# Best Big Data Analytics Software - Page 3 *By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)* Big data analytics software provides insights into large, complex data sets collected from big data clusters, helping business users understand data trends, patterns, and anomalies through visualizations, reports, and dashboards, often requiring query languages to extract data from unstructured file systems. ### Core Capabilities of Big Data Analytics Software To qualify for inclusion in the Big Data Analytics category, a product must: - Consume data, query file systems, and connect directly to big data clusters - Allow users to prepare complex big data sets into helpful and understandable data visualizations - Create business-applicable reports, visualizations, and dashboards based on discoveries inside the data sets ### Common Use Cases for Big Data Analytics Software Data engineers, analysts, and business intelligence teams use big data analytics software to extract value from large-scale, unstructured data environments. Common use cases include: - Querying and analyzing large Hadoop or distributed data clusters to surface business insights - Detecting patterns and anomalies in high-volume data sets for operational or strategic decision-making - Building self-service charts and dashboards for non-technical stakeholders from big data sources ### How Big Data Analytics Software Differs from Other Tools Big data analytics software is solely focused on manipulating complex, large-scale data clusters into understandable visualizations, differentiating it from [analytics platforms](https://www.g2.com/categories/analytics-platforms), which support a wide range of data sources and connectors beyond big data. The two categories are mutually exclusive. Big data analytics tools are commonly used at companies running Hadoop in conjunction with [big data processing and distribution software](https://www.g2.com/categories/big-data-processing-and-distribution) and integrate with [data warehouse software](https://www.g2.com/categories/data-warehouse) as the central hub for integrated data. Some solutions also leverage [machine learning](https://www.g2.com/categories/machine-learning) and [natural language processing](https://www.g2.com/categories/natural-language-processing-nlp) to enable natural language querying. ### Insights from G2 on Big Data Analytics Software Based on category trends on G2, query flexibility and scalability for large data sets stand out as standout capabilities. Faster insight generation from complex data environments stand out as the primary benefit of adoption. ## Top Big Data Analytics Software at a Glance | # | Product | Rating | Best For | What Users Say | |---|---------|--------|----------|----------------| | 1 | [Databricks](https://www.g2.com/products/databricks/reviews) | 4.6/5.0 (1,322 reviews) | Unified lakehouse ETL, analytics, and ML pipelines | "[Helpful for Managing and Analyzing Operational Data](https://www.g2.com/survey_responses/databricks-review-13090803)" | | 2 | [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) | 4.5/5.0 (1,144 reviews) | Serverless SQL analytics on petabyte-scale datasets | "[Easy-to-Use Cloud Tool with Shareable, Saved Queries](https://www.g2.com/survey_responses/google-cloud-bigquery-review-12958418)" | | 3 | [Snowflake](https://www.g2.com/products/snowflake/reviews) | 4.5/5.0 (708 reviews) | Elastic multi-workload analytics with zero-infrastructure overhead | "[Elastic Scaling and Fast Analytics with Snowflake](https://www.g2.com/survey_responses/snowflake-review-13129003)" | | 4 | [IBM watsonx.data](https://www.g2.com/products/ibm-watsonx-data/reviews) | 4.4/5.0 (159 reviews) | Federated lakehouse querying across hybrid data environments | "[Powerful Query Performance and Governance, But a Steep Onboarding Learning Curve](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12836202)" | | 5 | [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) | 4.5/5.0 (212 reviews) | Unified Spark-native lakehouse ETL and ML | "[A powerhouse for scaling ML workflows, but keep a close eye on your billing.](https://www.g2.com/survey_responses/azure-databricks-review-12976834)" | | 6 | [Alteryx](https://www.g2.com/products/alteryx/reviews) | 4.6/5.0 (847 reviews) | No-code ETL and multi-source data blending | "[Intuitive Drag-and-Drop Analytics That Speeds Up Data Prep and Insights](https://www.g2.com/survey_responses/alteryx-review-12983224)" | | 7 | [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) | 4.8/5.0 (265 reviews) | Sub-second OLAP querying on cloud-scale datasets | "[Kyvos Semantic Layer Boosts AI Accuracy with Business-Ready Data](https://www.g2.com/survey_responses/kyvos-semantic-layer-review-13142366)" | | 8 | [Azure Synapse Analytics](https://www.g2.com/products/azure-synapse-analytics/reviews) | 4.4/5.0 (37 reviews) | Unified ETL and big data warehousing on Azure | "[Unified Data Warehousing and Big Data in One Powerful Platform](https://www.g2.com/survey_responses/azure-synapse-analytics-review-12435130)" | | 9 | [Dataiku](https://www.g2.com/products/dataiku/reviews) | 4.4/5.0 (213 reviews) | End-to-end ML pipelines with low-code collaboration | "[Unified, Low-Code Platform That Boosts End-to-End Data & AI Productivity](https://www.g2.com/survey_responses/dataiku-review-13125252)" | | 10 | [Splunk Enterprise](https://www.g2.com/products/splunk-enterprise/reviews) | 4.3/5.0 (415 reviews) | Cross-source log correlation and security analytics | "[Excellent Enterprise Observability and Log Management Solution for Hybrid Cloud Infrastructure](https://www.g2.com/survey_responses/splunk-enterprise-review-12045230)" | ## G2 Grid® for Big Data Analytics Software ![G2 Grid® for Big Data Analytics Software plotting products by satisfaction and market presence](https://www.g2.com/categories/big-data-analytics/grids.png?focus%5B%5D=10470&focus%5B%5D=6073&focus%5B%5D=10938&focus%5B%5D=1308796&focus%5B%5D=67962&focus%5B%5D=989&focus%5B%5D=27024&focus%5B%5D=52199) Highlighted products: Databricks, Google Cloud BigQuery, Snowflake, IBM watsonx.data, Azure Databricks, Alteryx, Kyvos Semantic Layer, and Azure Synapse Analytics. Underlying data: [Grid® JSON](https://www.g2.com/categories/big-data-analytics/grids.json?focus%5B%5D=databricks&focus%5B%5D=google-cloud-bigquery&focus%5B%5D=snowflake&focus%5B%5D=ibm-watsonx-data&focus%5B%5D=azure-databricks&focus%5B%5D=alteryx&focus%5B%5D=kyvos-semantic-layer&focus%5B%5D=azure-synapse-analytics) ## How Many Big Data Analytics Software Products Does G2 Track? **Total Products under this Category:** 110 ### Category Stats (Jul 2026) - **Average Rating**: 4.45/5 The average rating of products in this category, based on all submitted ratings - **Top Trending Product**: Megaladata (+0.37%) - Among all products in this category, Megaladata recorded the largest rating increase compared to last month *Last updated: July 22, 2026* ## How Does G2 Rank Big Data Analytics Software Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 8,400+ Authentic Reviews - 110+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. --- **Sponsored** ### Starburst Starburst is the data platform for analytics, applications, and AI, unifying data across clouds and on-premises to accelerate AI innovation. Organizations—from startups to Fortune 500 enterprises in 60+ countries—rely on Starburst for fast data access, seamless collaboration, and enterprise-grade governance on an open hybrid data lakehouse. Wherever data lives, Starburst unlocks its full potential, powering data and AI from development to deployment. By future-proofing data architecture, Starburst helps businesses fuel innovation with AI. Learn more at starburst.ai [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=1041&secure%5Bchosen_at%5D=2026-07-23T07%3A00%3A30Z&secure%5Bdisplayable_resource_id%5D=1041&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=1041&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=116699&secure%5Bresource_id%5D=1041&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=llm_category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fbig-data-analytics%3Fopen_modal_url%3D%252Fproducts%252Fchaossearch-data-lake-platform%252Fwishlists%253Fhost_path%253D%25252Fcategories%25252Fbig-data-analytics%2526source%253Dcategory&secure%5Btoken%5D=03d939644e80d7c3d5013d87dbb6c0bb0677649df0e13973102099dfca4f8a63&secure%5Burl%5D=https%3A%2F%2Fwww.starburst.io%2Ffree-trial%2F&secure%5Burl_type%5D=free_trial) --- ## Big Data Analytics Software Features & Capabilities ### What are the Best Big Data Analytics Software with Embedded Analytics? Allows big data tool to run and record data within external applications. **Top-rated Big Data Analytics Software for Embedded