Trending March 2024 # 15 Hadoop Vendors Leading The Big Data Market # Suggested April 2024 # Top 12 Popular

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Also see: Hadoop and Big Data

Hadoop and Big Data analytics are popular topics, perhaps only overshadowed by security talk. Apache’s Hadoop and its other 15 related Big Data projects are enterprise-class and enterprise-ready. Yes, they’re open source and yes, they’re free, but that doesn’t mean that they’re not worthy of your attention. For businesses that want commercial support, here are 15 companies ready to serve you and your Hadoop needs.

This list of Hadoop/Big Data vendors in alphabetical order.

1. Amazon Elastic MapReduce

Key differentiators: Amazon’s Elastic Cloud, S3, and DynamoDB integration plus an expensive and flexible pay-as-you-use plan. An added bonus is that EMR plays nice with Apache Spark and the Presto distributed SQL query engine.

Amazon Elastic MapReduce (Amazon EMR) is a part of Amazon Web Services (AWS) and is a web service that allows you to manage your big data sets. Amazon EMR (EMR) promises to securely and reliably handle your big data, log analysis, web indexing, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Amazon’s pricing model is simple. Using the simple charge per hour rates, you can accurately predict your monthly fees, which makes it easy to budget and plan next year’s budget. Since Amazon’s cloud computing prices keep going in a southerly direction, your budget shrinks while your revenues pile up. Per hour prices range from $0.011 to $0.27 ($94/year to $2367/year), depending on the size of the instance you select and on the Hadoop distribution.

The downside of Amazon’s services is that they’re somewhat difficult to use. They’re easier to use now than they were a few years ago, but to use AWS and associated services, you will have to possess intermediate level technical skills as a system administrator to understand all of the options and how to handle key pairs and permissions.

2. Attunity Replicate

Key differentiators: Attunity automates data transfer into Hadoop from any source and it also automates data transfers out of Hadoop, including both structured and unstructured data. Attunity has forged strategic partnerships with Cloudera and Hortonworks (Both included in this article).

It’s hard to pinpoint exactly what Attunity Replicate does for big data until you see the process in action. Replicate takes data from one platform and translates it into another. For example, if you have multiple data sources and want to combine them all into a single data set, then you’d have to struggle with grabbing or dumping the data from all your source platforms and transforming that data into your desired target platform. You might have sources from Oracle, MySQL, IBM DB2, and SQL Server and your target is MySQL.

Attunity support a wide range of sources and targets, but check closely before you purchase because not all databases are source and target capable.

3. Cloudera CDH

Key differentiators: CDH is a distribution of Apache Hadoop and related products. It is Apache-licensed, open source, and is the only Hadoop solution that offers unified batch processing, interactive SQL, interactive search, and role-based access controls.

Cloudera claims that enterprises have downloaded CDH more than all other distributions combined. CDH offers the standard Hadoop features but adds its own user interface (Hue), enterprise-level security and integration more than 300 vendor products and services.

Cloudera offers multiple choices for starting up with Hadoop that include an Express version, an Enterprise version, and a Director (cloud) version, four Cloudera Live options, and a Cloudera demo. Additionally, you can download the Cloudera QuickStart VM for those of you who want to test in your own environment.

4. Datameer Professional

Key differentiators: The first big data analytics platform for Hadoop-as-a-Service designed for department-specific requirements.

Datameer Professional allows you to ingest, analyze, and visualize terabytes of structured and unstructured data from more than 60 different sources including social media, mobile data, web, machine data, marketing information, CRM data, demographics, and databases to name a few. Datameer also offers you 270 pre-built analytic functions to combine and analyze your unstructured and structured data after ingest.

5. DataStax Enterprise Analytics

Key differentiators: DataStax uses Apache Cassandra and Apache Hadoop as the database engine and the analytics platform that is highly scalable, fast, and capable of real-time and streaming analytics.

DataStax delivers powerful integrated analytics to 20 of the Fortune 100 companies and well-known companies such as eBay and Netflix. DataStax is built on open source software technology for its primary services: Apache Hadoop (analytics0, Apache Cassandra (NoSQL distributed database), and Apache Solr (enterprise search).

