecosystem:

Apache's Hadoop project develops open-source software for scalable, distributed computing.  Hadoop is a technology that is most closely associated with big data.  Hadoop's library is a foundation that provides the distribution for the large sets of data across extensive groups of computers that are using simple programming standards.  It's developed to scale from a single server to thousands with each providing local computation and storage.

The Hadoop project includes the following:

Hadoop Common: which are common utilities that support other Hadoop modules.

Their File System for distribution: measures the access to the data.

Hadoop YARN: the core for scheduling and group support management.

Hadoop MapReduce: which is a YARN-based system for parallel processing of large sets of data.

Apache Spark:

This is an open source cluster-computing foundation that works as an engine for processing big data in Hadoop.  It is one of the best big data distributing processing structures that can be sent in many ways.  Spark offers native bindings for Java, Python, Scala and specifically for Anaconda Python distro, and R programming languages for big data.  It also supports SQL, streaming data, machine learning, and graph processing.

Data Lakes:

Data lakes are storage repositories that house very large volumes of raw data in their native format until it's needed. Digital transformation actions increase the growth of data lakes and IoT Data lakes.  They are developed to make it easier for you to access enormous amounts of data when you need it.

NoSQL Databases: 

Traditional SQL databases are designed for dependable transactions and ad hoc questions but are limited, making them less satisfactory for some applications.  NoSQL databases address these shortcomings, storing and managing data in ways that will allow for increased operational speed and greater flexibility.  Many of these databases were created by companies looking for improved ways to store content and process data for huge websites.  NoSQL database can be extended horizontally across thousands of servers.

IMDB:

An IMDB (in-memory database) is a management system that basically depends on main memory instead of disk for data storage.  These databases are faster than disk-optimized databases which is important to keep in mind for big data analytics, the creation of data warehouses, and data marts.

Big Data Skills:

There are certain skills required for big data and big data analytics.  These skills are found either within an organization or through outsourcing.  A lot of these skills are related to important big data technology components including Hadoop, Spark, NoSQL, IMDB, and analytics software.

Other skills are acquired through precise training including data science, data mining, statistical, and quantitative analysis, data visualization, general programming, data structure, and algorithms.  There is also a growing need for people with overall management skills to oversee big data projects until they are completed.

Because big data analytics have become high in demand, the shortage of people with the right skills has become a challenge for most businesses.

Big Data Uses:

Here are some examples how big data and analytics are being used:

Companies can analyze customer data to improve their customers' experiences, improve their conversion rates, and increase retention through Customer Analytics.

Companies can increase their operational performance and improve the use of corporate assets.  Operation Analytics can help businesses find solutions for running more efficiently and improving their performance.

Data Analysis helps businesses discover suspicious activities and patterns that suggest there are fraudulent behaviors taking place and reduce the risks.

Data analytics is also utilized to optimize the price a company is charging for their products and services to increase their revenues.

With the increased improvement in technology regarding speed and scaling, it has not decreased the challenges associated with schema transformation, integration of data, or the complexity to take informed actions.


The influence of cloud computing, distributed computing, and mobile technologies, have all contributed to today's diversified IT environment for big data.  Traditional approaches to data management and data lakes cannot keep up with the requirements to bring together data, no matter where it's located, across the enterprise platform for one singular control over multiple sources.

The Enterprise Knowledge Graph is a platform that combines big data and graph technology, which deals with these limitations and greatly improves big data management.  It offers singular access to data across this platform in any form.  It adjusts data into a standard format and helps assist with actions needed to continually leverage them for many organizational uses.

Enterprise Knowledge Graphs:

Data lakes allow universal access to data in their original formats but do not have the needed metadata and semantic consistency for sustainability for any length of time.  Enterprise Knowledge Graphs have metadata and semantic benefits of MDM hubs and will connect all data together in line with semantic standards.

While businesses have different names for labeling their products, the combination of enterprise spanning connections and data representation brings everything together for immediate recognition by the user.