Analytics:** - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) - [Alteryx](https://www.g2.com/products/alteryx/reviews) [Explore Big Data Analytics Software with Embedded Analytics](https://www.g2.com/categories/big-data-analytics/f/embedded-analytics) ### What are the Best Big Data Analytics Software with Spark Integration? Aligns processing and distribution workflows on top of Apache Spark **Top-rated Big Data Analytics Software for Spark Integration:** - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) - [Alteryx](https://www.g2.com/products/alteryx/reviews) [Explore Big Data Analytics Software with Spark Integration](https://www.g2.com/categories/big-data-analytics/f/spark-integration) ### What are the Best Big Data Analytics Software with Governed Discovery? Isolates certain datasets and facilitates management of data access. **Top-rated Big Data Analytics Software for Governed Discovery:** - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) - [Alteryx](https://www.g2.com/products/alteryx/reviews) [Explore Big Data Analytics Software with Governed Discovery](https://www.g2.com/categories/big-data-analytics/f/governed-discovery) ### What are the Best Big Data Analytics Software with Notebooks? Use notebooks for tasks such as creating dashboards with predefined, scheduled queries and visualizations **Top-rated Big Data Analytics Software for Notebooks:** - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) - [Alteryx](https://www.g2.com/products/alteryx/reviews) [Explore Big Data Analytics Software with Notebooks](https://www.g2.com/categories/big-data-analytics/f/notebooks) ### What are the Best Big Data Analytics Software with Data Lake? Facilitates the dissemination of collected big data throughout parallel computing clusters. **Top-rated Big Data Analytics Software for Data Lake:** - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) - [Dataiku](https://www.g2.com/products/dataiku/reviews) [Explore Big Data Analytics Software with Data Lake](https://www.g2.com/categories/big-data-analytics/f/data-lake) ## What Are the Top-Rated Big Data Analytics Software Products in 2026? ### 1. [Axiom](https://www.g2.com/products/axiom-inc-axiom/reviews) Store and query unlimited machine data with Axiom’s Cloud Native data insight platform. **Average Rating:** 2.5/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Axiom?** - **Multi-Source Analysis:** 5.0/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 5.0/10 (Category avg: 8.5/10) - **Data Workflow:** 5.0/10 (Category avg: 8.5/10) **Who Is the Company Behind Axiom?** - **Seller:** [Axiom](https://www.g2.com/sellers/axiom-fc226ec4-02fa-4efe-a8ac-3e1c49b513a9) - **Year Founded:** 2020 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://linkedin.com/company/axiomhq (61 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 2. [ChaosSearch Data Lake Platform](https://www.g2.com/products/chaossearch-data-lake-platform/reviews) ChaosSearch helps modern organizations Know Better™ by activating the data lake for analytics. The ChaosSearch Data Lake Platform indexes customers’ cloud data, rendering it fully searchable and enabling analytics at scale with massive reductions of time, cost and complexity. ChaosSearch was purpose-built for cost-effective, highly scalable analytics encompassing full text search, SQL and machine learning capabilities in one unified offering. The patented ChaosSearch technology instantly transforms your cloud object storage (Amazon S3, Google Cloud Storage) into a hot, analytical data lake. Whereas other solutions require dedicated data scientists, engineers and DBAs to manage complex data pipelines and massive ETL data movement between siloed databases, ChaosSearch indexes all data as-is, while auto-detecting native schemas and optimizing for both data size and performance. ChaosSearch reduces data prep time from weeks to minutes through virtual views, presented to data consumers via open APIs (Search, SQL, ML) with full governance and role-based access controls. Users continue working in the tools they know and trust, such as Kibana, Looker, and Tableau. With its revolutionary approach delivered in a fully managed service, ChaosSearch overcomes the cost and complexity of competitive solutions, delivering unlimited scalability, industry-leading resiliency, and massive time and cost savings. Based on these capabilities, ChaosSearch is an ideal replacement for the commonly deployed Elasticsearch (ELK) stack. With ChaosSearch, customers can perform scalable log analytics on AWS S3 or GCS, using the familiar Elasticsearch API for queries, and Kibana for log analytics and visualizations, while reducing costs and improving analytical capabilities. **Average Rating:** 4.8/5.0 **Total Reviews:** 2 **How Do G2 Users Rate ChaosSearch Data Lake Platform?** - **Multi-Source Analysis:** 9.2/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 9.2/10 (Category avg: 8.5/10) - **Data Workflow:** 9.2/10 (Category avg: 8.5/10) **Who Is the Company Behind ChaosSearch Data Lake Platform?** - **Seller:** [ChaosSearch](https://www.g2.com/sellers/chaossearch) - **Year Founded:** 2017 - **HQ Location:** Boston, US - **Twitter:** @CHAOSSEARCH (751 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/chaossearch (26 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Small-Business, 50% Enterprise #### What Are Recent G2 Reviews of ChaosSearch Data Lake Platform? **"[AWS S3 Index Lifecycle Management & Metric Evaluation with ChaosSearch Data Lake Platform](https://www.g2.com/survey_responses/chaossearch-data-lake-platform-review-5440650)"** **Rating:** 4.5/5.0 stars *— Ravi C.* [Read full review](https://www.g2.com/survey_responses/chaossearch-data-lake-platform-review-5440650) --- **"[Most Impactful Data Decision We've Made](https://www.g2.com/survey_responses/chaossearch-data-lake-platform-review-4115563)"** **Rating:** 5.0/5.0 stars *— Jimmy M.* [Read full review](https://www.g2.com/survey_responses/chaossearch-data-lake-platform-review-4115563) --- #### What Are G2 Users Discussing About ChaosSearch Data Lake Platform? - [What is ChaosSearch Data Lake Platform used for?](https://www.g2.com/discussions/what-is-chaossearch-data-lake-platform-used-for) ### 3. [ETHoS.](https://www.g2.com/products/standpoint-software-llc-ethos/reviews) ETHoS™ is an AI-driven precision care platform developed by Standpoint Software, designed to enhance the performance of physician-led care teams in perioperative settings. By integrating machine learning with real-time data from hospital systems, ETHoS™ provides deep insights into clinical quality, cost, and team effectiveness, enabling healthcare professionals to make informed decisions that improve patient outcomes and reduce operational expenses. Key Features and Functionality: - Augmented Intelligence: Utilizes machine learning to analyze data from hospital sources, identifying opportunities to optimize team connectivity and patient care pathways. - Real-Time Communication: Offers a secure, social-like mobile interface that facilitates efficient coordination and collaboration among care team members during patient care episodes. - Performance Tracking: Monitors patient cases in real-time, providing actionable insights to improve care quality and cost-effectiveness. - Data Integration: Seamlessly integrates with existing hospital clinical, supply chain, and financial systems to provide comprehensive analytics. Primary Value and Problem Solved: ETHoS™ addresses critical challenges in perioperative care, including communication errors, unwarranted clinical variations, and coordination inefficiencies. By providing real-time, data-driven insights and facilitating effective team collaboration, ETHoS™ empowers physicians and care teams to enhance patient outcomes, reduce operational and clinical expenses, and improve overall care performance. **Average Rating:** 4.0/5.0 **Total Reviews:** 1 **Who Is the Company Behind ETHoS.?** - **Seller:** [Standpoint Software](https://www.g2.com/sellers/standpoint-software) - **Year Founded:** 2019 - **HQ Location:** Seattle, US - **LinkedIn® Page:** https://www.linkedin.com/company/standpoint-software-llc/ (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are Recent G2 Reviews of ETHoS.? **"[Ethos works](https://www.g2.com/survey_responses/ethos-review-5200004)"** **Rating:** 4.0/5.0 stars *— Jeremy S.* [Read full review](https://www.g2.com/survey_responses/ethos-review-5200004) --- #### What Are G2 Users Discussing About ETHoS.? - [What is ETHoS. used for?](https://www.g2.com/discussions/what-is-ethos-used-for) ### 4. [INDEXIMA Data Hub](https://www.g2.com/products/indexima-data-hub/reviews) A DATA HUB that provides the missing “layer” in an ANALYTICS Data-Driven landscape to IT teams using Data Analytics solutions such as Tableau®, Excel®, Talend, Qlik®, Power BI®, DATAIKU® or MicroStrategy®). The DATA HUB makes it possible to query all Big Data directly at source, tens of billions of lines in just a few milliseconds. **Average Rating:** 3.5/5.0 **Total Reviews:** 1 **How Do G2 Users Rate INDEXIMA Data Hub?