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8 Best Big Data Tools in 2020

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8 Best Big Data Tools in 2020

In this article, look at eight of the best big data tools.

· Big Data Zone ·
Free Resource

In today’s reality, data gathered by a company is a fundamental source of information for any business. Unfortunately, it is not that easy to drive valuable insights from it.

Problems with which all data scientists are dealing are the amount of data and its structure. Data has no value unless we process it. To do so, we need big data software that will help us in transforming and analyzing data.

Best Big Data Tools in 2020

Below, I present big data tools that offer the most opportunities in 2020.

Apache Hadoop

Apache Hadoop is without a doubt the most popular big data tool. It is an open-source framework that allows users to process huge amounts of data and operates on commodity hardware in an already existing data center.

Apache Hadoop is free under the Apache License.

Main features:

  • cloud infrastructure
  • libraries supporting other models to work on this framework
  • MapReduce - a model used in processing big data
  • HDFS - a distributed file system allowing holding any type of data
  • highly scalable
  • efficient and flexible data processing

Apache Storm

Apache Storm is a free distributed real-time framework that supports any programming language.  It is written in Java and Clojure. Apache Storm can process and transform streams of data from different sources. 

This big data tool is free.

Main features:

  • fault-tolerant
  • can process one million 100-byte messages per second per node
  • integrates with any programming language
  • fast and scalable
  • ensures the processing of each unit of data (at least once or exactly once)

RapidMiner

RapidMinder is an open-source cross-platform big data tool. It integrates data science, predictive analytics, and machine learning technology. It offers a range of products enabling you to build new data mining processes. 

The tool is available under various licenses. The free one enables users 1 logical processor and up to 10,000 data rows. The commercial version of Rapidminer starts at $2.500 per year.

Main features:

  • well-developed cloud integration
  • interactive dashboards which are easy to share
  • integration with in-house databases
  • building and validating predictive models
  • variety of data management methods
  • predictive analytics based on big data
  • support of a client-server model

Qubole

Qubole is an autonomous big data platform. Basing on your activity, it learns, optimizes, and manages data. Data professionals can focus exclusively on heir business tasks rather than managing the framework.

Qubole is a subscription-based tool designed mostly for big enterprises with multiple users. The price starts at $199 per month. 

Main features:

  • optimized for cloud
  • high flexibility
  • easy to use
  • open-source engines
  • automatically introduces procedures to avoid repetition of manual actions
  • actionable Alerts, Insights, and Recommendations that optimize reliability, performance, and costs

Tableau

Tableau is a data visualization tool for business intelligence and data analytics. The software contains of three main products:

  1. Tableau Desktop — suitable for an analyst
  2. Tableau Server — suitable for an enterprise
  3. Tableau Online  suitable for a cloud

This big data tool can process all data sizes. Enables live-data visualization through web-connector. It is easy to use.

Tableau offers a free trial. Subscription starts from $35 per month, depending on an edition (desktop/server/online).

Main features:

  • enables real-time collaboration,
  • users can create any type of visualization,
  • no-code data queries,
  • sharing interactive dashboards suitable for mobile devices,
  • easy and fast software setup,
  • blending various datasets.

Cassandra

Apache Cassandra is an open-source, distributed type database designed to manage large volumes of data spread across the servers. It focuses on structured data sets. Its service ensures no point of failure.

This big data tool is free.

Main features:

  • processes huge amounts of data very fast,
  • linear scalability,
  • cloud availability,
  • no single point of failure,
  • automatic replication,
  • easy data distribution among data centers.

Apache Spark

Apache Spark is an open-source tool that handles both real-time data and batch data. It enables in-memory data processing which ensures faster outcomes. This big data tool can run on a single local system enabling easier testing and development. 

This tool is based on the Apache License. Offers free trial.

Main features:

  • enables high-streaming operations,
  • includes a fast graph processing system,
  • standalone cluster mode,
  • stack of libraries combined in the same application,
  • DataFrame API,
  • deploy to cloud environments.

Flink

Apache Flink is an open-source framework of stream processing for big data. It can be both bounded and unbounded. Flink can run in all known cluster environments. It is able to perform tasks at any scale and at in-memory speed.

Main features:

  • accurate results (also for out-of-order or late-arriving data),
  • fault-tolerant and recovers from a failure,
  • supports a variety of connectors to third-party systems for data sources,
  • enables flexible windowing,
  • runs on thousands of nodes.

All In All

Today, there are plenty of big data tools available. It is crucial to clearly define your needs before choosing the right framework for your business. Follow big data trends to be up to date with the latest solutions.

As most platforms offer a trial version, it is advisable to devote time to check different big data tools, so that it suits your requirements and work style.

Topics:
big data, big data tools, data science, software

Opinions expressed by DZone contributors are their own.

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