Dell’s Statistica Big Data Analytics is an integrated, configurable, cloud-enabled software platform that you can easily deploy in minutes. You can harvest sentiments from social media and the web and combine that data to better understand market traction and trends. Dell leverages Hadoop, Lucene/Solr search, and Mahout machine learning to bring you a highly scalable analytic solution running on Dell PowerEdge servers.

7. FICO Big Data Analyzer

Key differentiators: The FICO Decision Management Suite includes the FICO Big Data Analyzer, which provides an easy way for companies to use big data analytics for decision management solutions.

FICO’s Big Data Analyzer provides purpose-built analytics for business users, analysts, and data scientists from any type of data on Hadoop. Part of FICO’s Big Data Analyzer appeal is that it masks Hadoop’s complexity, allowing any user to gain more business value from any data.

FICO provides an end-to-end analytic modeling lifecycle solution for extracting and exploring data, creating predictive models, discovering business insights, and using this data to create actionable decisions.

8. Hadapt Adaptive Analytical Platform

Key differentiators: Hadapt was recently purchased by Teradata and has a patent-pending technology that features a hybrid architecture that leverages the latest relational database research to the Hadoop platform.

Hadapt 2.0 delivers interactive applications on Hadoop through Hadapt Interactive Query, the Hadapt Development Kit for custom analytics, and integration with Tableau software. Hadapt’s hybrid storage engine features two different approaches to storage for structured and unstructured data. Structured data uses a high-performance relational engine and unstructured data uses the Hadoop Distributed File System (HDFS). Hadapt has a lot of trademarked products as part of its Adaptive Analytical Platform plus its pending patent for its complete technology solution.

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Are Big Data Vendors Forgetting History?

With any new hot trend comes a truckload of missteps, bad ideas and outright failures. I should probably create a template for this sort of article, one in which I could pull out a term like “cloud” or “BYOD” and simply plug in “social media” or “Big Data.”

When the trend in question either falls by the wayside or passes into the mainstream, it seems like we all forget the lessons faster than PR firms create new buzzwords.

Of course, vendors within trendy news spaces also tend to think they’re in uncharted waters. But in fact there’s actually plenty of history available to learn from. Cloud concepts have been around at least since the 1960s (check out Douglas Parkhill’s 1966 book, The Challenge of the Computer Utility, if you don’t believe me), but plenty of cloud startups ignored history in favor of buzz.

And it’s not like gaining insights from piles of data is some new thing that was previously as rare as detecting neutrinos from deep space.

Here are five history lessons we should have already learned, but seem to be doomed to keep repeating:

It wasn’t that long ago that every time a cloud project or company failed, some tech prognosticator would sift through the tea leaves and claim that the cloud concept itself was dead.

The same thing is happening with Big Data. According to a recent survey, 55 percent of Big Data projects are never even completed. It’s hard to achieve success if you don’t even finish what you started, yet many mistakenly believe that this means Big Data is bunk.

Not true. Plenty of companies are reaping the rewards of Big Data, analyzing piles of data to improve everything from marketing and sales to fraud detection.

People mean many different things when they use terms such as “cloud” and “Big Data.” Are you talking about virtualized infrastructures when you say cloud? Private clouds? AWS? Similarly, Big Data can refer to existing pools of data, data analytics, machine learning, and on and on.

The Big Mistake with the term Big Data is that many use the term to mask vague objectives, fuzzy strategies and ill-defined goals.

Often when people use these terms loosely it’s because they not only don’t really know what the heck the terms mean in general, but they also don’t know what they mean to their particular business problems. As a result, vendors are asked for proposals that are a poor fit for an organization’s cloud or Big Data challenges.

If your CEO or CIO orders you to start investigating Big Data, your first question needs to be the most basic one: Why, specifically?

If you can’t answer that question concisely, you’re in trouble.

If you’re the person tasked with building out a Big Data architecture, then it’s fine to focus on details that won’t matter to anyone who isn’t a data scientist.

