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In the early 1800s, as the field of statistics expanded, it included collecting and analyzing data. But it saw the first problem with the overwhelming amount of data. Throughout the 20th century, volumes of data kept growing at an unexpected speed and machines started storing information magnetically and in other ways. Then computers started doing the same. Accessing and storing huge data volumes for analytics was going on for a long time. But ‘big data’ as a concept gained popularity in the early 2000s when Doug Laney, an industry analyst, articulated the definition of big data as the 3Vs. The Latest Big Data Statistics Reveal that the global big data analytics market is expected to earn $68 billion in revenue by 2025. No doubt companies are investing in big data and as a career, it has huge potential. Many business owners and professionals are interested in harnessing the power locked in Big Data using Hadoop often pursue Big Data and Hadoop Training. We will discuss more on this later in this article.
Big data is a huge collection of structured, semi-structured and unstructured data that organizations keep collecting for information, business, machine learning, predictive modeling and plenty of other applications. Big data is often denoted as three V’s: Volume, Variety and Velocity.
Some examples of Big Data:
1. Data generated by social media
statistics show that Facebook alone generates 500+terabytes of new data every day in the form of photo, video uploads, message exchanges, comments and replies.
2. Data generated by Jet engines
In a flight time of 30 minutes, a single jet engine can generate 10+terabytes of, and with thousands of airplanes flying across the world each day, this data volume reaches to many Petabytes (one Petabyte equals 1,000 Terabytes).
1. Structured (any data that can be stored, accessed and processed in a fixed format)
2. Unstructured (Any data with an unknown form or structure, like heterogeneous data source containing a combination of simple text files, images, videos etc.)
3. Semi-structured (may contain both the forms of data like data represented in an XML file.
Source - Guru99.com
The key understandings of big data:
It is not the amount of data a company possesses, but the importance and advantage of big data depend on how a company interprets and utilizes it. Because of its sheer diversity, it becomes inherently complex to handle big data; resulting in the need for systems capable of processing the different structural and semantic differences of big data.
The more effectively a company is able to collect and handle big data the more rapidly it grows. Because big data has plenty of advantages, hence its importance cannot be denied.
As per a report from the IDC, by 2024, the revenues from big data analytics are expected to reach $274.3 billion. No doubt then, businesses around the globe put so much importance on big data and its potential for business and revenue increase. They leverage the advantage of big data by making the best use of the tools used in big data. A big data tool is software that extracts information from various complex data types and sets, and then processes these to provide meaningful insights. Traditional databases cannot process huge data hence best big data tools that manage big data easily are used by businesses. However, big data analytics and using big data tools must be learned. The good part is anyone from a non-technical background also can learn the skills from Big Data Analytics Training.
We are discussing here the top big data tools:
This open-source software framework processes data sets of big data with the help of the MapReduce programming model. Written in Java it provides cross-platform support. This is one of the most popular big data tools used by most Fortune 50 companies, including Amazon Web services, Hortonworks, IBM, Intel, Microsoft and Facebook among others.
Pros:
Cons:
Pricing: Free to use under the Apache License.
Totally open source with a free platform distribution encompassing Apache Hadoop, Apache Spark, Apache Impala, and others for unlimited data processing.
Pros:
Cons:
Pricing: As such CDH is a free software from Cloudera but the per-node cost is approximately $1000 to $2000 per terabyte.
This is also an open-source free of cost software capable of handling huge volumes of data spread across numerous servers employing CQL(Cassandra Structure Language) to interact with the database. Apache Cassandra is used by many fortune 500 companies including Facebook, General Electric, Honeywell, Yahoo, Accenture and American Express among others.
Pros:
Cons:
Pricing: Free of cost.
KNIME (an acronym for Konstanz Information Miner) is an open-source tool that supports Linux and Windows operating systems. It is quite useful for Enterprise reporting, integration, research, CRM, data mining, data analytics, text mining, and deriving business intelligence. Many branded companies like Johnson & Johnson, Canadian Tire, Comcast etc., use it.
Cons:
Pricing: Free of cost.
Again, an open-source tool that helps in data visualization and prepares precise, simple and embeddable charts fast. The platform is used by many big brands like Twitter, Bloomberg, The Times and Fortune to name a few.
Pros:
Cons:
Pricing: Offers both Free and pricing models. The pricing can be had from the Datawrapper site.
Free and open-source tool supporting multiple operating systems, including Windows Vista (and later versions), OS X (10.7 and later versions), Linux, Solaris, and FreeBSD. This NoSQL, document-oriented database is written in C, C++, and JavaScript. It is used by brands like Facebook, eBay, MetLife and Google, among others.
Pros:
Cons:
Pricing: Pricing for the enterprise and SMB versions is available on request.
Used for big data fusion/integration, analytics and visualization, this is an open-source and free tool. Its primary features include
Pros:
Pricing: Free of cost.
Written in C++ and a data-centric programming language knowns as ECL(Enterprise Control Language) the tool (developed by LexisNexis Risk Solutions ) offers a three sixty-degree big data solution over a massively scalable supercomputing platform and is also called DAS (Data Analytics Supercomputer). Based on a Thor architecture, this open-source tool offers a good substitute for Hadoop and some other big data platforms as well.