** - **Multi-Source Analysis:** 10.0/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 10.0/10 (Category avg: 8.5/10) - **Data Workflow:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind INDEXIMA Data Hub?** - **Seller:** [INDEXIMA](https://www.g2.com/sellers/indexima) - **Year Founded:** 2016 - **HQ Location:** Paris, FR - **Twitter:** @indexima (292 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/13297928 (7 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are G2 Users Discussing About INDEXIMA Data Hub? - [What is INDEXIMA Data Hub used for?](https://www.g2.com/discussions/what-is-indexima-data-hub-used-for) ### 5. [Kognitio](https://www.g2.com/products/kognitio-kognitio/reviews) Kognitio is a mature SQL engine for your Hadoop cluster or your data warehouse. It allows businesses to gain ROI from big data projects by providing interactive BI on big data for business users who can continue to use their preferred BI tools, like Tableau or Power BI. Kognitio is completely free to use on Hadoop with no limitations on time, scale or functionality. It runs as a YARN application directly on the Hadoop cluster. Kognitio offers a range of paid support options for clients using Kognitio in a production environment. Kognitio is also available to use on standalone compute clusters. **Average Rating:** 4.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Kognitio?** - **Multi-Source Analysis:** 5.0/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 5.0/10 (Category avg: 8.5/10) - **Data Workflow:** 3.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Kognitio?** - **Seller:** [Kognitio](https://www.g2.com/sellers/kognitio) - **Year Founded:** 1987 - **HQ Location:** N/A - **Twitter:** @kognitio (2,291 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are Recent G2 Reviews of Kognitio? **"[Cloud data analysis](https://www.g2.com/survey_responses/kognitio-review-4665874)"** **Rating:** 4.0/5.0 stars *— Fernando F.* [Read full review](https://www.g2.com/survey_responses/kognitio-review-4665874) --- ### 6. [Kpow for Apache Kafka®](https://www.g2.com/products/kpow-for-apache-kafka/reviews) Kpow is a sophisticated enterprise Kafka management tool designed to enhance the experience of engineering teams by providing a comprehensive solution for managing, monitoring, exploring, and securing Kafka environments. This JVM-based web application serves as an all-in-one console, empowering Kafka engineers with the capabilities they need to streamline their operations and improve productivity. Targeted primarily at engineering teams working with Kafka, Kpow addresses the complexities of managing multiple Kafka clusters, schema registries, and connection installations. With Kpow, users can efficiently monitor and control their Kafka resources from a single interface, simplifying the management process and reducing the time spent on routine tasks. The tool is particularly beneficial for organizations that rely heavily on Kafka for data streaming and processing, as it provides essential functionalities that enhance observability and operational efficiency. One of the standout features of Kpow is its real-time monitoring and visualization capabilities. Users can quickly identify unbalanced brokers and gain insights into how data is distributed across their Kafka Streams topologies. This level of visibility is crucial for diagnosing production issues and optimizing performance. Kpow's advanced search functionalities, including Data Inspect, Streaming Search, and kREPL, enable users to search through vast amounts of messages at remarkable speeds, allowing for rapid troubleshooting and data analysis. Kpow also prioritizes security and access control, making it suitable for enterprise environments. It integrates seamlessly with standard authentication providers and offers role-based access controls, ensuring that user actions can be finely tuned to meet organizational security requirements. Additional security features, such as data masking and audit logs, further enhance the tool's capability to operate in sensitive environments, including air-gapped installations. Installation of Kpow is straightforward, requiring only a single Docker container or JAR file, which operates efficiently with minimal resource requirements of 1GB memory and 1 CPU for production use. This ease of deployment, combined with its powerful features, positions Kpow as a valuable asset for organizations looking to maximize their Kafka infrastructure while maintaining robust security and operational control. **Average Rating:** 4.9/5.0 **Total Reviews:** 4 **How Do G2 Users Rate Kpow for Apache Kafka®?** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 8.9/10) **Who Is the Company Behind Kpow for Apache Kafka®?** - **Seller:** [Factor House](https://www.g2.com/sellers/factor-house) - **Year Founded:** 2019 - **HQ Location:** Melbourne, AU - **Twitter:** @factorhousehq (125 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/factorhouse/ (15 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Mid-Market, 25% Enterprise #### What Are Kpow for Apache Kafka®'s Pros and Cons? **Pros:** - Affordability (1 reviews) - Setup Ease (1 reviews) - User Interface (1 reviews) **Cons:** - Data Management Issues (1 reviews) ### What Do G2 Reviewers Say About Kpow for Apache Kafka®? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **affordable pricing** of Kpow for Apache Kafka, highlighting its value and user-friendly experience. - Users value the **easy setup** of Kpow for Apache Kafka, appreciating its user-friendly interface and multi-schema support. - Users appreciate the **easy setup and clean UI** of Kpow for Apache Kafka, enhancing their overall experience. **Cons:** - Users face **data management issues** in Kpow for Apache Kafka due to null fields when deserializers are not specified. #### What Are Recent G2 Reviews of Kpow for Apache Kafka®? **"[Best choice for startup and middle size companies](https://www.g2.com/survey_responses/kpow-for-apache-kafka-review-7054047)"** **Rating:** 5.0/5.0 stars *— Antoine B.* [Read full review](https://www.g2.com/survey_responses/kpow-for-apache-kafka-review-7054047) --- **"[The best choice for managing our Apache Kafka environments](https://www.g2.com/survey_responses/kpow-for-apache-kafka-review-8402275)"** **Rating:** 4.5/5.0 stars *— Sidnei D.* [Read full review](https://www.g2.com/survey_responses/kpow-for-apache-kafka-review-8402275) --- #### What Are G2 Users Discussing About Kpow for Apache Kafka®? - [What is kPow for Apache Kafka used for?](https://www.g2.com/discussions/what-is-kpow-for-apache-kafka-used-for) ### 7. [Mindtree Data Analytics](https://www.g2.com/products/mindtree-data-analytics/reviews) Mindtree Data Analytics is an adoptable data analytics platform with industry-specific machine learning algorithms. **Average Rating:** 4.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Mindtree Data Analytics?** - **Has the product been a good partner in doing business?:** 5.0/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 6.7/10 (Category avg: 8.5/10) - **Data Workflow:** 6.7/10 (Category avg: 8.5/10) **Who Is the Company Behind Mindtree Data Analytics?** - **Seller:** [Mindtree Ltd.](https://www.g2.com/sellers/mindtree-ltd) - **Year Founded:** 1999 - **HQ Location:** Bangalore, India - **Twitter:** @Mindtree_Ltd (78,012 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/mindtreeltd/ (19,644 employees on LinkedIn®) - **Ownership:** BSE: 532819 **Who Uses This Product?** - **Company Size:** 100% Mid-Market #### What Are Recent G2 Reviews of Mindtree Data Analytics? **"[Skills](https://www.g2.com/survey_responses/mindtree-data-analytics-review-7103039)"** **Rating:** 4.0/5.0 stars *— Verified User in Alternative Dispute Resolution* [Read full review](https://www.g2.com/survey_responses/mindtree-data-analytics-review-7103039) --- ### 8. [Platfora](https://www.g2.com/products/platfora-2026-03-09/reviews) Platfora is a textile design commerce platform for studios, artists and print agents. Featuring branded storefronts, interactive buyer presentation, commission tracking, agent network access, and buyer analytics — all from one place. Built specifically for the print industry. No technical knowledge required. **Average Rating:** 4.5/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Platfora?** - **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.9/10) **Who Is the Company Behind Platfora?