If you’re a business user or non-data scientist, it’s best to just ignore all this noise. It’ll sort itself out soon enough. I’ve seen this phenomena repeat with everything from CDNs to storage to cloud computing and now Big Data. Engineers and product developers often fall prey to “if we build it, they will come” syndrome, ignoring the real-world pain points of potential customers in favor of hyping their technical chops.

When they fail to find real-world customers for the resulting products, they then set their sights on technical minutiae, since it couldn’t possibly be a flawed go-to-market strategy that was the problem in the first place.

Take the recent news that Facebook is making its query analysis software, Presto, open source. Is this a win for Hadoop or for SQL? Does it mark the end of Hive?

Who cares?

Okay, if you’re reading this, you’re probably an early adopter or you’ve already placed some Big Data bets, so it matters to you. But for the rest of the world, it’s not even on their radar – nor should it be.

Big Data Market 2023: Growing And Moving To The Cloud

The big data market is strong and thriving — although it isn’t always called “big data” these days.

The term “big data” first became part of the tech lexicon in the late 1990s, when people like John Mashey at SGI began using the phrase to describe the enormous and growing stores of enterprise data that were difficult to store and analyze using the technology available at the time.

In 2001, analyst Doug Laney suggested a definition of big data that included three Vs: volume, velocity and variety. Over the next few years, Laney’s definition became something of an industry standard, and some people added a fourth V — variability — to the definition.

In 2005, big data technology took a dramatic step forward when Yahoo debuted the Hadoop open source distributed data store. The project became the lynchpin for an entire ecosystem of commercial and open source data storage and analytics solutions.

In 2014, IDC and EMC released their most recent digital universe study, which revealed that the amount of data stored by the world’s digital systems is growing by 40 percent per year. The companies predicted that by 2023, the digital universe would include 44 zettabytes of information. That’s nearly as many bits as there are stars in the universe, and it’s enough information to fill a stack of 2014-era tablets stretching to the moon 6.6 times.

Today, big data certainly hasn’t become any smaller, but the size of growing data stores no longer gets as much attention as it once did. Instead, most organizations are focused on analytics, data science and machine learning. They have accepted that managing big data is simply a part of doing business; if they want to compete and succeed, they need to find ways to turn those big data stores into valuable insights.

Enterprise spending on big data technologies continues to climb as it has for the past decade. According to IDC, worldwide revenues for big data and business analytics are likely to grow from $150.8 billion in 2023 to $210 billion in 2023. That’s a compound annual growth rate of 11.9 percent.

“After years of traversing the adoption S-curve, big data and business analytics solutions have finally hit mainstream,” said Dan Vesset, an IDC group vice president. “BDA as an enabler of decision support and decision automation is now firmly on the radar of top executives. This category of solutions is also one of the key pillars of enabling digital transformation efforts across industries and business processes globally.”

And organizations are reporting that their big data initiatives are having a positive impact on their bottom line. In the NewVantage Partners Big Data Executive Survey, 80.7 percent of respondents said that their big data investments had been successful, and 48.4 percent said that they had realized measurable benefits as a result of their big data initiatives.

Those sorts of results are likely to encourage enterprises to continue investing in big data, but the types of big data solutions they are adopting are shifting. According to Forrester Research, “The shift to the cloud for big data is on. In fact, global spending on big data solutions via cloud subscriptions will grow almost 7.5 times faster than on-premise subscriptions.” The firm added, “Furthermore, public cloud was the number one technology priority for big data according to our 2024 and 2023 surveys of data analytics professionals.”

As the big data market has matured, vendors have developed a wide variety of different big data technologies to meet enterprises’ needs. This is a very broad market, but most big data solutions fall into one of the following categories:

Business intelligence (BI): Business intelligence solutions provide analytics and reporting capabilities on business data typically stored in a data warehouse. According to Gartner, the BI and analytics market is forecast to increase from $18.3 billion in 2023 to $22.8 billion in 2023. However, this is slower growth than in the past.

Data mining: Data mining is a broad category that encompasses a wide variety of techniques for finding patterns in big data. While many big data solutions still offer data mining capabilities, the term has fallen somewhat out of favor as vendors instead are using terms like “predictive analytics” and “machine learning” to describe their solutions.

