What is Big Data Analytics Software?

The huge amount of data that is accessible to businesses today has made it a near necessity for them to implement some type of analytics software to better understand and act on that data. Implementing big data analytics software has been a major initiative for companies undergoing digital transformation, as these tools offer deeper visibility into an organization's data. Companies adopt these solutions to make sense of large data sets collected from big data clusters. With the ability to visualize and understand business data, employees can make informed decisions. For example, retailers can use these tools to better understand inventory distribution across their channels and make data-driven decisions based on this data. Some big data analytics solutions may offer artificial intelligence or machine learning features, such as natural language processing, as an interface capability to further aid nontechnical users. What Types of Big Data Analytics Software Exist? Many types of big data analytics solutions share overlapping functionality, while simultaneously catering to different user personas such as data analysts and financial analysts or providing unique services. Because of the unstructured nature of big data clusters, these analytics solutions require a query language to pull the data out of the file system. Most commercial table databases allow SQL queries; however, big data analytics tools do not necessarily offer such SQL language capabilities and may require a more intricate knowledge of querying from a data scientist. As an alternative, some solutions may offer self-service features so that the average employee can assemble their own charts and graphs from big data sets. Self-service big data analytics tools Self-service big data analytics tools do not require coding knowledge, so end users with limited to no coding knowledge can take advantage of them for data needs. This enables business users like sales representatives, human resource managers, marketers, and other nondata team members to make decisions based on relevant business data. Self-service solutions often provide drag-and-drop functionality for building dashboards, prebuilt templates for querying data, and, occasionally, natural language querying for data discovery. Similar to analytics platforms, organizations use these tools to build interactive dashboards for discovering actionable insights. Embedded analytics solutions Embedded analytics solutions offer the ability to integrate proprietary analytics functionality within other business applications. Commonly, businesses embed analytics solutions in software such as CRMs, ERP, and portals (e.g., intranets or extranets). Businesses may choose an embedded product to promote user adoption; by placing the analytics inside regularly used software, companies enable employees to take advantage of available data. These solutions provide self-service functionality so average business end users can take advantage of data for improved decision making.

What are the Common Features of Big Data Analytics Software?

Big data analytics software helps companies get a better understanding of their data. The following are some core features of this software: Data connectivity: If businesses cannot connect the requisite data, then there is no use for big data analytics software. The methods for connecting data include Hadoop and Spark integration which allows for processing and distribution workflows on top of Apache Hadoop and Apache Spark, respectively. In addition, this software should allow for analyzing data that is stored in data lakes, data warehouses, and data lake houses. Data transformation: For data to be analyzed, it needs to be properly cleaned and transformed into a usable format. Big data analytics software provides features such as real-time analytics and data querying. With these features, businesses can gain a high-level view of their data in real time, allowing one to query it and better understand it. Through query languages like SQL, users can query their data and dig deeper into particular data sets and data points. Data operations: Once the data is connected (or integrated) and transformed, it can be analyzed. Firstly, it is important to establish data workflows, which can help in stringing together specific functions and data sets to automate analytics iterations. In addition, big data analytics software provides the ability to visualize data through dashboards, as well as notebooks which can be used to create visualization with predefined or scheduled queries. It is not always the case that one will access analytics via a standalone analytics platform. Therefore, some products provide embedded analytics capabilities. This allows users to access analytics inside business applications, which allows for more streamlined work since the users need not switch between applications. Other Features of Big Data Analytics Software: Governed Discovery,

What are the Benefits of Big Data Analytics Software?

