Best Big Data Analytics Software - Page 4

How Many Big Data Analytics Software Products Does G2 Track?

Total Products under this Category: 114

Category Stats (Oct 2026)

  • Average Rating: 4.49/5 The average rating of products in this category, based on all submitted ratings
  • Top Trending Product: Cloudera (+0.31%) - Among all products in this category, Cloudera recorded the largest rating increase compared to last month

Last updated: October 16, 2026

How Does G2 Rank Big Data Analytics Software Products?

Why You Can Trust G2's Software Rankings:

  • 30 Analysts and Data Experts
  • 8,600+ Authentic Reviews
  • 114+ 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.

G2 Grid® for Big Data Analytics Software

G2 Grid® for Big Data Analytics Software plotting products by satisfaction and market presence

Highlighted products: Databricks, Google Cloud BigQuery, Snowflake, IBM watsonx.data, Alteryx, Azure Databricks, 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=alteryx&focus%5B%5D=azure-databricks&focus%5B%5D=kyvos-semantic-layer&focus%5B%5D=azure-synapse-analytics)

DoubleCloud

DoubleCloud is winding down operations. The company ceased creating new accounts on October 1, 2024, and will completely close on March 1, 2025. DoubleCloud specialized in data analytics infrastructure, offering managed services for open-source data technologies. Throughout its operations, DoubleCloud provided tools for building data pipelines, including solutions for data ingestion, storage, orchestration, ELT, and real-time visualization. The company was committed to helping businesses streamline and optimize their data operations with powerful, open-source-based technologies.

Average Rating: 4.9/5.0

Total Reviews: 4

How Do G2 Users Rate DoubleCloud?

  • Has the product been a good partner in doing business?: 10.0/10 (Category avg: 8.9/10)
  • Multi-Source Analysis: 9.2/10 (Category avg: 8.6/10)
  • Real-Time Analytics: 10.0/10 (Category avg: 8.6/10)

Who Is the Company Behind DoubleCloud?

Who Uses This Product?

  • Company Size: 75% Small, 25% Medium

What Are Recent G2 Reviews of DoubleCloud?

Exploratory

Exploratory enables users to understand data by transforming, visualizing, and applying advanced statistics and machine learning algorithms.

Average Rating: 4.2/5.0

Total Reviews: 3

How Do G2 Users Rate Exploratory?

  • Has the product been a good partner in doing business?: 10.0/10 (Category avg: 8.9/10)
  • Multi-Source Analysis: 8.3/10 (Category avg: 8.6/10)
  • Real-Time Analytics: 3.3/10 (Category avg: 8.6/10)
  • Data Workflow: 8.3/10 (Category avg: 8.6/10)

Who Is the Company Behind Exploratory?

Who Uses This Product?

  • Company Size: 100% Small

What Do G2 Reviewers Say About Exploratory?

AI-generated summary from verified user reviews

Pros
  • Users value the AI-assisted modeling capabilities of Exploratory, enhancing data organization and simplifying analysis for beginners.
  • Users value the effective problem-solving capabilities of Exploratory, simplifying data organization and enhancing analysis for all levels.
  • Users value the productivity improvement provided by Exploratory, simplifying complex data analysis with AI assistance.

What Are Recent G2 Reviews of Exploratory?

What Are G2 Users Discussing About Exploratory?

Kinetica

Kinetica is the database for time & space. Kinetica makes it easy and fast to: - ingest massive amounts of IoT data and other contextual data sets - fuse data sets using spatial and temporal joins - analyze data using SQL based analytics for spatial, graph, and time-series analytics or running containerized ML models

Average Rating: 4.3/5.0

Total Reviews: 2

How Do G2 Users Rate Kinetica?

  • Has the product been a good partner in doing business?: 8.3/10 (Category avg: 8.9/10)
  • Multi-Source Analysis: 6.7/10 (Category avg: 8.6/10)
  • Real-Time Analytics: 6.7/10 (Category avg: 8.6/10)
  • Data Workflow: 8.3/10 (Category avg: 8.6/10)

Who Is the Company Behind Kinetica?

