Introduction

From the name itself, you can make out that Big Data is big, massive and therefore, it is impossible for one single tool or maybe a couple of big data tools to handle this behemoth. Big Data Architecture is typically a collection of several tools which specialize in a certain aspect of the Big Data ecosystem and sometimes adapt well to the business requirements. There are several big data tools in the market, both proprietary and open-source, to control, manage and extract value from this mammoth collection of structured and unstructured data. This listicle brings up a list of the 20 most popular big data tools.

Big Data Tools of 2021

The big data tools that we discuss below will hold the Big Data Scene for some time to come before a new technology takes over. You have to understand that technology is an evolving domain, and today’s technology might not be in use 3-5 yrs down the line. Let’s start with our list of the most popular Big Data tools at present.

  1. Hadoop
  2. Qubole
  3. HPCC
  4. Cassandra
  5. MongoDB
  6. Apache Storm
  7. CouchDB
  8. Statwing
  9. Flink
  10. Pentaho
  11. Hive
  12. RapidMiner
  13. Cloudera
  14. DataCleaner
  15. Openrefine
  16. Talend
  17. Apache Samoa
  18. Neo4j
  19. Teradata
  20. Tableau 

1. Hadoop

Apache Hadoop has been the cornerstone, so to speak, of Big Data. It is the framework that Big Data was proposed on and became wildly popular. Apache Hadoop is an open-source framework based on a distributed file system, the Hadoop Distributed File System. Hadoop is built on commodity hardware and is based on the premise that hardware is subject to failure and automatically adjusts to the node failures to keep the system highly available.

2. Qubole

Qubole is an open-source big data tool that helps to harness your big data further in the value chain by using ad-hoc analysis, Machine Learning, Artificial Intelligence by helping build a data lake for your business. Qubole can easily adapt to a multi-cloud setup with support for AWS, Google Cloud and Microsoft Azure. Qubole is a tool that can take up the entire space for Big Data Analytics within a big data architecture. Qubole offers the whole gamut of tools to perform Big Data Analytics using SQL, Notebooks and dashboards. Qubole can bring down the cost of big data cloud computing by at least 50%.

3. HPCC

HPCC is an ETL engine used to extract, transform, and load data using a scripting language called ECL. HPCC also offers a query engine, ROXIE, an index-based search engine. HPCC can take data processing within the Big Data Architecture, easily integrating and managing data clusters.

4. Cassandra

Apache Cassandra is an open-source NoSQL distributed Management System used primarily for unstructured data across a commodity hardware cluster. Cassandra is a highly available system following a No Single Point Of Failure architecture, replicating data automatically for maximum fault tolerance and capable of handling a high volume of unstructured data.

5. MongoDB

MongoDB is an open-source, cross-platform document-oriented database program for NoSQL data processing, allowing scalable querying and indexing while offering high availability. MongoDB stores data using J-Son like documents. MongoDB is also a distributed database, horizontally scaling, and distributed geographically.

6. Apache Storm

Apache Storm is again an open-source, distributed real-time computational framework offering big data analysis. Apache Storm is capable of consuming streams of data from multiple sources. Apache Storm can easily integrate with any programming language. A storm is also scalable and fault-tolerant. Use cases for Storm include ETL, online machine learning, distributed RPC and real-time analytics, among others.

7. CouchDB

Apache CouchDB is a document-oriented NoSQL database implemented in a functional programming language, Erlang. CouchDB uses JSON to store data and JavaScript as its query language for MapReduce, and HTPP calls for API. The tool makes a user-friendly interface available for insertion, update, retrieval and deletion of a document.

8. Statwing

Statwing is a statistical analysis tool designed solely for tables of data. It is an efficient tool that analyses tabular data in minutes. Statwing also is designed to scale up to massive data volumes where other similar big data tools will slow down considerably. Data cleansing, exploring relationships, and visualizations are done effortlessly in Statwing.

Apache Flink is an open-source stream and batch processing framework for stateful computation of data. Flink core is all about distributed streaming data flow, executing data flow programs in a data-parallel Apache Flink is compatible with all known cluster systems like Hadoop, YARN, Kubernetes and more. 

10. Pentaho

Pentaho is an analytical software that provides many features like data integration, ETL, OLAP services, dashboarding, reporting, all on a single platter. With a wide range of support for big data sources, it can access and integrate various data for analytical and visualization purposes.

11. Hive

An open-source ETL and data warehousing tool, Hive is an HDFS based database for structured data with SQL support. It is developed for the Hadoop architecture and provides SQL like interaction with various databases and file systems on Hadoop. You can build user-defined functions with Hive for data cleansing, filtering etc.

12. RapidMiner

RapidMiner is a data science software platform with an open-source core, RapidMiner Studio, among other big data tools, making an end-to-end suite of tools that can be used for almost all activities in the chain from data preparation to machine learning to data model development. RapidMiner helps to develop predictive models easily without any programming required.

13. Cloudera

Cloudera is an enterprise software platform that supports data warehousing, data engineering, machine learning and big data analytics either running on the cloud or on-premises. 

14. DataCleaner

DataCleaner is a data profiling engine used for improving data quality in big data projects. It is used in the discovery and analysis of the quality of big data projects. With a user-friendly interface, DataCleaner offers extensive data profiling, data wrangling and data monitoring services. DataCleaner offers GUI based tools along with its command-line utilities and does not require coding.

15. Openrefine

Openrefine is a spreadsheet-based desktop tool used for data wrangling and data cleansing activities. It can scale up to big data levels by integrating with web services and external data sources. Openrefine can work with semi-structured data to transform it into a structured data set for further analysis.

16. Talend

An ETL tool, Talend provides data integration, data quality, data management and preparation services along with plugins that help it to integrate with big data architectures effortlessly easily. Talend supports plugging into any cloud-based data warehouse.

17. Apache Samoa

Apache Scalable Advanced Massive Online Analysis (SAMOA) is a distributed streaming machine learning framework that provides a layer of abstraction for distributed stream processing engines like Apache Storm, Apache Flink and Apache Samza.  Apache SAMOA ML can be executed across multiple DSPEs easily.

18. Neo4j

Newo4j is a schema-less, highly scalable, highly available and flexible, ACID compliant transactional graph database management system. Neo4j stores everything in the form of edge, node or attribute. Neo4j interfaces through Cypher Query Language using a transactional HTTP endpoint.

19. Teradata

Tera data is your answer to big data scale data warehousing application, offering end to end solutions based on Massively Parallel Processing. Teradata is a highly scalable structured data warehousing tool with SQL support.

20. Tableau 

With its graphical user interface and advanced visualization tools, Tableau is a spreadsheet-based analytical tool that scales up to big data levels quite easily. Tableau brings in enterprise-scale collaboration, sharing, dashboarding, in-memory processing and connectivity, and a wide array of data sources.

Conclusion

Big Data technology is a constantly evolving domain, and to stay abreast of current technology. You must constantly be acquiring the necessary skills through training and hands-on experience. With Big Data becoming the mainstay at medium and large enterprises and the focus shifting to Machine Learning and Artificial Intelligence, there are a number of career opportunities in the Big Data domain. 

Big data analysts are at the vanguard of the journey towards an ever more data-centric world. Being powerful intellectual resources, companies are going the extra mile to hire and retain them. You too can come on board, and take this journey with our Big Data Specialization course.

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