Data integration: One of the big challenges with big data analytics is gathering all the relevant data from disparate sources and converting it into a format that allows for it to be analyzed easily. This had led to a whole crop of data integration solutions, which are sometimes also called ETL (short for “extract, transform, load”) solutions. According to Markets and Markets, data integration revenues could be worth $12.4 billion by 2023.

Data management: This category of solutions includes tools that help organizations integrate, clean, store, secure and assure the quality of their digital data. Markets and Markets predicted that this category of big data tools could generate $105.2 billion in revenue by 2023.

Open source technologies: Many of the most widely used big data technologies are available under open source licenses. In particular, technologies like Hadoop and Spark, which are managed by the Apache Foundation, have become very popular. Many vendors offer commercially supported versions of these open source big data technologies.

Data lakes: A data lake is a repository that ingests data from a wide variety of sources and stores it in its native format. This is a little different than a data warehouse, which stores data that has been cleaned and formatted for analytics. Data lakes are popular with organizations that want to perform analytics on both structured and unstructured data.

NoSQL databases: Unlike relational database management systems (RDBMSes), NoSQL databases don’t store information in traditional tables with rows and columns. Instead, they use other models, such as columns, documents or graphs for tracking data. Many enterprises use NoSQL databases for storing unstructured data for analytics.

Predictive analytics: Currently one of the most popular forms of big data analytics, predictive analytics looks at historical trends in order to offer a good estimate about what might happen in the future. Many modern predictive analytics solutions incorporate machine learning capabilities so that their forecasts become more accurate over time. A Zion Market Research report said spending on predictive analytics could climb from $3.49 billion in 2024 to $10.95 billion by 2023.

Prescriptive analytics: Prescriptive analytics goes a step farther than predictive analytics. In addition to telling organizations what is likely to happen in the future, these solutions also offer suggested courses of action in order to achieve desired results. Experts say few (if any) big data analytics solutions currently on the market have true prescriptive capabilities, but this is an area of intense research for vendors.

In-memory databases: In-memory technology makes big data analytics much, much faster. In any computer system, accessing data in memory (also sometimes called RAM) is much faster than accessing stored data on a hard drive or solid state drive. In-memory databases allow users to store vast quantities of data in memory, yielding dramatic speed boosts.

Artificial intelligence and machine learning: Many next-generation big data analytics tools incorporate machine learning, which is a subcategory of artificial intelligence (AI). Machine learning uses algorithms to help systems get better at tasks over time without explicit programming. This is one of the fastest-growing areas of the big data market.

Data science platforms: Many vendors have begun labelling their big data analytics solutions as “data science platforms.” Products in this category typically incorporate many different capabilities in a unified platform. Nearly all the products in this category have some analytics and machine learning features, and many also have data integration or data management features as well.

Given that the market includes so many different types of big data solutions, it should be no surprise that an extremely long list of companies offer big data products. The list below includes some of the best-known big data companies, but there are many others.

The Data Transfer Project’s Big

The Data Transfer Project addresses one pain point we all experience on our phones: moving our stuff around. While it’s certainly gotten easier over the years to share individual photos, songs, and files from one app to another, shifting large chunks of data or entire libraries and histories between services is often an exercise in futility, even with hundreds of gigabytes of cloud storage at our disposal.

But while the four founding members are certainly big enough to get the Data Transfer Project off the ground, it’s missing the support of the biggest player of all: Apple. And without the iPhone maker on board, it’s going to be a tougher sell than it should be.

Share and share alike

On the surface, the Data Transfer Project has a very simple goal that all providers and developers should support: portability, privacy, and interoperability. In the announcement, Google, Facebook, Twitter, and Microsoft served up this clear mission statement: Making it easier for individuals to choose among services facilitates competition, empowers individuals to try new services, and enables them to choose the offering that best suits their needs.


iPhone users should get the same Data Transfer experience as Android users.

