Data tools

Best Big Data Tools and Software for Analytics 2021

In today’s business landscape, data is the key ingredient for long-term growth. However, specialized tools and software are needed to translate data into actionable information. Without it, the data is effectively worthless.

A recent report from Sigma Computing found that 63% of company employees cannot collect insights from their data within the required time frame, which means that data is more of a productivity inhibitor than a productivity booster. The right tool will provide valuable information and meet your business needs without being too costly.

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What is Big Data?

Big data describes the information companies use to automate processes, discover high-level patterns, and innovate in products or services. This contrasts with traditional datasets, which are generally more consistent, simpler, and less valuable.

Basically, big data is made up of three concepts known as Three Vs:

  • Volume: The collective amount of data from a wide range of sources
  • Variety: How data is formatted (structured versus unstructured)
  • Speed: The speed at which data is received and processed

Big data requires more advanced software and more computing power to process than traditional data sets, so a business looking to leverage big data should be prepared to make significant investments in the technology and IT staff to manage it. . However, big data is also more robust and accurate, so it can offer more business value and opportunities in the long run.

Read also : No digital transformation without Big Data

What is big data analysis?

Big Data Analysis describes the techniques and technologies used to analyze very large and diverse datasets. Businesses can use big data analysis tools to process structured, unstructured, or semi-structured data from multiple sources. Without these tools, big data would be impossible to manage.

In addition to processing datasets, big data analysis methodologies and tools are involved in data visualization, business forecasting, and data-driven decision making. Unlike traditional data technologies, these tools take raw data a step further by giving it context and meaning. Instead of just a repository of individual records, big data analytics tools help organizations get a bird’s eye view of the data being created.

Read also : Data management with AI: making big data manageable

There are a variety of big data tools that help organizations perform analysis. Some are all-in-one solutions, while others are more focused on a specific area like data visualization or data integration.


Tableau is industry-leading data visualization software that data analysts and business intelligence teams use to create compelling graphical representations of their data. It connects quickly and efficiently to data from a wide variety of sources and has one of the most advanced feature sets on the market.

Although Tableau says its interface is designed to meet any user’s skill level, many customers have reported that general users need a little more training to get the most out of the platform. . Seasoned data analysts, however, should have no trouble navigating Tableau to set it up and start digging into the data.


  • Queries and visualization without code
  • Easy installation
  • Real-time collaboration
  • Simple integrations

The inconvenients

  • More expensive than some tools
  • Customer support frustration

Apache Hadoop

Image of the Apache Hadoop logo.

Apache Hadoop is an open source data analysis software framework available for download since 2006, and it is one of the most popular tools among data analysts. The Hadoop storage component is the Hadoop Distributed File System (HDFS), which “provides high-speed access to application data,” and its processing component is Hadoop MapReduce, “a YARN-based system for parallel processing of large datasets ”.

Hadoop was designed with the premise that hardware failures are inevitable. The framework must therefore be ready to detect and resolve these issues at the application layer level. Although Hadoop offers exceptional high availability and parallel processing capabilities, it does not support real-time processing or in-memory calculations, two critical elements for efficient data analysis.


  • Java based
  • Easy to install
  • Strong parallel processing capabilities
  • High availability
  • Strong data protection

The inconvenients

  • Advanced training required
  • Requires significant processing power
  • Complex integrations
  • No real-time processing capability
  • No calculations in memory

Apache Spark

Apache Spark is another open source utility that works similarly to Hadoop with one key difference: instead of a file system, Spark caches and processes data using the underlying hardware’s RAM. This means that Spark is able to fill the real-time processing and in-memory compute gaps that Hadoop cannot, thereby making the Spark ecosystem more effective and efficient.

In fact, Spark’s data processing capabilities for small workloads are 100 times faster than Hadoop’s MapReduce. Additionally, Spark is able to work with a wider range of datastores and HDFS, making Spark a much more versatile and flexible solution. However, Hadoop remains a more cost effective option because it does not require large amounts of RAM.


  • Open source
  • High level operators
  • More flexibility and versatility than Hadoop
  • Supports real-time and batch processing, as well as in-memory calculations

The inconvenients

  • Advanced training required
  • Documentation not always useful
  • Additional security measures required

Zoho Analytics

Zoho Analytics logo image.

For small businesses, Zoho Analytics is an affordable and accessible big data analytics solution. It has an intuitive user interface that makes it easy to build rich dashboards and quickly find the most important information.

Although it is a strong stand-alone product, one of the advantages of using Zoho Analytics is that it can be directly integrated with the larger suite of Zoho business tools, including CRM applications, HR and marketing automation. What Zoho Analytics lacks in terms of advanced features, it makes up for in ease of use and price.


  • Relatively affordable
  • Integrates with other Zoho products
  • Relatively easy to use

The inconvenients

  • Dull report features
  • Less suitable for large organizations with advanced needs


MongoDB logo image.

MongoDB is a NoSQL database that uses document-based collections rather than SQL-based rows and columns. It was built by developers, so it makes app development a lot faster and more intuitive.

It’s also an ideal choice for data-driven organizations embarking on their digital transformation process or those looking to start small and scale as the business grows. MongoDB’s processing speeds have been a hindrance for some customers, so this potential limitation is something large enterprises should be aware of.


  • Good at load balancing
  • Serverless option
  • Relatively easy to use

The inconvenients

  • Slower processing speeds
  • Complex integrations
  • Slow customer support


Image of the Xplenty logo.

Xplenty is a cloud-based data integration platform that helps streamline data from a variety of structured, unstructured and semi-structured sources. It is a low-code ETL platform, which means it cleans, enriches, and transforms every dataset before sending it to a data warehouse, all with minimal code needed to complete. the process. It is often used with other tools like Tableau.

Xplenty integrates with a variety of applications, including Zendesk, Oracle, and Salesforce. Once connected, Xplenty automates the data integration process from all the tools you use to run your business and creates a single source of truth for all data-driven information.


  • Simple UX
  • Easy to use for non-technicians
  • Helpful customer support

The inconvenients

  • Troubleshooting and debugging can be difficult
  • Difficult to use for complicated pipelines

How to choose the right big data tool

The right big data tool for your business will meet your unique needs. Consider the apps you use, the types of data your business needs to manage, and what information you need to understand from your data when it comes to making decisions. Next, look for a tool (or a combination of tools) that will help you meet your analysis needs without breaking the bank.