Top 6 Big Data Tools

5 min readMay 10, 2019

What Big Data tools and techniques to use, SMEs should choose based on specific business objectives.

In our previous articles, we highlighted how Big Data and Analytics would soon transform your business as well as described specific niches that can benefit the most from using it.

In this article, openGeeksLab has created a list of the most effective and sought after Big Data tools to provide a critical competitive advantage in the world market.

Best Big Data Analytics Tools

1. Hadoop

Hadoop is a framework that allows distributing huge data sets between different clusters and processing big numbers. This is one of the most popular Big Data processing tools in the world. Such industry monsters as Netflix, Twitter, and Facebook use this software and similar tools.

The main feature of the product is the Hadoop Distributed File System (HDFS) distributed file system, due to which the repositories are simultaneously located on thousands of nodes, and store exabytes of data.

As for 2019, Apache Hadoop 3.2 was released where the MapReduce Programming Paradigm is implemented. As a result, any task is divided into small fragments which can be processed separately. At the same time, the hardware requirements are rather low.

With Hadoop Libraries, this software is easily integrated into individual solutions of companies.

2. Cassandra

Cassandra is a distributed database that allows managing large amounts of data on different servers. The product works well even with high loads due to the architecture without a single point of failure.

Cassandra is a high value for its scalability and ease of operation due to its convenient and uncomplicated query language. The system is fault-tolerant because there is continuous replication between several data processing centers. It also provides high-speed operation. Due to its properties, Cassandra ensures permanent access to a source of information. Cassandra is an excellent solution for those who cannot afford to lose access to the database even for a short time.

3. Hive

Hive is a solution that allows analyzing large data sets stored on Hadoop. Hive helps to request and manage big numbers quickly.

The minus of the solution is that in the basic version it is suitable for managing and querying only structured data. But its great advantage is the use of SQL-based programming language, which significantly simplifies the work and allows creating a structure for unstructured data.

4. MongoDB

MongoDB is an open source NoSQL cross-platform database that is compatible with many programming languages.

A product allows storing any data: from a text and an integer to lines, data sets, dates, and boolean. The possibility of cloud deployment and a large number of very flexible settings also attract SMEs.

Another critical advantage of MongoDB is that using dynamic schemes allows preparing data in the fastest way possible which ensures savings. It is the ideal solution for businesses which need fast, real-time data for instant solutions, and for those who use Big Data reporting tools.

5. Elasticsearch

Elasticsearch is a powerful search engine that allows the system to index and find the needed file filtered by multiple extensions in real time. It is one of the most effective options to showcase how Big Data visualization tools work.

It ensures a quick full-text search inside the document as well as autocomplete search. ElasticSearch also provides trigram search showing all possible matches with the search keywords.

It is often used by financial and law firms which need to get access to a massive number of archive records to obtain search results quickly.

Another advantage of the system is the ability to use it by a large number of users within one virtual workplace.

6. TensorFlow

TensorFlow is Google’s open source library and powerful AI system that helps to implement machine learning functions and then process information using AI functionality.

It works like this — you configure the machine to search for patterns in the processed data, and then the algorithms begin to search for similar patterns.

In Google Photos, for instance, TensorFlow algorithms allow extracting location data from images and their context.

If until 2018, TensorFlow had a lot of restrictions, now the developers have fixed them. Now it is possible to program in many popular languages, and it gives flexibility in the choice of software for large databases processing implementation. It also became possible to use a boilerplate code and implement a mobile version of this library.

Which Big Data Testing Tools Are Right for Your Business?

The software listed above is about how to work with big numbers correctly and use its benefits for your business. But there are plenty of other tools that have many options and can help you to save time and uncover essential business insights. Some of them complement each other, some are mutually exclusive. A careful choice allows you to find a diamond in the rough.

We at openGeekLab, as your tech partners, pick out the solutions which are right for your project and have a specific feature set. Just drop us a line to make your product more flexible, efficient, and relevant to the market changes.

If you have any further questions, please don’t hesitate to contact us.

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This article was originally published on the openGeeksLab blog.