The timing of the announcement isn’t accidental. While the group was officially formed last year, 2023 has been a troubling year for data and privacy, particularly with regard to three of the companies here. Facebook, Twitter, and Google have each taken very public lumps over the handling of user data. Most recently, the European Union implemented a stringent set of laws governing privacy rights and adding layers of transparency for users.

If nothing else, the Data Transfer Project is a public commitment to free users’ data from any one service and respect the right to move it between apps. In simple terms, your Facebook photos are just photos, so when the next big social thing comes along, you won’t need to rebuild your entire digital profile.

The benefit applies to non-social situations as well. As the group explains in its white paper: “A user doesn’t agree with the privacy policy of their music service. They want to stop using it immediately, but don’t want to lose the playlists they have created. Using this open-source software, they could use the export functionality of the original provider to save a copy of their playlists to the cloud. This enables them to import the playlists to a new provider, or multiple providers, once they decide on a new service.”

Opening the walled garden

The aim of the Data Transfer Project is something that simultaneously agrees and disagrees with Apple’s core philosophies. On the one hand, Apple promotes ease-of-use and interoperability among all of its products. The company is constantly working to break down barriers so our data can jump seamlessly from one device and app to the next.


If Apple is truly serious about privacy, it needs to sign on board with the Data Transfer Project.

But if Apple is truly committed to privacy—and not just Apple device privacy—it needs to take a stand here. While the lock-in inherent to Apple’s ecosystem is often derided, the fact of the matter is, a walled garden is a nice place to play. The devices all work well together, and they’re encrypted and secure and receive the latest security patches and updates. That’s why many people would be plenty happy to stay, even if Apple made it easier to leave by supporting the Data Transfer Project.

As it stands, the Data Transfer Project is an ambitious project that won’t see its full potential without the support of Apple. If the ease-of-use and privacy gains it delivers stops at the iPhone, the rest of the industry will be reluctant to join forces, even with the might of Google, Microsoft, and Facebook behind it. And Apple doesn’t need to tear down its walled garden to support it. It merely needs to put a key under the doormat.

What Is Big Data? Why Big Data Analytics Is Important?

What is Big Data? Why Big Data Analytics Is Important? Data is Indispensable. What is Big Data?

Is it a product?

Is it a set of tools?

Is it a data set that is used by big businesses only?

How big businesses deal with big data repositories?

What is the size of this data?

What is big data analytics?

What is the difference between big data and Hadoop?

These and several other questions come to mind when we look for the answer to what is big data? Ok, the last question might not be what you ask, but others are a possibility.

Hence, here we will define what is it, what is its purpose or value and why we use this large volume of data.

Big Data refers to a massive volume of both structured and unstructured data that overpowers businesses on a day to day basis. But it’s not the size of data that matters, what matters is how it is used and processed. It can be analyzed using big data analytics to make better strategic decisions for businesses to move.

According to Gartner:

Importance of Big Data

The best way to understand a thing is to know its history.

Data has been around for years; but the concept gained momentum in the early 2000s and since then businesses started to collect information, run big data analytics to uncover details for future use.  Thereby, giving organizations the ability to work quickly and stay agile.

This was the time when Doug Laney defined this data as the three Vs (volume, velocity, and variety):

Volume: is the amount of data moved from Gigabytes to terabytes and beyond.

Velocity: The speed of data processing is velocity.

Variety: data comes in different types from structured to unstructured. Structured data is usually numeric while unstructured – text, documents, email, video, audio, financial transactions, etc.

Where these three Vs made understanding big data easy, they even made clear that handling this large volume of data using the traditional framework won’t be easy.  This was the time when Hadoop came into existence and certain questions like:

What is Hadoop?

Is Hadoop another name of big data?

Is Hadoop different than big data?

All these came into existence.

So, let’s begin answering them.

Big Data and Hadoop

Let’s take restaurant analogy as an example to understand the relationship between big data and Hadoop

Tom recently opened a restaurant with a chef where he receives 2 orders per day he can easily handle these orders, just like RDBMS. But with time Tom thought of expanding the business and hence to engage more customers he started taking online orders. Because of this change the rate at which he was receiving orders increased and now instead of 2 he started receiving 10 orders per hour. This same thing happened with data. With the introduction of various sources like smartphones, social media, etc data growth became huge but due to a sudden change handling large orders/data isn’t easy. Hence a need for a different kind of strategy to cope up with this problem arise.