  • Seller: Kinetica
  • Year Founded: 2016
  • HQ Location: Vienna, US
  • Twitter: @KineticaHQ
    3,461 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    66 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 100% Medium

What Are Recent G2 Reviews of Kinetica?

What Are G2 Users Discussing About Kinetica?

AI can help you find the answers. G2 helps you trust them.

Connect G2 to Claude or ChatGPT for answers grounded in G2's trusted reviews, comparisons, and pricing from real user insights.

How it works

Querio

Querio is an AI-native analytics platform with governance built in. Business users ask questions in plain English and get answers they can trust. Data teams define the context once, keep it in their own Git, and let agents do the work without losing control of how the data is used. Most AI analytics tools bolt a chatbot onto a warehouse. The results drift: every user gets a slightly different definition of revenue, every query is a fresh guess at the joins, and nobody can audit what the model did. Querio is designed around that problem. A context layer holds table relationships, metrics and business terminology as plain files that live in version control. Every agent run reads from the same context, so answers stay consistent across teams and change only when the data team says so. Querio connects to Snowflake, BigQuery, Postgres and other modern warehouses and databases over read-only connections. Queries run on live data where it already sits. There is no data duplication and no proprietary storage. For business users, Querio works like a conversational analyst. Ask a question, follow up with "break this down by region" or "show last year instead", and the charts update while the context is kept. Results can be pinned to boards and dashboards that stay in sync with the warehouse. For data and analytics teams, Querio is a code-first workspace. The agent's working medium is a reactive Python notebook where prompts, generated SQL and Python, charts and narrative live together. Every line of code the agent writes is visible, editable and versioned. Analysts can stop a run, correct the query, and continue. Nothing is hidden and nothing is a black box. Querio also powers embedded analytics. Product and platform teams can ship AI-driven questions, charts and tables inside their own applications, with APIs and theme tokens to match their design system. Security and governance are first-class. Querio is SOC 2 Type II audited, uses read-only connections with encrypted credentials, offers granular access control, and never uses customer data or queries to train external models, on any plan. Key Features Natural language querying and conversational analytics. Ask questions in everyday language, refine with follow-ups, and drill into detail without writing SQL. The agent handles translation and keeps the context of the conversation. Context layer as code. Define joins, metrics and glossary terms once, as plain files in Git. Every agent, every user and every query reuses the same definitions, so answers stay consistent and changes are reviewable. Agentic notebooks (coding optional). Reactive Python notebooks where prompts, generated SQL and Python, charts and narrative sit together. Business users stay in prompt mode. Technical users drop into code when they need to. Explore: instant answers on live data. Start from a prompt, iterate freely, drill down as many times as needed, and adjust filters or visualisations on the fly. AI-assisted dashboards and boards. Generate charts and tables from questions, then organise them into dashboards and boards that track KPIs or tell a data story, kept in sync with the warehouse. Embedded analytics and AI experiences. Put AI-powered questions, charts and tables inside your own product, with APIs and theme tokens so customer-facing analytics look native. Schema awareness and full transparency. Tag specific database values inside prompts, see what data Querio can reach, and inspect every query it writes. No hidden logic. Stop and edit. Pause a run, change the filters or the generated SQL and Python, and re-run without leaving the workflow. Analysts keep the control they expect with the speed of an agent. Version control for outputs. Update analyses and dashboards while previous versions stay intact, so teams can compare past and present results without losing work. Secure, governed access. SOC 2 Type II, read-only database connections, encrypted credentials, granular access control, and no training on customer data or queries on any plan. Typical Use Cases For Data and Analytics teams Centralising metrics, joins and terminology in one version-controlled context layer Cutting the data request backlog without giving up governance Reviewing and hardening agent-generated SQL and Python in a transparent workspace Replacing or modernising Tableau and Power BI estates with self-serve that the data team still controls For Product and Growth teams Funnel, activation and feature adoption analysis Experiment and cohort analysis without writing SQL Self-serve product analytics embedded directly in the app For Revenue, Sales and Marketing Pipeline, MRR/ARR and retention reporting Campaign performance and attribution analysis Shared boards for weekly and monthly business reviews For Operations and Support Monitoring operational KPIs and SLAs on live data Finding bottlenecks and anomalies through follow-up questions Answering ad hoc performance questions without Excel exports For SaaS products Adding self-service, AI-driven analytics to customer portals and internal tools

Average Rating: 4.5/5.0

Total Reviews: 11

How Do G2 Users Rate Querio?