Data is both common and invaluable and within that data lies insights that could impact an organization's processes and performance. There are seemingly infinite insights a business can pull from their data and numerous reasons to utilize big data analytics software. Big data analytics software helps people make decisions easier by allowing teams to gain deeper insight into their data. With increased data literacy, teams across a business, from sales to marketing to finance can become more efficient and better understand how they can improve through data-driven initiatives. With big data analytics software, businesses can ingest, integrate, and prepare big data sources. Subsequently, they can connect all company data sources into a single platform to make cross-department connections, visualize and understand company data, encourage data-driven decision making for business optimization, and discover new insights that can enhance the bottom line. Enable data-driven decision making: Businesses can use big data analytics software to fuel digital transformation by leveraging data to drive business decisions. Companies can leverage analytics and business intelligence (BI) tools to understand all aspects of the business, including hiring forecasts, which marketing campaign should be used to target certain demographics, which sales prospects to target first, supply chain optimization, and many others. Measure and understand company performance: Organizations often leverage data visualization tools to track company key performance indicators (KPIs) in real time. From there, big data analytics software can be used to determine why the business is either exceeding or falling short of those important company metrics. When stakeholders develop a keen understanding of why the business is performing the way it is, they can make adjustments and pivots; if a team is falling short of a goal, they can examine and adjust processes as needed. It is one thing to simply know the performance of sales or web traffic numbers, but it is another to dig into the reasons behind it and adapt based on what is successful and what is not. Discover new actionable insights: Analytics tools combine data from a variety of sources, including accounting software, enterprise resource planning (ERP) software, CRM software, marketing automation software, and others. Data analysts can leverage this integrated data to find correlations between different departments, and their processes and actions, to discover previously hidden insights. For example, it is possible that certain sales tactics have varying impacts on the numbers for one specific product versus another. Analysts can discover this impact by comparing the list of closed accounts from their company CRM with products shipped in their ERP system. Teams are generally siloed and use disparate software, so these insights that were traditionally more difficult to discover, are now made easier.

Page 2 | Best Big Data Analytics Software: User Reviews from October 2026

EXASOL

Exasol is the world’s most powerful Analytics Engine, purpose-built to handle the most demanding data workloads at an unmatched price / performance ratio. In-memory architecture Want to process 3 billion rows in 3 seconds, not 3 hours? Exasol manages memory cache automatically, only bringing what's needed into the database for dramatically faster access times. Automatic query tuning Enjoy optimized performance while minimizing data administration overhead. Exasol uses intelligent, proprietary algorithms to self-tune queries on the fly -- adding and removing indices automatically – so you can bring true self-service BI to your organization. User defined functions (UDF) When you need more than a SQL statement, UDF scripts allow you to program your own analysis. Take your unique machine learning and data ingest scripts written in Python, R, and Lua, and run them in our database engine. Through UDF scripts, you'll get a highly flexible interface for nearly every requirement, allowing you to bring in data quickly from wherever it lives. In addition to being the fastest, Exasol also leads in the TPC price-performance metrics, meaning everyone in your organization can take advantage of unrivaled in-memory speed at a low price. And, unlike our competitors, Exasol allows you to choose the deployment destination. Deploy in the cloud, on-premises, or hybrid to meet your organization's unique needs and preferred vendors.

Average Rating: 4.7/5.0

Total Reviews: 23

How Do G2 Users Rate EXASOL?

Has the product been a good partner in doing business?: 9.7/10 (Category avg: 8.9/10)
Multi-Source Analysis: 9.2/10 (Category avg: 8.6/10)
Real-Time Analytics: 9.6/10 (Category avg: 8.6/10)
Data Workflow: 6.7/10 (Category avg: 8.6/10)

Who Is the Company Behind EXASOL?

Seller: EXASOL
Year Founded: 2000
HQ Location: Nurnberg, Bayern
LinkedIn® Page: www.linkedin.com
223 employees on LinkedIn®

Who Uses This Product?

Company Size: 39% Large, 32% Medium

What Do G2 Reviewers Say About EXASOL?

AI-generated summary from verified user reviews

Pros

Users highlight the unmatched query performance of EXASOL, enabling incredibly fast results for large data sets.
Users highlight the unparalleled query performance of EXASOL, achieving rapid results even with massive data sets.
Users value the unparalleled query performance of EXASOL, enhancing efficiency for analytical workloads with speed and reliability.
Users find EXASOL to be cost-effective, appreciating its efficiency and minimal administrative requirements.
Users appreciate the fast and competent customer support from EXASOL, enhancing their overall experience with the product.

Cons

Users experience complexity in query optimization, which can hinder performance despite available tricks for improvement.
Users report challenges with the lack of a robust debugger in EXASOL, making Python code development difficult.
Users find the difficult setup of EXASOL requires extensive configuration and DBA involvement for upgrades.
Users find the limited visualization capabilities of EXASOL hinder their ability to analyze data effectively.
Users experience performance issues with Exasol's optimizer, impacting complex query execution but workaround solutions are available.

What Are Recent G2 Reviews of EXASOL?

"Light-Speed Performance and Superior Support"

Rating: 5.0/5.0 stars

— Björn B.

Best Big Data Analytics Software - Page 2

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Category Stats (Oct 2026)

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G2 Grid® for Big Data Analytics Software

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