Likewise, to tackle the data problem huge datasets, multiple processing units were installed but this wasn’t effective either as the centralized storage unit became the bottleneck. This means if the centralized unit goes down the whole system gets compromised. Hence, there was a need to look for a better solution for both data and restaurant.

Tom came with an efficient solution, he divided the chefs into two hierarchies, i.e. junior and head chef and assigned each junior chef with a food shelf. Say for example the dish is pasta sauce. Now, according to Tom’s plan, one junior chef will prepare pasta and the other junior chef will prepare the sauce. Moving ahead they will hand over both pasta and sauce to the head chef, where the head chef will prepare the pasta sauce after combining both the ingredients, the final order will be delivered. This solution worked perfectly for Tom’s restaurant and for Big Data this is done by Hadoop.

Hadoop is an open-source software framework that is used to store and process data in a distributed manner on large clusters of commodity hardware. Hadoop stores the data in a distributed fashion with replications, to provide fault tolerance and give a final result without facing bottleneck problem. Now, you must have got an idea of how Hadoop solves the problem of Big Data i.e.

Storing huge amount of data.

Storing data in various formats: unstructured, semi-structured and structured.

The processing speed of data.

So does this mean both Big Data and Hadoop are same?

We cannot say that, as there are differences between both.

What is the difference between Big Data and Hadoop?

Big data is nothing more than a concept that represents a large amount of data whereas Apache Hadoop is used to handle this large amount of data.

It is complex with many meanings whereas Apache Hadoop is a program that achieves a set of goals and objectives.

This large volume of data is a collection of various records, with multiple formats while Apache Hadoop handles different formats of data.

Hadoop is a processing machine and big data is the raw material.

Now that we know what this data is, how Hadoop and big data work. It’s time to know how companies are benefiting from this data.

How Companies are Benefiting from Big Data?

A few examples to explain how this large data helps companies gain an extra edge:

Coca Cola and Big Data

Coca-Cola is a company that needs no introduction. For centuries now, this company has been a leader in consumer-packaged goods. All its products are distributed globally. One thing that makes Coca Cola win is data. But how?

Coca Cola and Big data:

Using the collected data and analyzing it via big data analytics Coca Cola is able to decide on the following factors:

Selection of right ingredient mix to produce juice products

Supply of products in restaurants, retail, etc

Social media campaign to understand buyer behavior, loyalty program

Creating digital service centers for procurement and HR process

Netflix and Big Data

To stay ahead of other video streaming services Netflix constantly analyses trends and makes sure people get what they look for on Netflix. They look for data in:

Most viewed programs

Trends, shows customers consume and wait for

Devices used by customers to watch its programs

What viewers like binge-watching, watching in parts, back to back or a complete series.

For many video streaming and entertainment companies, big data analytics is the key to retain subscribers, secure revenues, and understand the type of content viewers like based on geographical locations. This voluminous data not only gives Netflix this ability but even helps other video streaming services to understand what viewers want and how Netflix and others can deliver it.

Alongside there are companies that store following data that helps big data analytics to give accurate results like:

Tweets saved on Twitter’s servers

Information stored from tracking car rides by Google

Local and national election results

Treatments took and the name of the hospital

Types of the credit card used, and purchases made at different places

What, when people watch on Netflix, Amazon Prime, IPTV, etc and for how long

Hmm, so this is how companies know about our behavior and they design services for us.

What is Big Data Analytics?

The process of studying and examining large data sets to understand patterns and get insights is called big data analytics. It involves an algorithmic and mathematical process to derive meaningful correlation. The focus of data analytics is to derive conclusions that are based on what researchers know.

Importance of big data analytics

Ideally, big data handle predictions/forecasts of the vast data collected from various sources. This helps businesses make better decisions. Some of the fields where data is used are machine learning, artificial intelligence, robotics, healthcare, virtual reality, and various other sections. Hence, we need to keep data clutter-free and organized.