Pros:
Pricing: Totally free of cost.
This Cross-platform, free, fault-tolerant and open-source tool is based on customized spouts and bolts architecture and written in Clojure and Java. Yahoo and Alibaba among others use this.
Pros:
Cons:
Pricing: Totally free of cost.
This open-source platform (used for big data stream mining and machine learning) helps users to create the distributed streaming machine learning (ML) algorithms plus run them on multiple distributed stream processing engines or DSPEs.
Pros:
Fast, scalable, free and fun to use with real-time streaming.
Cons:
Pricing: Free of cost.
It is a free and open-source data integration platform and offers different software and services as well, suitable for big data, data integration, data management, data quality, cloud storage and enterprise application integration.
Pros:
Handles multiple data sources with numerous connectors under one roof to customize a solution as per the need.
Cons:
Pricing: It offers a free trial of each of its product versions.
Coming with various license options (like small, medium and large proprietary editions plus a free edition allowing 1 logical processor and up to 10,000 data rows), this cross-platform tool brings a comprehensive environment for data science, machine learning and predictive analytics.
Pros:
Cons:
Pricing: The website has the entire pricing information.
This is an all-inclusive big data platform managing on its own thus freeing the users from managing the platform.
Pros:
Pricing: It uses a proprietary license offering business (free of cost till 5 users) and subscription-based enterprise edition (for this the team needs to be contacted).
This quite famous software comes with three different options a)Tableau Desktop (for the analyst), b) Tableau Server (for enterprise use ) and Tableau Online (cloud-based). Tableau Reader and Tableau Public are the two recent additions. It is used mainly for data visualization, exploration and understanding. It is capable of handling all sizes of data and is quite easy to handle by technical and non-technical user base and comes with real-time customized dashboards.
Pros:
Cons:
Pricing: It is not free and the pricing starts from $35/month (with different desktop, server and online editions). Each edition has a free trial offer.
This flexible end-to-end marketing analytics platform is very useful to marketers for effortless tracking of market performance and to uncover new insights in real-time with powerful data visualizations and AI-powered predictive analytics and others.
Pros
Pricing: This software tool is not free and its subscription-based pricing can be had from the company.
This is a comprehensive elastic and scalable cloud platform tool to integrate, process, and prepare data for analytics on the cloud with low-code and no-code capabilities. It helps users to make the most of their data without having to invest in other software or hardware and also provides support via email, chats, phone and online meetings as well.
Pros:
Cons:
Pricing: Quotation based, comes with a 7-days free trial option.
Any business needs to customize the big data tool as per its specific requirements. Some of the important factors to look for while selecting one are:
Choosing the right big data tool for a business will depend on the business needs of the particular business. Factors like the types of data that the business needs to manage, the information needed to be extracted from the data, the applications the business uses and the sources from where the data comes. Only after considering these a tool or even a combination of tools should be chosen. Last but not least, the cost factor also needs to be considered and accordingly, the model(s) should be chosen.
Having had the in-depth big data tools overview, factors to be considered before selecting a tool, here are some of the benefits that big data tools and applications offer:
Covid19 is perhaps the best example of how companies and even governments worldwide were benefited by using big data insights to predict risk and remain ready for the unexpected.
A Gartner survey 2020 reports that businesses, especially the growing organizations, collect customer experience data “more than non-growth companies”. Businesses of course can leverage big data to improve their customer experiences and thus increase their brand value and business both.
Big data tools and technologies are used nowadays in almost every industrial sector across the globe to identify trends and patterns, get insights into customers’ experiences and expectations, and tackle complex problems. There is a multitude of other reasons why various companies use big data analytics. For example, reasons like augmenting research, making forecasts, making the best use of advertising by targeting the key audience, their choices, behavior patterns, psychology and more. Below is the list of business sectors where big data tool kits are used massively.
Pharmaceutical companies, hospitals and research centers use big data of patient and population data for advancing healthcare, researching diseases, developing new drugs, and drawing insights on health patterns of different populations.
The banking and financial sectors use big data for various purposes like risk assessment and management, predictive analysis, fraud detection, credit rankings, brokerage services, blockchain, and cyber security among various other purposes.
News channels, newspapers and media companies analyze audience data on viewing, reading, watching and listening habits. For example, Netflix, YouTube, Hulu etc. are some of the entertainment companies that provide viewing recommendations to audiences by gaining insight from big data analytics.
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Hopefully, this in-depth article covering almost everything about big data, its use, popular big data tools, both open source and paid versions, their industrial applications and the tips to choose the correct one has helped you provide an overview on the subject. Before we end, in case of going for a paid version of any big data processing tools it is advisable always to first explore and experience the trial version alongside connecting with its existing customers, if possible, to understand their feedback.
Structured, unstructured and semi structured.
Data generated by social media
Unified, flexibility and accessibility
Integrate.io, Tableau, Rapidminer,Apache Hadoop and others. Actually, it depends on the exact business objective and requirements.
There are many and often used by fortune500 companies depending on their business objectives. Apache Hadoop, Tableau, Adverity are a few examples.
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