** - **Seller:** [Platfora](https://www.g2.com/sellers/platfora-f209754a-8f06-4d09-98a6-0b94108ef353) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/platfora-co/ (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are Recent G2 Reviews of Platfora? **"[Intuitive, Professional Platform for Showcasing Designs Globally](https://www.g2.com/survey_responses/platfora-review-12473237)"** **Rating:** 4.5/5.0 stars *— Barbara d.* [Read full review](https://www.g2.com/survey_responses/platfora-review-12473237) --- ### 9. [Rayven](https://www.g2.com/products/rayven/reviews) What is Rayven? Rayven is an operational software platform that delivers an AI data fabric - connecting every system, data source, and data stream across a business into a single managed environment, then letting teams build custom apps, AI agents, workflow automations, dashboards, and MCP servers for direct AI model connectivity on top. It is the platform for organisations that need to act on operational data in real-time, deploy AI that actually works in production + build software that fits the way their business operates - without replacing existing systems or waiting 18 months for results. The Problem Rayven Solves Most organisations already have the systems and data they need. The challenge is fragmentation. ERP systems, SCADA platforms, IoT devices, databases, cloud tools, and files all generate valuable data - but it sits in silos, impossible to act on in real-time. The result: manual reporting, disconnected workflows, and AI projects that fail before reaching production. Industry research shows 95% of AI projects never ship - most because the underlying data layer is not clean, connected, or ready. Rayven builds that foundation first, then activates it. The Rayven Platform Rayven operates across five unified layers, delivered as a single managed environment: - Integration: More than 600 pre-built connectors pull data from IT, OT, IoT, files, APIs, databases, and data streams - bidirectionally, in real-time. Connects industrial protocols (OPC UA, Modbus, MQTT, BACnet) alongside cloud platforms, business systems, and proprietary tools. - Data: All connected data lands in a single managed platform - structured, governed + AI-ready. Real-time processing, ETL pipelines, data lakes, and AI model training handled in one place. - Execution: Automation rules, predictive models + agentic AI run directly on live operational data. Rules-based logic, machine learning, and goal-seeking autonomous agents all operate in one execution environment. - Presentation: Custom apps, dashboards, portals, conversational interfaces, and mobile applications deployed from the same platform - built for specific workflows, not generic reporting. - Security, Governance + Hosting: Role-based access control, data lineage, audit trails, AES-256 encryption, data residency controls, and enterprise-grade infrastructure - included as standard. AI Capabilities Rayven includes ten native AI capabilities built directly into the platform: 1. Custom AI agents (goal-seeking, action-taking) 2. Predictive analytics and machine learning 3. Conversational analytics 4. Real-time and continuous model training 5. AI-led workflow automation 6. Multimodal processing (documents, video, images, audio) 7. Anomaly and risk detection 8. Forecasting and optimisation 9. Vision and edge AI inference 10. Generative operational summaries MCP server support enables direct connectivity for AI models including Claude, GPT, and others. What Gets Built Rayven customers build and deploy: - Custom operational apps and field applications. - AI agents that monitor conditions, detect anomalies + take corrective action autonomously. - Predictive maintenance and performance models running on live plant data. - Real-time dashboards and executive reporting tools. - Workflow automations spanning IT and OT systems. - Customer and partner portals. - Data pipelines and integration layers. - White-label software products delivered under partner brands. Key Differentiators vs. Point solutions (Zapier, MuleSoft, Power BI, DataRobot): point solutions do one thing well but force teams to stitch together five separate tools to cover integration, data, AI, presentation, and governance. Rayven replaces the stack. vs. Traditional enterprise platforms (SAP, Oracle, Palantir): enterprise platforms take 12-18 months and seven figures to implement. Rayven deploys in two to 12 weeks at fixed scope and fixed price. vs. Low-code app builders (Mendix, OutSystems): app builders handle the presentation layer but do not solve the underlying data and integration problem. Rayven covers the full stack. Technology Compatibility Rayven is fully technology-agnostic and works alongside existing systems: - Cloud platforms: Microsoft Azure, Google Cloud + AWS - Business systems: SAP, Salesforce, Oracle, and Microsoft 365 - OT platforms: Siemens, Rockwell, Schneider Electric, and Ignition - Industrial protocols: OPC UA, Modbus, MQTT, BACnet, and EtherNet/IP - IoT devices: any device with a data output - Custom and proprietary systems via API, webhook, or direct connector Nothing needs to be replaced. Every existing investment is preserved. Who Uses Rayven Rayven serves businesses from growth-stage to large enterprise across 24+ industries globally - manufacturing, mining, construction, infrastructure, logistics, utilities, financial services, healthcare, agriculture, government, and more. Customers across Australia, Europe, North America, South America, and Africa. Named customers include Anglo American, Fulton Hogan, Glencore, Vodafone, NSW Ports, CSIRO, Collective Intelligence, Ramjack, and AngloGold Ashanti. Delivery Options - DIY: Full platform access. Internal teams build and deploy independently. - Done-For-You: Australia-based delivery team. Fixed scope, fixed price, two to 12 weeks from brief to go-live. - Hybrid: Guided delivery first, with the customer's team taking increasing ownership over time. By the Numbers - More than 600 pre-built connectors. - Ten native AI capabilities. - More than 240 deployments live globally. - Rated 5/5 across more than 140 independent reviews. - Deploys 66% faster than traditional development. - Two to 12 weeks to first working solution. - Rayven exists to close the gap: 95% of AI projects never reach production (industry average). **Average Rating:** 4.9/5.0 **Total Reviews:** 29 **Who Is the Company Behind Rayven?** - **Seller:** [Rayven](https://www.g2.com/sellers/rayven) - **Year Founded:** 2016 - **HQ Location:** Sydney, AU - **Twitter:** @RayvenIOT (56 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/rayveniot/ (33 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Retail - **Company Size:** 69% Mid-Market, 52% Small-Business #### What Are Rayven's Pros and Cons? **Pros:** - Ease of Use (61 reviews) - Features (49 reviews) - Automation (44 reviews) - Customization (42 reviews) - Data Management (36 reviews) **Cons:** - Learning Curve (32 reviews) - Difficult Learning (30 reviews) - Learning Difficulty (25 reviews) - Complex Setup (21 reviews) - Setup Complexity (19 reviews) ### What Do G2 Reviewers Say About Rayven? *AI-generated summary from verified user reviews* **Pros:** - Users commend Rayven's **ease of use** , highlighting effortless automation and streamlined integration through intuitive drag-and-drop features. - Users appreciate Rayven's **robust handling of large-scale data** and advanced AI capabilities, enhancing operational efficiency and flexibility. - Users praise Rayven for its **exceptional automation** capabilities, significantly streamlining data workflows and enhancing operational efficiency. - Users appreciate Rayven's **customization capabilities** , allowing tailored solutions that enhance integration and streamline operations efficiently. - Users appreciate Rayven's **robust data management** , enabling real-time processing and integration across complex systems with ease. **Cons:** - Users find the **learning curve steep** initially, requiring time and guidance to fully grasp all features. - Users find **difficult learning** curves with Rayven's features, requiring time and guidance for smoother onboarding experiences. - Users experience a **steep learning curve** initially, requiring time and additional resources to fully understand the platform. - Users find the **complex setup** challenging, especially with advanced features and multi-site deployments requiring careful planning. - Users note the **setup complexity** of Rayven, requiring assistance for advanced integrations and lengthy initial configurations. #### What Are Recent G2 Reviews of Rayven? **"[Seamless Automation and Predictive Maintenance with Rayven](https://www.g2.com/survey_responses/rayven-review-12211094)"** **Rating:** 5.0/5.0 stars *— Keith N.