For example, health care providers can connect to enormous volumes of data in their industry by creating lists of events including patient outcomes, operations, billing, diagnostics, and describing them in a uniform terminology across the data range.

It doesn't matter where the data is located, whether it's in the cloud or a cache, the user can link them in the same format that meets their business purposes.  The standard concepts or categories are adaptable to incorporate new events and into unified terminology to align all data to the knowledge graph's design, no matter what their origination or distinctions might be.

Active Automation:

With data coming from any number of sources, the automatic generation of code for analytics or for transformation is extremely valuable.  One of the biggest advantages of the enterprise knowledge graph, it reduces the challenges for not only accessing data but applying it to actions.

As an example, a healthcare provider attempting to anticipate the occurrence of respiratory failure for patients in various locations, they should use the knowledge graph application to monitor the blood pressure of all hospital occupants.  The graph will allow the organization to create a theoretical description of the blood pressure data, related to respiratory failure, then automatically amass the information into code that will gather the data.

The value of this approach, the user simply does a search for the data they need, no matter where it is located.  The automation abilities of the enterprise knowledge graph will create an action to collect data that matches the search.  The user does not need to know the source system or particulars of its design to get the data.  Access is much easier because all data engineering of cleaning and transforming the data is done prior to the search.

The relevant data for the search can come from multiple systems but will be accessible from a single place.  The user is not responsible for accessing those systems, instead, the search mechanism is able to pull the appropriate data from the various systems and is accessible from the central knowledge graph.

Enterprise Knowledge Graph Unification:

It brings together the means of accessing, automating, representing, and moving the data from many different sources and architectural intricacies.  Along with how users retrieve the data through automation, the knowledge graph will standardize the data in accordance with the relevancy of business terms.  This results in making the data standardized from any number of data sources or types.

The constant release of big data software along with the volumes of data under management, the market is ripe for a massive security breach!

Surveys taken last year discovered very few businesses have taken their infrastructure security seriously.  When asked about Hadoop, only 2% of people considered them a leading concern.  CIOs can pray until the cows come home but if they keep turning their backs from possible security threats, it'll all be in vain.


Unfortunately, many businesses are quite unaware of the threats to security with big data platforms such as Hadoop.  Experts have stated that they cannot believe that people think this platform is secure.  At every level, vulnerabilities are standing out and the level of data itself, there should be serious concerns.

Many leading experts believe that after enabling the security settings in Hadoop, business owners haven't a clue if their big data is secure or not.  The experts are not 100% satisfied and believe a great deal more work needs to take place on Hadoop to improve their security.

Others have stated that while Hadoop is aware of the need to protect data confidentiality in Hadoop clusters, there is continuous limited attention to data integrity.  Security on Hadoop often does not become implemented, claiming complexities or are just being ignored.

Hadoop is the leader of big data infrastructure with a great deal of time and attention being given to them.  If they cannot come up with sufficient security, despite petabytes of sensitive data flowing into their clusters, there is a very serious situation of security problems across the platform.

The bottom line, the longer a software is on the market, the more likely it's vulnerabilities will be discovered.  This is especially true with open source software that can dig into source code before and even after these vulnerabilities are detected.

That said, big data infrastructures will not sit still long enough for vulnerabilities to be discovered.  You cannot base the assessments of Hadoop that are over a year old as existing portions are maturing and new ones are being put into place.

It is believed by some that larger security issues will surface as Hadoop goes mainstream.  It's not just Hadoop either, it's Spark, Kafka, and many other fast moving data infrastructures.  With surrounding controversies, many of Hadoop's vendors are distancing themselves.

Businesses need to come to the same reality that everyone else knows.  Threats of breaching security on the internet have been taking place for some time now.  Big data must be safe and secure, it's the lifeline of a business.  All businesses, organizations, and corporations must start taking this very seriously and securing the business success and ensure they big data is safe!

In this day and age, the generation of line-of-business computer systems will generate over terabytes of data every single year by tracking sales and production through CRM and ERP.  This enormous flow of data will only continue to grow as you add the sensors of the industrial IoT along with the data that is needed to deliver.