This provides organizations with a chance to change and grow. And this is why big data analytics is becoming popular and is of utmost importance. Based on its nature we can divide it into 4 different parts:

In addition to this, large data also play an important role in these following fields:

Identification of new opportunities

Data harnessing in organizations

Earning higher profits & efficient operations

Effective marketing

Better customer service

Now, that we know in what all fields data plays an important role. It’s time to understand how big data and its 4 different parts work.

Big Data Analytics and Data Sciences

Data Sciences, on the other hand, is an umbrella term that includes scientific methods to process data. Data Sciences combine multiple areas like mathematics, data cleansing, etc to prepare and align big data.

Due to the complexities involved data sciences is quite challenging but with the unprecedented growth of information generated globally concept of voluminous data is also evolving.  Hence the field of data sciences that involve big data is inseparable. Data encompasses, structured, unstructured information whereas data sciences is a more focused approach that involves specific scientific areas.

Businesses and Big Data Analytics

Due to the rise in demand use of tools to analyze data is increasing as they help organizations find new opportunities and gain new insights to run their business efficiently.

Real-time Benefits of Big Data Analytics

Data over the years has seen enormous growth due to which data usage has increased in industries ranging from:







All in all, Data analytics has become an essential part of companies today.

Job Opportunities and big data analytics

Data is almost everywhere hence there is an urgent need to collect and preserve whatever data is being generated. This is why big data analytics is in the frontiers of IT and had become crucial in improving businesses and making decisions. Professionals skilled in analyzing data have got an ocean of opportunities. As they are the ones who can bridge the gap between traditional and new business analytics techniques that help businesses grow.

Benefits of Big Data Analytics

Cost Reduction

Better Decision Making

New product and services

Fraud detection

Better sales insights

Understanding market conditions

Data Accuracy

Improved Pricing

How big data analytics work and its key technologies

Here are the biggest players:

Machine Learning: Machine learning, trains a machine to learn and analyze bigger, more complex data to deliver faster and accurate results. Using a machine learning subset of AI organizations can identify profitable opportunities – avoiding unknown risks.

Data management: With data constantly flowing in and out of the organization we need to know if it is of high quality and can be reliably analyzed. Once the data is reliable a master data management program is used to get the organization on the same page and analyze data.

Data mining: Data mining technology helps analyze hidden patterns of data so that it can be used in further analysis to get an answer for complex business questions. Using data mining algorithm businesses can make better decisions and can even pinpoint problem areas to increase revenue by cutting costs. Data mining is also known as data discovery and knowledge discovery.

In-memory analytics: This business intelligence (BI) methodology is used to solve complex business problems. By analyzing data from RAM computer’s system memory query response time can be shortened and faster business decisions can be made. This technology even eliminates the overhead of storing data aggregate tables or indexing data, resulting in faster response time. Not only this in-memory analytics even helps the organization to run iterative and interactive big data analytics.

Predictive analytics: Predictive analytics is the method of extracting information from existing data to determine and predict future outcomes and trends. techniques like data mining, modeling, machine learning, AI are used to analyze current data to make future predictions. Predictive analytics allows organizations to become proactive, foresee future, anticipate the outcome, etc. Moreover, it goes further and suggests actions to benefit from the prediction and also provide a decision to benefit its predictions and implications.

Text mining: Text mining also referred to as text data mining is the process of deriving high-quality information from unstructured text data. With text mining technology, you uncover insights you hadn’t noticed before. Text mining uses machine learning and is more practical for data scientists and other users to develop big data platforms and help analyze data to discover new topics.

Big data analytics challenges and ways they can be solved

A huge amount of data is produced every minute hence it is becoming a challenging job to store, manage, utilize and analyze it.  Even large businesses struggle with data management and storage to make a huge amount of data usage. This problem cannot be solved by simply storing data that is the reason organizations need to identify challenges and work towards resolving them:

Improper understanding and acceptance of big data

Meaningful insights via big data analytics

Data storage and quality

Security and privacy of data

Collection of meaningful data in real-time: Skill shortage

Data synching

Visual representation of data

Confusion in data management

Structuring large data

Information extraction from data

Organizational Benefits of Big Data

Big Data is not useful to organize data, but it even brings a multitude of benefits for the enterprises. The top five are:

Understand market trends: Using large data and  big data analytics, enterprises can easily, forecast market trends, predict customer preferences, evaluate product effectiveness, customer preferences, and gain foresight into customer behavior. These insights in return help understand purchasing patterns, buying patterns, preference and more. Such beforehand information helps in ding planning and managing things.