  • Has the product been a good partner in doing business?: 8.9/10 (Category avg: 8.9/10)

Who Is the Company Behind Querio?

Who Uses This Product?

  • Company Size: 45% Small, 36% Medium

What Do G2 Reviewers Say About Querio?

AI-generated summary from verified user reviews

Pros
  • Users value the accuracy of responses in Querio, enabling quick and precise CRM data analysis effortlessly.
  • Users praise the seamless AI integration of Querio, enabling quick, accurate queries and insightful visualizations effortlessly.
  • Users praise the ease of use in data analysis with Querio, enabling quick, accurate insights through natural language queries.
  • Users praise the data visualization capabilities of Querio, enabling instant insights and clear presentation of analyses.
  • Users value the outstanding ease of use in Querio, enabling natural language queries for quick, accurate results.

What Are Recent G2 Reviews of Querio?

SAS Visual Statistics

Multiple users can explore data, then interactively create and refine predictive models. Distributed, in-memory processing slashes model development time, quickly surfacing valuable insights you can act on.

Average Rating: 4.4/5.0

Total Reviews: 24

How Do G2 Users Rate SAS Visual Statistics?

  • Has the product been a good partner in doing business?: 8.1/10 (Category avg: 8.9/10)
  • Multi-Source Analysis: 6.7/10 (Category avg: 8.6/10)
  • Real-Time Analytics: 6.7/10 (Category avg: 8.6/10)
  • Data Workflow: 6.7/10 (Category avg: 8.6/10)

Who Is the Company Behind SAS Visual Statistics?

  • Seller: SAS Institute
  • Year Founded: 1976
  • HQ Location: Cary, NC
  • Twitter: @SASsoftware
    60,863 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    15,122 employees on LinkedIn®
  • Phone: 1-800-727-0025

Who Uses This Product?

  • Company Size: 52% Large, 32% Small

What Do G2 Reviewers Say About SAS Visual Statistics?

AI-generated summary from verified user reviews

Pros
  • Users value the ease of use in SAS Visual Statistics, noting no training required for effective operation.
  • Users value the automated analytics in SAS Visual Statistics, making data understanding and forecasting straightforward.
  • Users value the proactive customer support of SAS Visual Statistics, enhancing their overall experience with the product.
  • Users value the advanced machine learning capabilities of SAS Visual Statistics for efficient data analysis and predictive modeling.
  • Users value the reliable reporting tools of SAS Visual Statistics for their effective forecasting and clarity.
Cons
  • Users express concerns about the high licensing cost, making SAS Visual Statistics difficult to afford for many.
  • Users find that learning may be difficult without prior experience with SAS tools, limiting usability and understanding.
  • Users are concerned about the high licensing cost and associated infrastructure requirements of SAS Visual Statistics.
  • Users feel that customization difficulty in SAS Visual Statistics hinders their ability to tailor the software effectively.
  • Users struggle with data analysis difficulty, finding it hard to grasp modeling calculations and reconcile approaches effectively.

What Are Recent G2 Reviews of SAS Visual Statistics?

Timbr

Timbr is the ontology-based semantic layer used by leading enterprises to make faster, better decisions with ontologies that transform structured data into AI-ready knowledge. By unifying enterprise data into a SQL-queryable knowledge graph, Timbr makes relationships, metrics, and context explicit, enabling both humans and AI to reason over data with accuracy and speed. Its open, modular architecture connects directly to existing data sources, virtualizing and governing them without replication. The result is a dynamic, easily accessible model that powers analytics, automation, and LLMs through SQL, APIs, SDKs, and natural language. Timbr lets organizations operationalize AI on their data - securely, transparently, and without dependence on proprietary stacks - maximizing data ROI and enabling teams to focus on solving problems instead of managing complexity.