Big data is usually unstructured and spread across many servers and databases.  Having data and knowing how to use it are two different things.  This is where big data tools come into play such as Apache Spark that distributes analytical tools across clusters of computers.  Creating techniques developed for the MapReduce algorithms using tools like Hadoop, big data analysis tools are going further to support more database-like behaviors, working with in-memory, data at scale, using loops to speed up searches, and offering a foundation for machine learning systems.

Although Apache Spark is very fast, Databricks is even faster.  Databricks is a cloud-optimized version of Spark and founded by the Spark team.  It takes advantage of the public cloud services to scale quickly and cloud storage to host its data.  It offers tools that make it easier to search your data using the notebook and is accessible and interesting to the public with tools such as Jupyter Notebooks.

Microsoft's support for Azure Databricks signals a new direction of its cloud services, bringing Databricks in as a partner vs an acquisition.  From the Azure Portal, you can perform a one-click setup, making Azure Databricks even easier to use.  You are able to host multiple analytical clusters, use autoscaling to decrease the resources used.  You can clone and edit clusters, you can assign them specific jobs or running different analyses on the same data.

Azure Databricks Configuration:

Microsoft's new service is a managed Databricks virtual appliance, developed by using containers that run on Azure Container Services.  You select the number of VMs for each cluster then the service will automatically handle the load once it's configured, running and loading new VMs for scaling.

Databricks' tools interact directly with Azure Resource Manager which adds a security group, a dedicated storage account, and virtual network to your Azure subscription.  You are allowed to use any of Azure VMs for your Databricks.  Use the newest GPU-based VMs if you are going to use it for your machine learning systems. If a particular VM is not right for your situation, you can easily change it out for another one.  All you do is clone a cluster and change the VM definitions.

Bringing Engineering To Data Science In Spark:

Spark has its own query language that's based on SQL and works with Spark DataFrames to take care of structured and unstructured data.  DataFrames are like relational tables built on top of collections of distributed data in different places. Relational databases or tables are a set of data values using a model of vertical columns and horizontal rows. Using identifiable named columns, you can build and manipulate DataFrames with languages such as R and Python.  Both data scientists and developers can take full advantage of them.

DataFrames are a domain-specific language for your data.  It's a language that extends the data analysis features of your chosen platform.  Using libraries with DataFrames you can build complex queries that take data from multiple sources, working across columns.

Azure Databricks is data-parallel and queries are evaluated only when called to deliver actions and the results are delivered quickly.  You can add Azure Databricks DataFrames and queries to existing data easily because Spark supports common data sources, either through extensions or native abilities.  This will reduce the need to migrate data to take advantage of its capabilities.

Azure Databricks is a very useful tool for developers and data scientists to develop and explore new models turning data science into data engineering.  You can create scratchpad view of the data with code and get a single view using Databricks Notebooks.

The notebooks are shared resources for anyone to use and explore their data and experiment with new queries.  Once a query is tested and turned into a regular job, its output is presented as an element Power BI dashboard.  This makes Azure Databricks part of an end-to-end data architecture that will allow for more complex reporting than SQL or NoSQL.

A New Platform For Azure Services – Microsoft Plus Databricks:

Microsoft has not announced the details regarding its pricing for Azure Databricks.  They have stated that it will improve your performance and reduce the costs as much as 99% compared to operating your own Spark installation on Azure's infrastructure service.  If Microsoft's claims are confirmed, you could experience significant savings.

Azure's Databricks directly connects to their storage services including Azure Data Lake with optimization for queries and caching.  You will also have the option to use it with Cosmos DB and take advantage of global data sources and various NoSQL data models, including MongoDB and Cassandra, and Cosmos DB's graph APIs.  Along with their data streaming tools, you will have the option for an assimilated real-time IoT analytics.