* [Read full review](https://www.g2.com/survey_responses/rayven-review-12211094) --- **"[Rayven's Low-Code Platform is the Fastest Way to Build and Scale Intelligent Apps](https://www.g2.com/survey_responses/rayven-review-11780301)"** **Rating:** 5.0/5.0 stars *— John M.* [Read full review](https://www.g2.com/survey_responses/rayven-review-11780301) --- ### 10. [Sparkflows Data Science & Analytics Platform](https://www.g2.com/products/sparkflows-data-science-analytics-platform/reviews) Build your ML/AI and data pipelines 20X faster with low-code Sparkflows studio and deploy your models in production at speeds in-line with the business needs. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Sparkflows Data Science & Analytics Platform?** - **Multi-Source Analysis:** 10.0/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 10.0/10 (Category avg: 8.5/10) - **Data Workflow:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind Sparkflows Data Science & Analytics Platform?** - **Seller:** [Sparkflows.io](https://www.g2.com/sellers/sparkflows-io) - **Year Founded:** 2016 - **HQ Location:** San Jose, US - **LinkedIn® Page:** https://www.linkedin.com/company/sparkflows (30 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market #### What Are Recent G2 Reviews of Sparkflows Data Science & Analytics Platform? **"[Accelerate leverage data science and analytics](https://www.g2.com/survey_responses/sparkflows-data-science-analytics-platform-review-8779169)"** **Rating:** 5.0/5.0 stars *— Surya Bhagavathi B.* [Read full review](https://www.g2.com/survey_responses/sparkflows-data-science-analytics-platform-review-8779169) --- ### 11. [Splunk Cloud Platform](https://www.g2.com/products/splunk-cloud-platform/reviews) Search, analyze, visualize and act on your data with the flexible, secure and cost effective data platform service. Go live in as little as two days, and with your IT backend managed by Splunk experts you can focus on acting on your data. Search any kind of data in real-time to detect and prevent issues before they happen with access to the latest streaming and machine learning capabilities. Search any kind of data in real-time to detect and prevent issues before they happen with access to the latest streaming and machine learning capabilities. **Average Rating:** 4.3/5.0 **Total Reviews:** 2 **Who Is the Company Behind Splunk Cloud Platform?** - **Seller:** [Cisco](https://www.g2.com/sellers/cisco) - **Year Founded:** 1984 - **HQ Location:** San Jose, CA - **Twitter:** @Cisco (720,366 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/cisco/ (95,545 employees on LinkedIn®) - **Ownership:** NASDAQ:CSCO **Who Uses This Product?** - **Company Size:** 50% Enterprise, 50% Mid-Market #### What Are Recent G2 Reviews of Splunk Cloud Platform? **"[Platform as a Service provided which helps to set up quickly without any on-premise infrastructure](https://www.g2.com/survey_responses/splunk-cloud-platform-review-5088506)"** **Rating:** 4.0/5.0 stars *— Verified User in Supermarkets* [Read full review](https://www.g2.com/survey_responses/splunk-cloud-platform-review-5088506) --- **"[SaaS offering from Splunk](https://www.g2.com/survey_responses/splunk-cloud-platform-review-5332911)"** **Rating:** 4.5/5.0 stars *— Verified User in Computer & Network Security* [Read full review](https://www.g2.com/survey_responses/splunk-cloud-platform-review-5332911) --- #### What Are G2 Users Discussing About Splunk Cloud Platform? - [What is Splunk Cloud Platform used for?](https://www.g2.com/discussions/what-is-splunk-cloud-platform-used-for) ### 12. [UnlimitedAnalytics](https://www.g2.com/products/unlimitedanalytics/reviews) UnlimitedAnalytics is a cloud/on-premises based enterprise big data analytics, business intelligence, dashboard, and reporting software solution that implements a fit-for-all data-sources algorithm which creates a unified multi-dimensional data warehouse. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate UnlimitedAnalytics?** - **Multi-Source Analysis:** 10.0/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 10.0/10 (Category avg: 8.5/10) - **Data Workflow:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind UnlimitedAnalytics?** - **Seller:** [5stones Software](https://www.g2.com/sellers/5stones-software) - **Year Founded:** 2007 - **HQ Location:** Bristol, GB - **LinkedIn® Page:** http://www.linkedin.com/company/5stones-software-ltd (2 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market #### What Are Recent G2 Reviews of UnlimitedAnalytics? **"[Review on UnlimitedAnalytics](https://www.g2.com/survey_responses/unlimitedanalytics-review-9095941)"** **Rating:** 5.0/5.0 stars *— Shiva Kumar B.* [Read full review](https://www.g2.com/survey_responses/unlimitedanalytics-review-9095941) --- ### 13. [Aerospike Vector Search](https://www.g2.com/products/aerospike-vector-search/reviews) Aerospike Vector Search (AVS) is an advanced extension to the Aerospike Database, designed to facilitate real-time, high-performance vector similarity searches across extensive datasets. By integrating seamlessly with existing data, AVS enables developers to build applications that deliver rapid and accurate results, making it ideal for AI-driven solutions such as fraud detection, personalized recommendations, and real-time advertising. **Who Is the Company Behind Aerospike Vector Search?** - **Seller:** [Aerospike](https://www.g2.com/sellers/aerospike) - **Year Founded:** 2009 - **HQ Location:** Mountain View, CA - **Twitter:** @aerospikedb (7,825 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/2696852/ (307 employees on LinkedIn®) ### 14. [Ahana Cloud for Presto](https://www.g2.com/products/ahana-cloud-for-presto/reviews) Ahana Cloud for Presto is a fully integrated, cloud-native managed service built for AWS and the easiest way to get up and running with Presto. The managed service includes the Ahana SaaS Console which allows users to create and manage multiple Presto clusters. The Ahana SaaS Console runs in Ahana's AWS account. The Presto clusters as well as the other system components like the Hive Metastore are provisioned in the Ahana Compute Plane in the user's AWS account. **Who Is the Company Behind Ahana Cloud for Presto?** - **Seller:** [Ahana](https://www.g2.com/sellers/ahana) - **Year Founded:** 2020 - **HQ Location:** Armonk, New York, United States - **Twitter:** @ahana (256 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/ibm (334,743 employees on LinkedIn®) ### 15. [AxonIQ Console](https://www.g2.com/products/axoniq-console/reviews) AxonIQ Console Insight and management for Axon Framework and Axon Server AxonIQ Console is designed to get the most out of your Axon Framework application and Axon Server environment, no matter where it runs. Near-zero configuration is required. AxonIQ Console simplifies a complex enterprise application infrastructure by providing insight, management, control, and reporting; all in one platform. AxonIQ Console AxonIQ Console is designed to evolve and enhance its functionalities over time and will cover all the products and services AxonIQ has to offer. Based on user feedback, we have designed a tool that provides insight into applications developed with Axon Framework that can run without or with our recommended Axon Server environment. The "one-stop shop" for all initialization, configuration, insights, and monitoring of AxonIQ products. Benefits One platform Access to: Axon Framework Axon Server GCP Marketplace AxonIQ Cloud (TBA) Quick and easy setup Connect Axon Framework-based applications to Axon Server with just a few clicks, saving valuable time. Overview Gain insight into all connected applications and server nodes. Applications Clusters Event Processors Message Handlers Aggregates **Average Rating:** 4.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate AxonIQ Console?** - **Real-Time Analytics:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind AxonIQ Console?