Average Rating: 4.4/5.0

Total Reviews: 7

How Do G2 Users Rate Timbr?

  • Multi-Source Analysis: 8.3/10 (Category avg: 8.6/10)
  • Real-Time Analytics: 10.0/10 (Category avg: 8.6/10)
  • Data Workflow: 8.3/10 (Category avg: 8.6/10)

Who Is the Company Behind Timbr?

  • Seller: Timbr.ai
  • Year Founded: 2018
  • HQ Location: Raanana , IL
  • LinkedIn® Page: www.linkedin.com
    11 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 63% Small, 38% Large

What Do G2 Reviewers Say About Timbr?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the powerful features of Timbr for simplifying data analysis and creating meaningful visualizations.
  • Users value the SQL support in Timbr, which simplifies data analysis and enhances understanding of complex datasets.
  • Users love the automation tools of Timbr, simplifying email campaigns, social media posts, and repetitive tasks.
  • Users appreciate the powerful data analysis features of Timbr, enabling clear insights through systematic graphs and SQL.
  • Users appreciate the enhanced data management features of Timbr, simplifying SQL complexities and providing valuable context.
Cons
  • Users find the learning curve steep, especially those from non-technical backgrounds, making it challenging to navigate Timbr.
  • Users from non-technical backgrounds find it challenging to grasp the complex usability of Timbr, requiring more time to understand.
  • Users find Timbr expensive, especially for small enterprises, with limited customization options available.
  • Users feel the limited customization options hinder their experience, especially given the high subscription price for small businesses.

What Are Recent G2 Reviews of Timbr?

ChaosSearch Data Lake Platform

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: 8.6/10)
  • Real-Time Analytics: 9.2/10 (Category avg: 8.6/10)
  • Data Workflow: 9.2/10 (Category avg: 8.6/10)

Who Is the Company Behind ChaosSearch Data Lake Platform?

  • Seller: ChaosSearch
  • Year Founded: 2017
  • HQ Location: Boston, US
  • Twitter: @CHAOSSEARCH
    751 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    10 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 50% Small, 50% Large

What Are Recent G2 Reviews of ChaosSearch Data Lake Platform?

What Are G2 Users Discussing About ChaosSearch Data Lake Platform?

ETHoS.

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.?

Who Uses This Product?

  • Company Size: 100% Small

What Are Recent G2 Reviews of ETHoS.?

What Are G2 Users Discussing About ETHoS.?

INDEXIMA Data Hub

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: 8.6/10)
  • Real-Time Analytics: 10.0/10 (Category avg: 8.6/10)
  • Data Workflow: 10.0/10 (Category avg: 8.6/10)

Who Is the Company Behind INDEXIMA Data Hub?

  • Seller: INDEXIMA
  • Year Founded: 2016
  • HQ Location: Paris, FR
  • Twitter: @indexima
    292 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    6 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 100% Small

What Are G2 Users Discussing About INDEXIMA Data Hub?

Kognitio

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: 8.6/10)
  • Real-Time Analytics: 5.0/10 (Category avg: 8.6/10)
  • Data Workflow: 3.3/10 (Category avg: 8.6/10)

Who Is the Company Behind Kognitio?

  • Seller: Kognitio
  • Year Founded: 1987
  • HQ Location: BERKSHIRE, GB
  • Twitter: @kognitio
    2,291 Twitter followers
  • LinkedIn® Page: uk.linkedin.com
    12 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 100% Small

What Are Recent G2 Reviews of Kognitio?

Kpow for Apache Kafka

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
  • Year Founded: 2019
  • HQ Location: Northcote, AU
  • Twitter: @factorhousehq
    125 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    15 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 50% Medium, 25% Small

What Do G2 Reviewers Say About Kpow for Apache Kafka?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the affordability of Kpow for Apache Kafka, praising its fair pricing and value.
  • Users appreciate the easy setup of Kpow for Apache Kafka, praising its user-friendly interface and schema support.
  • Users appreciate the clean UI of Kpow for Apache Kafka, enhancing their overall experience and usability.
Cons
  • Users experience data management issues in Kpow, particularly with null fields in topic data inspection lacking deserializers.

