Top 9 Big Data and Data Analytics tools

In today’s IT world, data is everything. But data without information is meaningless. Also, in 2020, every person generates 1.7 megabytes in just a second. Internet users are generating about 2.5 quintillion bytes of data each day.

This big data is too large and cannot be handled with traditional data processing systems. Thus there is a need for tools and techniques to analyze and process Big Data to gain insights from it. There are various big data tools from different vendors for analyzing big data.

Top 9 Big Data Analysis and Data Analytics tools in 2021 are

1. Apache Hadoop:

Apache Hadoop is the topmost big data too. It is an open-source software framework written in Java for processing varying varieties and volumes of data.

It is best known for its reliable storage (HDFS), which can store all types of data such as video, images, JSON, XML, and plain text over the same file system.

Hadoop processes big data utilizing the MapReduce programming model. It provides cross-platform support. Apache Hadoop enables parallel processing of data as data is stored in a distributed manner in HDFS across the cluster.

Over half of the Fortune 50 companies, including Hortonworks, Intel, IBM, AWS, Facebook, Microsoft, use Hadoop. If you haven’t yet started with Hadoop don’t worry here is the help, I have found this Optimal way of Learning Hadoop.
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2. Apache Spark:

Apache Spark is another popular open-source big data tool that overcomes the limitations of Hadoop. It offers more than 80 high-end operators to assist in order to build parallel apps. Spark provides high-level APIs in R, Scala, Java, and Python.
Spark supports real-time as well as batch processing. It is used to analyze large datasets.

The powerful processing engine allows Apache Spark to quickly process the data in a large-scale. Spark has the ability to run apps in Hadoop clusters 100 times quicker in memory and ten times quicker on disk.

It provides more flexibility as compared to Hadoop since it works with different data stores such as OpenStack, HDFS, and Apache Cassandra. It is also useful for machine learning like KNIME.

Apache Spark contains an MLib library that offers a dynamic group of machine algorithms that can be used for data science such as Clustering, Collaborative, Filtering, Regression, Classification, etc.

3. Apache Cassandra:

Apache Cassandra is an open-source, decentralized, distributed NoSQL(Not Only SQL) database which provides high availability and scalability without compromising performance efficiency.
It is one of the biggest Big Data tools that can accommodate structured as well as unstructured data. It employs Cassandra Structure Language (CQL) to interact with the database.

Cassandra is the perfect platform for mission-critical data due to its linear scalability and fault-tolerance on

commodity hardware or cloud infrastructure.

Due to Cassandra’s decentralized architecture, there is no single point of failure in a cluster, and its performance is able to scale linearly with the addition of nodes. Companies like American Express, Accenture, Facebook, Honeywell, Yahoo, etc. use Cassandra.

4. Apache Storm:

Apache Storm is an open-source distributed real-time computational framework written in Clojure and Java. With Apache Storm, one can reliably process unbounded streams of data (ever-growing data that has a beginning but no defined end).

Apache Storm is simple and can be used with any programming language. It can be used in real-time analytics, continuous computation, online machine learning, ETL, and more.

It is scalable, fault-tolerant, guarantees data processing, easy to set up, and can process a million tuples per second per node.

Among many, Yahoo, Alibaba, Groupon, Twitter, Spotify uses Apache Storm.
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5. MongoDB:

MongoDB is an open-source data analytics tool. It is a NoSQL, document-oriented database written in C, C++, and JavaScript and has an easy setup environment.

MongoDB is one of the most popular databases for Big Data as it facilitates the management of unstructured data or the data that changes frequently.

MongoDB executes on MEAN software stack, NET applications, and Java platforms.
It is also flexible in cloud infrastructure. It is highly reliable, as well as cost-effective. The main features of

MongoDB include Aggregation, Adhoc-queries, Indexing, Sharding, Replication, etc.
Companies like Facebook, eBay, MetLife, Google, etc. uses MongoDB.

6. Talend:

Talend is an open-source platform that simplifies and automates big data integration. Talend provides various software and services for data integration, big data, data management, data quality, cloud storage.

It helps businesses in taking real-time decisions and become more data-driven. Talend simplifies ETL and ELT for Big Data. It accomplishes the speed and scale of Spark. It handles data from multiple sources.

Talend provides numerous connectors under one roof, which in turn will allow us to customize the solution as per our need.

Companies like Groupon, Lenovo, etc. use Talend.
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7. Lumify:

Lumify is open-source, big data fusion, analysis, and visualization platform that supports the development of actionable intelligence.

With Lumify, users can discover complex connections and explore relationships in their data through a suite of analytic options, including full-text faceted search, 2D and 3D graph visualizations, interactive geospatial views, dynamic histograms, and collaborative workspaces shared in real-time.

Using Lumify, we can get a variety of options for analyzing the links between entities on the graph. Lumify comes with the specific ingest processing and interface elements for images, videos, and textual content.

Lumify’s infrastructure allows attaching new analytic tools that will work in the background to monitor changes and assist analysts. It is Scalable and Secure.

8. Apache Flink:

Apache Flink is an open-source framework and distributed processing engine for stateful computations over unbounded and bounded data streams.

It is written in Java and Scala. It is designed to run in all common cluster environments, perform computations in-memory and at any scale. It doesn’t have any single point of failure.

Flink has been proven to deliver high throughput and low latency and can be scaled to thousands of cores and terabytes of application state.

Flink powers some of the world’s most demanding stream processing applications like Event-Driven applications, Data Analytics applications, Data pipeline applications.
Companies, including Alibaba, Bouygues Telecom, BetterCloud, etc. uses Apache Flink.
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9. Tableau:

Tableau is a powerful data visualization and software solution tools in the Business Intelligence and analytics industry.

It is the best tool for transforming the raw data into an easily understandable format with zero technical skill and coding knowledge.

Tableau allows users to work on the live datasets and to spend more time on data analysis and offers real-time analysis.

Tableau turns the raw data into valuable insights and enhances the decision-making process.
It offers a rapid data analysis process, which results in visualizations that are in the form of interactive dashboards and worksheets. It works in synchronization with the other Big Data tools.

Conclusion

In this post, we’ve explored some of the most popular data analysis tools currently in use. The key thing is that there’s no one tool that does it all. A good data analyst has wide-ranging knowledge of different languages and software.

If you found a tool on this list that you didn’t know about, You can research more.