** - **Seller:** [AxonIQ](https://www.g2.com/sellers/axoniq) - **Year Founded:** 2017 - **HQ Location:** Utrecht, NL - **LinkedIn® Page:** https://www.linkedin.com/company/axoniq (39 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market #### What Are AxonIQ Console's Pros and Cons? **Pros:** - Ease of Use (1 reviews) - Easy Learning (1 reviews) - Intuitive Use (1 reviews) - Simple (1 reviews) - Usability (1 reviews) **Cons:** - Product Updates (1 reviews) - Slow Performance (1 reviews) - Slow Updates (1 reviews) - Update Issues (1 reviews) ### What Do G2 Reviewers Say About AxonIQ Console? *AI-generated summary from verified user reviews* **Pros:** - Users find AxonIQ Console to be **organized and user-friendly** , making it easy to teach to others. - Users find the **easy learning** aspect of AxonIQ Console greatly beneficial for teaching and usability. - Users appreciate the **intuitive design** of AxonIQ Console, finding it easy to learn and share with others. - Users find the **interface user-friendly** and appreciate how easily it can be taught to others. - Users value the **user-friendly organization** of AxonIQ Console, making it easy to teach and learn. **Cons:** - Users often find the **slow product updates** of AxonIQ Console frustrating when sharing with others. - Users experience **slow performance** , specifically in updating and sharing information with others promptly. - Users experience **slow updates** which can hinder sharing and timely collaboration with others effectively. - Users find the **update issues** of AxonIQ Console can lead to delays in sharing information effectively. #### What Are Recent G2 Reviews of AxonIQ Console? **"[Organized and User-Friendly, Perfect for Easy Onboarding](https://www.g2.com/survey_responses/axoniq-console-review-12092748)"** **Rating:** 4.0/5.0 stars *— Cameron J.* [Read full review](https://www.g2.com/survey_responses/axoniq-console-review-12092748) --- ### 16. [Babel Street Insights](https://www.g2.com/products/babel-street-insights/reviews) Babel Street revolutionizes how organizations manage and use knowledge, enabling smarter decision-making and strategy planning. **Who Is the Company Behind Babel Street Insights?** - **Seller:** [Babel Street](https://www.g2.com/sellers/babel-street) - **Year Founded:** 2012 - **HQ Location:** Washington, Wisconsin, United States - **Twitter:** @babelstreet (132 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/babelstreet (288 employees on LinkedIn®) ### 17. [Bizintel360](https://www.g2.com/products/bizintel360-com-bizintel360/reviews) Bizintel360 is a self-service big data analytics solution, that enables companies to gain actionable insight from a large volume of diverse data, with extreme velocity. Its powerful search engine capability enables business users to ask question within the system with various keywords to get real insight from varied data source and make relationship between islands of data sources. Our cloud based solution requires no ETL tools, no IT resources and no data warehouse. Bizintel360 helps business users to connect data of various sources and expose critical trends, metrics, and insights in the form of charts and graphs that enable business users at all levels to make key business decisions from operational level to strategic level. **Who Is the Company Behind Bizintel360?** - **Seller:** [Bizintel360.com](https://www.g2.com/sellers/bizintel360-com) - **Year Founded:** 2016 - **HQ Location:** San Jose, US - **LinkedIn® Page:** https://www.linkedin.com/company/bizintel360 (1 employees on LinkedIn®) ### 18. [Chatterlytics](https://www.g2.com/products/chatterlytics/reviews) Chatterlytics — AI-powered business analytics tool to visualize your business data, create reports and dashboards without any technical data expertise. **Who Is the Company Behind Chatterlytics?** - **Seller:** [Chatterlytics](https://www.g2.com/sellers/chatterlytics) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 19. [ComboCurve](https://www.g2.com/products/combocurve/reviews) ComboCurve is the fastest analysis platform in energy. It unites forecasting, type curves, economics, mapping, modeling, and net zero planning in a single workflow. Auto-forecast thousands of wells and compare dozens of economic scenarios simultaneously in minutes. “ComboCurve was created to solve critical pain points, helping energy companies better manage their forecasting, valuation, reporting, and decision-making functions,” said Armand Paradis, CEO and Co-Founder, ComboCurve. Track Record: 320+ Clients 150+ Personnel 2021 Disruptive Technology of the Year Winner 2022 Disruptive Technology of the Year Winner 2022 Innovation of the Year 2022 Best and Brightest Companies to Work for in the Nation 2023 Best and Brightest Companies to Work for in the Nation 2021 $10M in funding 2022 $50M in funding Key Features: Forecasting Type Curves Economics ComboCarbon - GHG & Carbon Reporting ComboSync - Automated Data Syncing **Who Is the Company Behind ComboCurve?** - **Seller:** [ComboCurve](https://www.g2.com/sellers/combocurve) - **Year Founded:** 2017 - **HQ Location:** Houston, US - **LinkedIn® Page:** https://www.linkedin.com/company/combocurve/ (81 employees on LinkedIn®) ### 20. [Data21.io](https://www.g2.com/products/data21-io/reviews) Hire skilled remote Data Analysts and Engineers Transform any data into better business decisions with power of Python and SQL. Hire Data Analysts and Data Engineers working remotely for a flat monthly rate. **Who Is the Company Behind Data21.io?** - **Seller:** [Data21.io](https://www.g2.com/sellers/data21-io) - **Year Founded:** 1998 - **HQ Location:** Manhasset, US - **LinkedIn® Page:** https://www.linkedin.com/company/infinity-interactive/ (10 employees on LinkedIn®) ### 21. [Dataleyk](https://www.g2.com/products/dataleyk/reviews) Dataleyk is the secure, fully-manage cloud data platform for SMBs. Our mission: make big data analytics simple & accessible to all. Dataleyk makes it quick & easy to have a stable, flexible, and reliable cloud data lake with near-zero technical knowledge. Store all of your company data from every single source, explore with SQL and visualize with your favorite external BI tool or internal built-in graphs. Aggregate multiple sources of data in one central location to have a 360° overview to truly realize and harness the power of your data. **Who Is the Company Behind Dataleyk?** - **Seller:** [Dataleyk](https://www.g2.com/sellers/dataleyk) - **Year Founded:** 2021 - **HQ Location:** Berlin, DE - **LinkedIn® Page:** https://linkedin.com/company/dataleyk (2 employees on LinkedIn®) ### 22. [Data Sandbox](https://www.g2.com/products/data-sandbox/reviews) The Data Sandbox is Data Republic's secure and collaborative environment which enables enterprise organizations to connect their data to the world's best people, apps, algorithms, without compromising data security or privacy. **Who Is the Company Behind Data Sandbox?** - **Seller:** [Data Republic](https://www.g2.com/sellers/data-republic-f44c51e6-ab92-4f09-8ed2-af1aa6f931ac) - **Year Founded:** 2015 - **HQ Location:** Sydney, AU - **LinkedIn® Page:** https://www.linkedin.com/company/6653960 (9 employees on LinkedIn®) ### 23. [Diskover Data](https://www.g2.com/products/diskover-data/reviews) Diskover Data develops data analytics software that enables organizations to gain real-time visibility, streamline compliance, automate workflows, orchestrate policy-based governance, integrate with existing tools through plugins, and prepare data pipelines for analytics and cloud migration. **Who Is the Company Behind Diskover Data?** - **Seller:** [Diskover](https://www.g2.com/sellers/diskover) - **Year Founded:** 2016 - **HQ Location:** Reno, US - **LinkedIn® Page:** https://www.linkedin.com/company/diskover-data (23 employees on LinkedIn®) ### 24. [DNA Intelligence](https://www.g2.com/products/dna-intelligence/reviews) **Who Is the Company Behind DNA Intelligence?** - **Seller:** [Mobileum](https://www.g2.com/sellers/mobileum) - **HQ Location:** Cupertino, California, United States - **LinkedIn® Page:** https://www.linkedin.com/company/mobileum (1,998 employees on LinkedIn®) ### 25. [DreamBig Semiconductor](https://www.g2.com/products/dreambig-semiconductor/reviews) DreamBig Semiconductor's MARS Chiplet Platform is an innovative solution designed to revolutionize the development of semiconductor products for AI, data centers, 5G, and automotive markets. By leveraging open-standard chiplets, MARS enables companies of all sizes to create scalable, high-performance, and energy-efficient systems tailored to their specific applications. This platform democratizes silicon design, allowing for rapid deployment of solutions that meet the evolving demands of modern technology landscapes. **Who Is the Company Behind DreamBig Semiconductor?** - **Seller:** [DreamBig Semiconductor](https://www.g2.com/sellers/dreambig-semiconductor) - **Year Founded:** 2019 - **HQ Location:** San Jose, 2120, US - **LinkedIn® Page:** https://www.linkedin.com/company/dreambigsemiconductor/ (215 employees on LinkedIn®) ## What Is Big Data Analytics Software? [Big Data Software](https://www.g2.com/categories/big-data) ## What Software Categories Are Similar to Big Data Analytics Software? - [Data Warehouse Solutions](https://www.g2.com/categories/data-warehouse) - [Predictive Analytics Software](https://www.g2.com/categories/predictive-analytics) - [Analytics Platforms](https://www.g2.com/categories/analytics-platforms) - [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms) - [Big Data Processing And Distribution Systems](https://www.g2.com/categories/big-data-processing-and-distribution) - [ETL Tools](https://www.g2.com/categories/etl-tools) - [Big Data Integration Platforms](https://www.g2.com/categories/big-data-integration-platforms) --- ## How Do You Choose the Right Big Data Analytics Software? ### What You Should Know About Big Data Analytics Software ### 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](https://www.g2.com/categories/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](https://www.g2.com/categories/big-data-analytics/f/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](https://www.g2.com/categories/big-data-analytics/f/data-lake), 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](https://www.g2.com/categories/big-data-analytics/f/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](https://www.g2.com/categories/big-data-analytics/f/embedded-analytics). 