It makes a great deal of sense that Microsoft wants to partner with Databricks as Databricks has the experience and Microsoft has the platform. If the service turns into a success, this could set a new precedence how Azure evolves in the future.

Many businesses will try to find answers to data issues and end out failing.  This could lead to approaches that will harm the market on getting the right solutions for data.  To create greater success, we will discuss some of the leading problems and how to overcome many of these challenges.


Lacking a good data preparation strategy will prevent the success of big data. The necessary actions for data preparation include acquiring, preparing, curating, and managing data assets within a business.  Healthy data comes from insight delivered by advanced analytic operations.  Data that is impaired can lead to questionable conclusions and if not backed with accurate intelligence can lead to a great deal of confusion and added turmoil.  Therefore, making bad decisions will lead to bad information, no matter how confident you are in your findings.

Unfortunately, many businesses that are toying with big data, and vendors selling the solutions, hardly ever consider the ramifications of data preparation.  Creating the hardware infrastructure and software for a big data lake can be very complicated and very expensive.  Many believe this is the most challenging part of the big data issue.  Once they have the infrastructure in place, they will quickly discover that is only the beginning.  Collecting and managing data can be quite expensive, especially if the project has been built on not understanding what data will be required.  So, where do you go from there?

First Know The Content Of Your Businesses' Decision-Making:

This will help define the data sets you are going to use to support the decision. This includes how the data will be used and the analytical process that will define insight generation.  Many people believe that by simply cleansing and curating effectively, all analytic processes can be supported and that's just not the case.  If business leaders define the endgame first, data preparation will be much easier.

Choose Data Sources To Support The Decisions:

You probably will not know every single possible data source in advance, but if you can identify the primary data sources you will need is a step in the right direction.  It will define the kinds of data available and the kinds of data cleansing that will have to be done.

Select The Right Vendor For Data Cleansing Technology:

Not only do you need technology that will accommodate your identified data types but will also provide a platform that will nourish your existing cross-organization analytic tools.  There are many levels within an organization using tools for informed decisions.  The data preparation tool offers a platform that is accessible by everyone to allow for access to curated and trusted data.  Therefore, starting off from the same basic data set is the only way decisions will be consistent throughout the company.

Examine & Take In Additional Data Sets:

There is no way to know every single data set that might be needed.  Data sets are continually being discovered that might be used.  Discovering new data sources is going to be an on-going process and an extremely important part of your decision-making.

Discover New Analytic Tools To Produce Insight:

There are many excellent tools on the market from Simple statistical tools to very advanced machine learning applications.  They all offer different insights and different levels of data cleansing.  For instance, machine learning may be able to handle data in a native mode, but a statistical tool will need very clean data with every field adapted.

Expand Your Data Preparation For New Data:

Most data sets are constantly evolving and as new data is found and becomes available, data preparation must take place to ensure it will be readily available.

The first step is to realize that data preparation is important for big data value. A business can significantly lower the costs of big data while increasing actionable insights that drive good decision-making.  Because the data preparation market is on the rise, data preparation strategies are always available.

One of the leading issues that's making things even worse rests in the technology industry which instead of solving problems is focusing only on recent trends.  Not that long ago, everything was about clients, distributing data, and web services.  Now, it's about machine learning that even though is important, there are other issues that must be addressed.

Enter Big Data And Lose Indexing & Search:

Unfortunately, indexing and search were thrown to the side of the road which has caused even greater problems.  Many believe that the web would be a great deal smaller if search portals and Yahoo had reined in the 90s but then came the dot come and guess who rose to the top, Google.  It was search that actually created big data and today's machine learning trend. Companies such as Google and Facebook needed to find out how to deal with their indexing and huge data distribution on the scale of the internet.  They needed to find and organize data after discovering they needed services, content, and ideas from large groups of people, especially from the online community.


Once Amazon invested in search technology, they blew the doors off the retail market.  Let's face it, if there is something you need, you're going to visit Amazon.com.  Through their technology, they will often suggest what you are looking for before you ever start a search.  Even though this technology is starting to slip away, many businesses still utilize the built-in search in their commerce and can't figure out why their conversions and engagement are at an all time low.