What Are Recent G2 Reviews of Kpow for Apache Kafka?

What Are G2 Users Discussing About Kpow for Apache Kafka?

Mindtree Data Analytics

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: 8.6/10)
  • Data Workflow: 6.7/10 (Category avg: 8.6/10)

Who Is the Company Behind Mindtree Data Analytics?

  • Seller: Mindtree Ltd.
  • Year Founded: 1999
  • HQ Location: Bangalore, India
  • Twitter: @Mindtree_Ltd
    78,012 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    19,417 employees on LinkedIn®
  • Ownership: BSE: 532819

Who Uses This Product?

  • Company Size: 100% Medium

What Are Recent G2 Reviews of Mindtree Data Analytics?

Platfora

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?

Who Uses This Product?

  • Company Size: 100% Small

What Are Recent G2 Reviews of Platfora?

Rayven

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
  • Year Founded: 2016
  • HQ Location: Sydney, AU
  • Twitter: @RayvenIOT
    56 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    32 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Retail
  • Company Size: 69% Medium, 52% Small

What Do G2 Reviewers Say About Rayven?

AI-generated summary from verified user reviews

Pros
  • Users value the ease of use of Rayven, allowing seamless integration and workflow creation without coding skills.
  • Users value Rayven for its real-time data integration and seamless unification of various tools into one efficient system.
  • Users value Rayven's automation capabilities, enabling seamless integration and real-time data unification for improved performance tracking.
  • Users value the customization capabilities of Rayven, enabling tailored solutions to meet specific healthcare needs seamlessly.
  • Users value the robust data management of Rayven, enabling real-time processing and efficient automation across diverse systems.
Cons
  • Users report a steep initial learning curve due to complex setups and the need for technical knowledge.
  • Users find the difficult learning curve challenging due to overwhelming initial setup and complex feature configurations.
  • Users find the learning difficulty challenging due to the overwhelming initial setup and complex configuration processes.
  • Users find the complex setup of Rayven challenging, necessitating extensive planning for effective use and integration.
  • Users find the setup complexity challenging, often needing extra support for advanced integrations and configurations.

What Are Recent G2 Reviews of Rayven?

Shakudo

Shakudo ensures compatibility across data tools allowing companies to build the best data infrastructure for their needs. With Shakudo you can mix and match your data tooling to create a more reliable, performant, and cost-effective stack than ever before.

Average Rating: 4.5/5.0

Total Reviews: 2

Who Is the Company Behind Shakudo?

  • Seller: Shakudo
  • Year Founded: 2021
  • HQ Location: Toronto, CA
  • LinkedIn® Page: ca.linkedin.com
    34 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 50% Medium, 50% Small

What Do G2 Reviewers Say About Shakudo?

AI-generated summary from verified user reviews

Pros
  • Users find Shakudo's connectivity exceptional, effectively integrating various data tools for optimal performance.
  • Users appreciate the seamless data access provided by Shakudo, maximizing the potential of their diverse data tools.
  • Users value Shakudo for its effective data integration, maximizing the utility of diverse data tools in their stack.
  • Users find Shakudo excellent for data management, successfully coordinating tools and optimizing their data stack.
  • Users appreciate Shakudo for its flexible data pipelining, effectively coordinating various tools in their data stack.
Cons
  • Users find limited data transformation capabilities in Shakudo, often needing additional tools for complex tasks.
  • Users find feature limitations in Shakudo, as it lacks robust data transformation capabilities for complex tasks.
  • Users find Shakudo's lack of data transformation features limits their ability to perform complex data manipulations efficiently.
  • Users note a significant lack of functionality in Shakudo, requiring additional tools for complex data manipulation tasks.
  • Users find a lack of data transformation tools in Shakudo, often relying on external tools for complex tasks.

What Are Recent G2 Reviews of Shakudo?

Bijou Barry
BB
Researched and written by Bijou Barry
Updated April 9, 2026