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](https://www.g2.com/categories/big-data-analytics/f/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](https://www.g2.com/categories/accounting), [enterprise resource planning (ERP) software](https://www.g2.com/categories/erp), [CRM software](https://www.g2.com/categories/crm),[marketing automation software](https://www.g2.com/categories/marketing-automation), 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.  ### Who Uses Big Data Analytics Software? **Data analysts:** Depending on the complexity of the software, it is likely that analysts will be required. They can help set up the requisite queries, dashboards, and notebooks for other employees and teams. They can create complex queries inside the platforms to gather a deeper understanding of business-critical data. **Operations and supply chain teams:** A company’s supply chain frequently has many touchpoints, and as a result, many data points. Therefore, employees working in operations and supply chain teams are able to use big data analytics software to gain a better understanding of their departments and the data that is generated, such as from an ERP system. These applications track everything from accounting to supply chain and distribution; by inputting supply chain data into this software, supply chain managers can optimize a number of processes to save time and resources. **Finance teams:** Finance teams leverage big data analytics software to gain insight and understanding into the factors that impact an organization's bottom line. Through integrations with financial systems such as [accounting software](https://www.g2.com/categories/accounting), employees such as chief financial officers (CFOs) can see how well the business is performing. As mentioned above, these employees will likely be accessing the software via self-service dashboards that were set up by data analysts. By integrating financial data with sales, marketing, and other operations data, accounting and finance teams pull actionable insights that might not have been uncovered through the use of traditional tools. **Sales and marketing teams:** Sales teams also seek to improve financial metrics and can benefit tremendously from being more data-driven. Through the use of both self-service analytics tools and embedded analytics solutions, they can obtain insights into prospective accounts, sales performance, and pipeline forecasting, among many other use cases. Using analytics tools in a sales team can help businesses optimize their sales processes and influence revenue. For marketing teams, tracking the performance of campaigns is key. Since they run different types of campaigns, including email marketing, digital advertising, or even traditional advertising campaigns, analytics tools allow marketing teams to track the performance of those campaigns in one central location. **Consultants:** Businesses do not always have the luxury to build, develop, and optimize their own analytics solutions. Some businesses opt to employ external consultants, such as [business intelligence (BI) consulting providers](https://www.g2.com/categories/business-intelligence-bi-consulting). These providers seek to understand a business and its goals, interpret data, and offer advice to ensure goals are met. BI consultants frequently have industry-specific knowledge alongside their technical backgrounds, with experience in healthcare, business, and other fields.  ### What are the Alternatives to Big Data Analytics Software? Alternatives to big data analytics software can replace this type of software, either partially or completely: [Analytics platforms](https://www.g2.com/categories/analytics-platforms) **:** Analytics platforms might include big data integrations, but are broader-focused tools that facilitate the following five elements: data preparation, data modeling, data blending, data visualization, and insights delivery. [Log analysis software](https://www.g2.com/categories/log-analysis): Businesses that are focused on log data might benefit from deploying log analysis software, which is used to analyze log data from applications and systems. It should be kept in mind that this software is much more limited in terms of data types and data sources to which it can be connected to. However, since log analysis software focuses on logs, it frequently provides more granular details around log-related data. [Stream analytics software](https://www.g2.com/categories/stream-analytics) **:** When one is looking for tools specifically geared toward analyzing data in real time, stream analytics software is a go-to solution. These tools help users analyze data in transfer through APIs, between applications, and more. This software can be helpful with internet of things (IoT) data, which one frequently wants to analyze in real time. [Predictive analytics software](https://www.g2.com/categories/predictive-analytics): Broad-purpose big data analytics software allows businesses to conduct various forms of analysis, such as prescriptive, descriptive, and predictive. Businesses that are focused on looking at their past and present data to predict future outcomes can use predictive analytics software for a more finetuned solution.  [Text analysis software](https://www.g2.com/categories/text-analysis): Big data analytics software is focused on structured or numerical data, allowing users to drill down and dig into numbers to inform business decisions. If the user is looking to focus on unstructured or text data, text analysis solutions are the best bet. These tools help users quickly understand and pull sentiment analysis, key phrases, themes, and other insights from unstructured text data. #### Software Related to Big Data Analytics Software Related solutions that can be used together with big data analytics software include: [Data warehouse software](https://www.g2.com/categories/data-warehouse) **:** Most companies have a large number of disparate data sources, so to best integrate all their data, they implement a data warehouse. Data warehouses can house data from multiple databases and business applications, which allows BI and analytics tools to pull all company data from a single repository. This organization is critical to the quality of the data that is ingested by analytics software. [Data preparation software](https://www.g2.com/categories/data-preparation) **:** A key solution necessary for easy data analysis is a data preparation tool and other related data management tools. These solutions allow users to discover, combine, clean, and enrich data for simple analysis. Data preparation tools are often used by IT teams or data analysts tasked with using BI tools. Some BI platforms offer data preparation features, but businesses with a wide range of data sources often opt for a dedicated preparation tool. ### Challenges with Big Data Analytics Software Software solutions can come with their own set of challenges.  **Need for skilled employees:** Big data analytics software is not necessarily simple. Often, these tools require a dedicated administrator to help implement the solution and assist others with adoption. However, there is a shortage of skilled data scientists and analysts that are equipped to set up such solutions. Additionally, those same data scientists will be tasked with deriving actionable insights from within the data.  Without people skilled in these areas, businesses cannot effectively leverage the tools or their data. Even the self-service tools, which are to be used by the average business user, require someone to help deploy them. Companies can turn to vendor support teams or third-party consultants to assist if they are unable to bring someone in house. **Data organization:** To get the most of analytics solutions, that data needs to be organized. This means that databases should be set up correctly and integrated properly. This may require building a data warehouse, which can store data from a variety of applications and databases in a central location.  