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 3 | Best Big Data Analytics Software: User Reviews from September 2026

Keboola

Keboola is a finance data and AI platform for companies that need trusted financial reporting, planning, analysis, and close processes across complex systems. Built for CFOs, FP&A teams, Controllers, finance operations, and data teams, Keboola helps organizations turn fragmented data from ERPs, accounting systems, spreadsheets, CRM, warehouses, and operational tools into one governed foundation for financial intelligence. Many finance teams rely on disconnected systems, inconsistent charts of accounts, manual reconciliations, and spreadsheet-based reporting. This makes it difficult to close quickly, produce reliable forecasts, answer board questions, support acquisitions, or use AI safely with financial data. Keboola solves this upstream data problem by connecting, standardizing, transforming, and governing financial data before it reaches FP&A, CPM, BI, reporting, or AI tools. Keboola is especially valuable for multi-entity companies, private-equity-backed businesses, holding groups, financial services firms, franchises, and organizations growing through M&A. These companies often operate across multiple ERPs, entities, countries, currencies, and reporting definitions. Keboola helps standardize chart-of-accounts logic, automate financial data pipelines, reconcile data across systems, and create trusted business definitions that teams can use consistently. Unlike traditional FP&A, CPM, EPM, budgeting, forecasting, or financial reporting tools that assume clean data already exists, Keboola focuses on the governed data foundation underneath. The platform gives teams transparent, auditable logic for financial transformations, source-to-output lineage, reusable business rules, and a semantic layer that helps ensure reports, dashboards, forecasts, and AI outputs are based on trusted company data. Keboola works alongside existing tools such as Snowflake, BigQuery, Databricks, Microsoft Fabric, Power BI, Tableau, Looker, Anaplan, Pigment, DataRails, Workiva, and other finance and analytics platforms. Rather than forcing a rip-and-replace project, Keboola strengthens the data layer beneath these systems so finance teams can plan, report, reconcile, and analyze with greater confidence. For AI adoption, Keboola provides the governed data environment that makes AI practical for business-critical financial workflows. Teams can connect AI agents and assistants to approved metrics, trusted pipelines, financial definitions, and auditable data products, reducing the risk of confident but untraceable answers. With Keboola, organizations can improve financial analysis, accelerate reporting, support financial close, automate reconciliation workflows, enable more reliable FP&A, and create a trusted foundation for AI-powered finance.

Average Rating: 4.6/5.0

Total Reviews: 138

How Do G2 Users Rate Keboola?

Has the product been a good partner in doing business?: 9.0/10 (Category avg: 8.9/10)
Multi-Source Analysis: 8.3/10 (Category avg: 8.5/10)
Real-Time Analytics: 5.0/10 (Category avg: 8.5/10)
Data Workflow: 9.2/10 (Category avg: 8.5/10)

Who Is the Company Behind Keboola?

Seller: Keboola
Year Founded: 2008
HQ Location: Prague
Twitter: @keboola
2,004 Twitter followers
LinkedIn® Page: www.linkedin.com
97 employees on LinkedIn®

Who Uses This Product?

Who Uses This: Data Analyst, Data Engineer
Top Industries: Information Technology and Services, Marketing and Advertising
Company Size: 63% Medium, 22% Small

What Do G2 Reviewers Say About Keboola?

AI-generated summary from verified user reviews

Pros

Users appreciate the ease of use of Keboola, enjoying its intuitive interface and efficient data connection setup.
Users appreciate Keboola's wide variety of connectors, intuitive interface, and powerful automation features for data integration.
Users value Keboola's wide range of data connections and intuitive interface, enhancing their data management experience.
Users value the seamless integrations in Keboola, enhancing data workflows and orchestration with minimal limitations.
Users praise Keboola's responsive and helpful customer support, enhancing their overall experience with the platform.

Cons

Users struggle with the steep learning curve of Keboola, finding the setup of services and training insufficient.
Users find Keboola's interface complex and confusing, especially when managing large projects and navigating components.
Users find a steep learning curve with Keboola, especially due to fragmented setups and challenging training materials.
Users find data type management complicated, needing more guidance on transformation errors and advanced monitoring capabilities.
Users find the UX improvement necessary due to a complicated interface and messy data management with flows.

What Are Recent G2 Reviews of Keboola?

"Keboola’s Intuitive UI Makes Big-Data Pipelines and Transformations Effortless"

Rating: 4.5/5.0 stars

— Verified User in Logistics and Supply Chain

Best Big Data Analytics Software - Page 3

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Category Stats (Sep 2026)

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