Some businesses will move some of your data to SaaS, other data to PaaS, more data to IaaS, and across many vendor's cloud platforms while keeping other data behind a firewall, it's no wonder you can't find anything.

Redefining Integration:

For this world of cloud computing and distribution, redefining integration is a must.  There was a time that data integration meant taking all the data and putting it in one place such as databases, data warehouses, etc.  Unfortunately, what happened, data was being moved farther away from indexed technology.  Now, integration means going to the source of the data and getting rid of duplicated information from computer data uploaded from many different places and getting the results.

To integrate properly, you need a single search answer that will reach out to your on-premise data and your cloud data as well.  What you do not is a search tool that can only search one source of data, serving only one source at a time and cannot be used behind a firewall.

We need search to be able to find data and analyze it all together, no matter where it is located.  You need tools to get to where the right data resides, not just dumping everything in one place.

As of late, everyone seems to be talking about cloud-based architecture and the need for various platforms including cloud management.  What no one seems to be talking about is the huge growth of digital data stored across the world.


Big data and digital data have impacted cloud services around the world.  Cloud is, and will continue to be, an important aspect of IT landscapes and is considered the cure-all for every IT issue out there.  There are two major elements of all IT, the data and the logic working with the data.  Everybody working with big data knows that in order to use huge levels of data, you must bring the processing of data to data, not the other way around.  Processing from a distance can create bottlenecking which will cause performance to decrease practically to nothing as well as the functioning of that logic.

If you move your application server to the cloud but keep your database service at your place of business,  when you perform an application that is latency-sensitive, the application server and database server will not work. This is already the situation with smaller levels of data and why many businesses are trying to adapt software to become less latency-sensitive by moving to the cloud.  That said, you need to bring processing and data close to each other for large amounts of data to work.  To deal with enormous amounts of data, you really need Hadoop and other architectures to deal with the problem of processing massive amounts of data.

It is expected that in a few years the world will store approximately 50ZB.  While the Internet's capacity for moving data is on the rise, it's doing so at a much slower pace.  Adding to that, the total internet bandwidth will reach around 2.5ZB on a yearly basis.

Should this information turn out to be true, there will not be enough internet bandwidth available to move even a fraction of the data anywhere.  Also, 80% of all current bandwidth is currently used for streaming videos.  Even if you have addressed your latency issues for massive data, there will still be an issue with your bandwidth.

If you are processing data in the same location that holds the data, there shouldn't be an issue.  As the number of data increases, everyone is looking for cloud strategies and going to extremes by putting everything in the cloud.

While more and more people are getting into cloud-based platforms for the distribution of data, the amount of data you use is getting larger and larger.  You will have to consolidate data and the processing in a single physical location.

So What Does The Future Hold?

Many people are running around to minimize the need to move data.  In the IoT world, there are many discussions regarding handling data locally where IoT devices and sensors are.  Again, all that means is processing must be local as well.  You can assume that you will not have the same level of computing power in a set of sensors than what you could do in big analytic setups.  You can minimize data traffic but at the expense of how much you can compute.

Another solution is in the rise of colocation providers.  They are providing large data centers with optimized internal traffic abilities where cloud providers and large cloud users are working together.  What this means, you may be in the cloud but physically you are in the same space as your cloud provider. You want to run your logic on a data center where you will also have your own private data lake.  All the data is local to the processing and data aggregation.  It is possible that cloud providers will extend into your data centers but then again, colocation seems to be another possible solution for bandwidths and other issues regarding the growth of data.

If all seems doom and gloom, it's not.  The lack of stability of data is actually very low.  That said, just thinking you can distribute your work to a variety of different cloud providers can still be risky.  If the data you are sending increases in volume, which it probably will if everyone combines their own data streams from Facebook, Twitter, etc and creates new streams.

It's important you create a strategy regarding the location of your data and processing, what you can and cannot isolate from other data.