Businesses may need to purchase a dedicated [data preparation software](https://www.g2.com/categories/data-preparation) as well to ensure that data is joined and is clean for the analytics solution to consume in the right way. In the context of big data, a company might want to specifically consider big data processing and distribution software. This often requires a skilled data analyst, IT employee, or an outside consultant to help ensure data quality is at its finest for easy analysis. **User adoption:** It is not always easy to transform a business into a data-driven company. Particularly at more established companies that have done things the same way for years, it is not simple to force analytics tools upon employees, especially if there are ways for them to avoid it. If there are other options, such as spreadsheets or existing tools that employees can use instead of analytics software, they will most likely go that route. However, if managers and leaders ensure that analytics tools are a necessity in an employee’s day to day, then adoption rates will increase. ### Which Companies Should Buy Big Data Analytics Software? As has often been said, data is the fuel that drives modern businesses. Although it is cliche, it no doubt has truth to it. Therefore, businesses across the globe and across industries should consider some sort of analytics solution, such as big data analytics in order to make sense of that data and begin to make data-driven decisions.  **Financial services:** Within financial institutions, such as insurance brokerages, banks, and credit unions, it is common for a host of different systems to be used. These companies have data ranging from customer records, to transactions, to market data, and more. With the proliferation of systems comes more data. With a robust analytics solution in place, they can get a better understanding of the data that is being produced from the various systems across the business. As an industry that is heavily regulated, users can benefit from governed access capabilities which can be particularly beneficial, since it can assist in auditing company processes. **Healthcare:** Within the space of healthcare, bad data practices might have dire or even deadly consequences. Big data analytics software can help these organizations with having an overarching view of their data, such as patient records, insurance claims, finances, and more. Through the implementation of analytics, healthcare companies can lower risk and costs, and make their billing and collections smarter. **Retail** : Retail organizations, whether they be B2C, B2B, D2C, or others, rely on data to make informed decisions. For example, a seller of printers, in order to run a successful business, must keep track of many things such as their inventory, sales, their sales team, and returns. If all of this data is kept siloed within different systems, there is no single source of truth and departments cannot have a conversation around the actual state of the business’ data. With big data analytics software set up and connected to all of the relevant data sources, any retail business can see benefits and make meaningful data-driven decisions. ### How to Buy Big Data Analytics Software #### Requirements Gathering (RFI/RFP) for Big Data Analytics Software If a company is just starting out on their analytics journey, g2.com can help in selecting the best software for the particular company and use case. Since the particular solution might vary based on company size and industry, G2 is a great place to sort and filter reviews based on these criteria, along with many more. As mentioned above, the variety, volume, and velocity of data are vast. Therefore, users should think about how the particular solution fits their particular needs, as well as their future needs as they accumulate more data.  To find the right solution, buyers should determine pain points and jot them down. These should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy. Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more. Depending on the scope of the deployment, it might be helpful to produce a request for information (RFI), a one-page list with a few bullet points describing what is needed from a big data analytics software. #### Compare Big Data Analytics Software Products **Create a long list** From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor. **Create a short list** From the long list of vendors, it is helpful to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions. **Conduct demos** To ensure the comparison is thoroughgoing, the user should demo each solution on the shortlist with the same use case and data sets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.  #### Selection of Big Data Analytics Software **Choose a selection team** As big data analytics software is all about the data, the user must make sure that the selection process is data driven as well. The selection team should compare notes and facts and figures which they noted during the process, such as time to insight, number of visualizations, and availability of advanced analytics capabilities. **Negotiation** Just because something is written on a company’s pricing page, does not mean it is not negotiable (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others. **Final decision** After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board. ### What Does Big Data Analytics Software Cost? Businesses decide to deploy big data analytics software with the goal of deriving some degree of a return on investment (ROI). #### Return on Investment (ROI) As they are looking to recoup their losses that they spent on the software, it is critical to understand the costs associated with it. As mentioned above, this software is typically billed per user, which is sometimes tiered depending on the company size. More users will typically translate into more licenses, which means more money. Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the big data analytics tool. ### Implementation of Big Data Analytics Software **How is Big Data Analytics Software Implemented?** Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether that be an implementation specialist from the vendor or a third-party consultancy. With vast experience, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively. **Who is Responsible for Big Data Analytics Software Implementation?** It may require a lot of people, or many teams, to properly deploy an analytics platform. This is because data can cut across teams and functions. As a result, it is rare that one person or even one team has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can piece together their data and begin the journey of analytics, starting with proper data preparation and management. ### Big Data Analytics Software Trends **Data literacy** Business data is no longer locked up in silos. With big data analytics solutions, more users across a business can find, access, and analyze this data. In addition, [artificial intelligence (AI) software](https://www.g2.com/categories/artificial-intelligence) such as [natural language processing (NLP) software](https://www.g2.com/categories/natural-language-processing-nlp) help make searching through and for data easier and more powerful, providing more accurate results. Implementing 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 all their various sources. **Shift to the cloud** The move from on-premises data analytics to the cloud has been underway for a number of years, with more and more businesses moving their data and data insights into the cloud. This is taking place for various reasons, such as time to insights. The move away from on-premises infrastructure has helped many companies enable data work anywhere one has access to the cloud—anywhere with internet access.  **Conversational AI** Historically, to query data within an analytics solution, users needed to master a query language like SQL. With the rise of conversational interfaces, users uncover the data and insights they are looking for using intuitive language. Intuitive methods of querying data mean enabling a larger user base to access and make sense of company data. **Machine learning** AI is quickly becoming a promising feature of analytics solutions throughout the whole data journey, from ingestion to insights. From AI-powered data preparation to smart insights, in which the platform suggests visualizations to the end user, big data analytics solutions are quickly becoming more powerful. Machine learning is helping end users discover hidden insights, allowing them to make sense of data and helping them to understand what they are seeing.