Top 10 Big Data Tools in 2018

Big data has been a game changer for organizations across industries and revenue size. Big data helps companies to process data of great complexity and size at a speed and accuracy that helps in making better decision. If a company has to sift and sort through some millions of records to pick out that one faulty record that the auditor is asking for, then big data technology can help it index and search through those legacy records in record time. There are many more scenarios where big data can propel a company’s success and help it make its processes smoother and more efficient.

The following big data tools are in great use today and each of them offer a specific niche advantage to the firm using it.

Data Engineering Tools

Kafka

Apache Kafka is a tool that allows you to handle large volumes of rapid data with a relatively modest set of hardware. It is used to create the subscription based messaging functionality that allows asynchronous messaging to work on the basis of large amounts of data. It can process many events per day (LinkedIn has reported Kafka to be ingesting 1 trillion events a day!) and process this data. It can generate messages for parallel consumption in a fault-tolerant manner. Kafka is extremely beneficial to organizations who want to maintain large messaging channels without having the expensive hardware to do it.

Cloudera

Cloudera is the first company to offer a Hadoop distribution. The idea of a Hadoop distribution is to get a company to better set up and easily manage their Hadoop clusters. Cloudera is an excellent tool in this regard as it offers a comprehensive console that gives great insight into the state of all your Hadoop clusters. It also supports the Node Template feature. This means, to deploy a particular repeating node configuration, you can create a template and re-use it to create more nodes, instead of having to reconfigure from the start. Cloudera is an experienced player in this arena that has built a solid reputation for security and stability in all Hadoop installations.

Splunk

Splunk is a powerful data aggregator and analyzer tool that can gather extensive amounts of data in real-time and also generate insights in the form of reports and dashboards. It is used in analyzing machine-generated big data (like logs, error reports, status reports etc.) Splunk is advantageous to organizations as it can be used in the areas of application management, security and compliance to process logs of data to get to know discrepancies, if any and to detect the instances of anomaly that can be useful for compliance purposes.

Elastic Search

ElasticSearch is a powerful search engine that allows a system to index and find a file (of many possible formats) in real-time. ElasticSearch allows an organization to quickly set up fast and reliable search functionality to implement full-text search, autocomplete supported search, fuzzy search (where you can get an approximate match with the keywords) and also document-oriented search. The last one has a powerful impact on finance and legal firms where massive amounts of historical records have to be accessed to generate search results quickly. ElasticSearch can also work on a multi-tenant system which makes it very cost effective to set up to address users working on different installations or versions of the same master system. Organizations can also capitalize on ElasticSearch’s language analyzers, spell check, synonym match and stemming to refine its search experience.

Flume

The Hadoop File System is an excellent tool for running MapReduce jobs to process the extensive amounts of data that big data technology is known for. But to make it work, a data ingestion tool is needed that can collect, aggregate and transport that volume of data into the file system. Apache Flume is an excellent tool in this category. It is advantageous to organizations as they can get different sources of data like emails, social media logs, network traffic all ingested in the file system, efficiently and reliably. Flume also automatically maintains a steady flow of data between the ingesting and persisting operations. This means, if your systems ingest data faster than it is being sent over, then their processes won’t be blocked.

Machine Learning and Deep Learning Tools

Apache Spark

Apache Spark is sort of an alternative to Hadoop that has been built on top of the Hadoop Distributed File System (HDFS). It does the same thing as Hadoop does but it does it slightly differently (placing the data into Resilient Distributed Datasets, to improve accessibility). It helps organizations run MapReduce jobs faster, thus opening up more powerful avenues in stream data processing. This has a direct application is areas like fraud detection, trading data, log processing etc. This also helps an organization to run faster graph processing jobs that assist in advertising and social media analysis.

TensorFlow

TensorFlow is the famed Artificial Intelligence system from Google that helps in implementing machine learning functionality and generating insights from data, with AI features. A great example of this is the Google Photos app, where TensorFlow has been used to automatically detect the locations of the pictures and the context. TensorFlow can offer many cutting edge advantages to organizations as it can help them run big data experiments on a large scale. It can be set up to find patterns in the data and the same algorithm can then locate similar patterns and specific actions can be triggered on the basis of that. This has significant impact on customer loyalty programs that can be preempted to present points or discounts based on predictable customer behavior.

Mist

While Apache Spark is great to run many jobs to quickly crunch data, Mist comes in to run and manage several Apache Spark applications in tandem. It is important as practically speaking, a large enterprise would seldom use a single set up of Apache Spark. The advantage offered by Mist here is that the big data team or the IT admin team can set up Spark for multiple departments and locations using Mist.

Visualization Tools

Qlik

Once a big data system crunches the data that you have to offer, it is important to have a tool that can generate insights into that data. Qlik (under which you have QlikView) enables organizations to analyze the data, whether it is aggregated from multiple sources or from a single large source. QlikView provides excellent dashboards, statistics, drillable reports and other Management Information System functionality to make sense of all the data that you have painstakingly gathered. Qlik also supports the mobile interface which means that its apps and dashboards are accessible on the go as well.

Tableau (and Tableau Public)

Tableau is frequently known as the holy grail of Management Information Systems reporting. It supports a wide variety of reporting options and tools within its umbrella. It is known widely for its visualization capabilities and the ability to drag and drop different visual elements to create your own compelling visual reports is its true advantage. It can work with large amounts of data as well and can process it efficiently to generate beautiful reports and graphs.

Tableau Public is the community version of Tableau that is offered for free. While it can pretty much do everything that enterprise Tableau can do, it is limited by the size of the data sets that it can process.

Final Thoughts

The entire gamut of tools talked about above move specific cogs in the big data clock-house to deliver a compelling range of functionalities that make companies more nimble, more efficient and more welcoming to the changing forces of the market. As the market only promises to produce more and more data for every facet of any business, it is big data that holds the true promise of helping a business out there, to make sense of the ever growing oceans of data.

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Author: Pradeep Parthiban
Pradeep is a Content Writer and Digital Marketing Specialist at Indium Software with a demonstrated history of working in the information technology and services industry.

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