Understand customer needs:  Big Data analytics helps companies understand and plan better customer satisfaction. Thereby impacting the growth of a business. 24*7 support, complaint resolution, consistent feedback collection, etc.

Improving the company’s reputation: Big data helps deal with false rumors, provides better service customer needs and maintains company image. Using big data analytics tools, you can analyze both negative and positive emotions that help understand customer needs and expectations.

Promotes cost-saving measures: The initial costs of deploying Big Data is high, yet the returns and gainful insights more than you pay. Big Data can be used to store data more effectively.

Makes data available: Modern tools in Big Data can in actual-time presence required portions of data anytime in a structured and easily readable format.

Sectors where Big Data is used:

Retail & E-Commerce

Finance Services



With this, we can conclude that there is no specific definition of what is big data but still we all will agree that a large voluminous amount of data is big data. Also, with time the importance of big data analytics is increasing as it helps enhance knowledge and come to a profitable conclusion.

If you are keen to benefit from big data, then using Hadoop will surely help. As it is a method that knows how to manage big data and make it comprehensible.

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Preeti Seth

From Big Data To Smart Data

Fight the Big Data Backlash and use Smart Data help you identify purchase intent

Big data is starting to experience some significant backlash. A ‘case in point’ comes from a recent popular article in VentureBeat: ‘Big data’ is dead. What’s next? The backlash is more to do with the buzz than the data but the reason relates to the difficulty of extracting meaningful insights from big data.

Born from the backlash comes another buzzword; smart data, a means of extracting these meaningful insights from big data.

Looking past the marketing hype, smart data is actually the metamorphosis of big data into something actionable. Here we look at recognizing purchase intent as an example of actionable data extraction.

Big data vs Smart data rundown Big data, strong signals, Smart Insights

The big opportunity for big data is how to extract a ‘strong signal’ from the noise. Collecting big data and mining it mercilessly is not the opportunity. The opportunity is leveraging ‘a strong signal’ data set and integrating it to label big data, thus making it immediately usable. This is where an information rich contextual data set can inform big data and turn it into smart data.

Let’s take a real example: Say you were trying to identify and target website visitors who intend to purchase. If you were to rely only on mining your web analytics data for this information you would have to sort through the entire data set looking for the behavioral traits of purchase intenders. This not only is difficult but could be wildly inaccurate. You would think that focusing on the shopping cart is all you would have to do to get a stronger signal of purchase intent, but there is more to the story. Data shows that for a typical e-commerce site only 44% of visitors that enter the cart actually have the intent to purchase while the remaining 56% represent all other intent types such as researchers.

By labeling your data set with a ‘strong signal’ such as visitors who are actually intending to purchase, you can segment and contextualize the web data illuminating the most important aspects of the data set.

Empowering your Big Data

Collecting visitor stated intent, or in other words the way someone describes their intention for visiting a website, provides a much stronger signal because it is the visitor who describes their intention.

iPerceptions research shows that a visitor who states that they intend to ‘purchase’ is 15 to 20 times more likely to do so than someone who describes their intent to ‘research’.  This powerful qualitative intent data paired with quantitative and descriptive data creates contextualized data sets, transforming your big data into smart data.

Putting it all together – Big and Smart Data

Big data is complex and vast but many of the benefits cannot be truly realized without adding contextual information. If these data sources are combined not only can you transform big data into smart data, but you can also provide enormous windfalls for consumers and companies alike improving the customer experience and the company’s ability to meet the needs of its customers. However having the right type of data is only half the story. To make personalization a reality and directly impact the customer experience, a real-time approach to leveraging this information must be taken so that the quickly eroding opportunities can be recognized and acted upon.

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