eRAG

eRAG is an enterprise data interaction platform developed by GigaSpaces that enables non-technical users to query live operational databases using natural language, bypassing the need for traditional ETL pipelines or pre-built dashboards.

Reviewed by 7wData

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eRAG is an enterprise data interaction platform developed by GigaSpaces that enables non-technical users to query live operational databases using natural language, bypassing the need for traditional ETL pipelines or pre-built dashboards. It is designed for business analysts, data stewards, and decision-makers who need real-time access to structured data across multiple sources without writing SQL or waiting for IT requests. The platform positions itself as an alternative to conventional retrieval-augmented generation (RAG) systems by focusing on dynamic, in-flight data rather than static document stores, making it suitable for industries like finance, retail, and logistics where data freshness is critical.

eRAG works by generating execution plans based on situational data analysis, visualizing information, and anticipating exploration directions before a user finishes typing. It supports real-time, ad hoc queries against live sources such as Google BigQuery, MSSQL, MySQL, Oracle, PostgreSQL, Snowflake, DB2 for IBM i (AS/400), Oracle11, and SAP HANA, with no upfront data modeling or indexing required. The platform uses a semantic reasoning layer that aligns definitions and context across departments, ensuring consistent answers even when different teams use different terminology. It integrates with Amazon SageMaker and IBM Watsonx.governance for real-time monitoring and risk assessment of GenAI interactions with enterprise databases, and allows users to adjust actions while execution is underway, a capability not common in static RAG tools.

In the market for conversational AI on structured data, eRAG competes with tools like ThoughtSpot, which offers AI-driven analytics but requires pre-built models, and with Microsoft's Copilot for Azure SQL, which is tightly coupled to the Microsoft ecosystem. Unlike general-purpose RAG frameworks such as LlamaIndex or LangChain, eRAG provides a ChatGPT-like user experience out of the box, with managed governance and audit trails. Its integration with IBM Watsonx.governance gives it a compliance edge for regulated industries, but it lacks the open-source flexibility and community-driven extensions of frameworks like Haystack or Weaviate.

The honest trade-offs with eRAG include its high starting price of $2,000 per month for up to 10 users, which may be prohibitive for small teams or startups. The platform limits data source connections to 2 Level 1 sources on the Basic plan and caps queries at 200 per user per month, which can throttle heavy usage. It does not support unstructured data (e.g., PDFs, images) natively, focusing exclusively on structured databases. Additionally, the Enterprise tier requires contacting sales for pricing, and the 12-month minimum term on paid plans reduces flexibility for short-term projects or proof-of-concept trials.

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How it works

  1. Execution plan generation

    Generates step-by-step execution plans based on situational data analysis, anticipating user needs before queries are fully specified.

  2. Natural language querying

    Allows users to ask questions in plain English and receive answers from live databases without writing SQL or setting up data pipelines.

  3. Real-time data exploration

    Supports ad hoc exploration of multiple live data sources simultaneously, with results updating as data changes in the source systems.

  4. Semantic reasoning layer

    Aligns definitions and context across departments, ensuring consistent answers even when different teams use different business terminology.

  5. Governance integration

    Integrates with Amazon SageMaker and IBM Watsonx.governance for real-time monitoring and risk assessment of GenAI interactions with databases.

  6. In-flight action adjustment

    Enables users to modify queries or actions while execution is underway, adapting to new insights without restarting the process.

  7. Multi-source support

    Connects to up to 15 Level 1 data sources (e.g., Snowflake, PostgreSQL) and 1 Level 2 source (e.g., SAP HANA) on the Advanced plan.

Strengths and trade-offs

Strengths

  • eRAG eliminates the need for ETL pipelines or data pre-processing, allowing users to query live operational databases directly with natural language.
  • The platform supports up to 15 Level 1 data source connections on the Advanced plan, including major databases like Snowflake, Oracle, and PostgreSQL.
  • Integration with IBM Watsonx.governance provides real-time monitoring and risk assessment for GenAI interactions, a compliance advantage for regulated industries.
  • The semantic reasoning layer ensures consistent answers across departments by aligning business definitions and context organization-wide.

Trade-offs

  • The Basic plan costs $2,000 per month for up to 10 users with a 12-month minimum term, making it expensive for small teams or short-term projects.
  • Query limits are capped at 200 per user per month on the Basic plan, which can restrict heavy usage or large-scale data exploration.
  • eRAG does not natively support unstructured data sources like PDFs or images, limiting its use to structured databases only.
  • The Enterprise tier requires contacting sales for pricing, and the platform lacks an open-source version, reducing flexibility for custom deployments.

Pricing context

Basic: $2,000/month for up to 10 users (12-month min); Advanced: $5,000/month for up to 20 users (12-month min); Enterprise: custom pricing, contact required. Annual billing available at $130/user/month (Basic) or $195/user/month (Advanced).

Getting started with eRAG

  1. Sign up for eRAG

    Go to the GigaSpaces eRAG website and click the Get Started button. Choose a plan (Basic, Advanced, or Enterprise) and complete the registration form. You will receive login credentials and access to the platform dashboard.

  2. Connect your data sources

    From the dashboard, navigate to the Data Sources section. Click Add Source and select your database type (e.g., Snowflake, PostgreSQL). Enter the connection details such as host, port, credentials, and database name. Test the connection to verify access.

  3. Configure the semantic layer

    In the Settings menu, open the Semantic Reasoning Layer. Define business terms and mappings for your organization, such as aligning 'revenue' across departments. This ensures consistent answers when different teams use different terminology.

  4. Run a natural language query

    Type a question in plain English into the query bar, such as 'What were total sales last quarter?' eRAG generates an execution plan and displays results from your live databases. Review the plan and adjust if needed before finalizing.

  5. Set up governance monitoring

    Integrate with IBM Watsonx.governance or Amazon SageMaker by going to the Governance tab. Provide API keys and configure monitoring rules for real-time risk assessment. This enables audit trails and compliance oversight for all GenAI interactions.

Frequently Asked Questions

What is eRAG and how does it work?

eRAG is an enterprise platform by GigaSpaces that lets non-technical users query live operational databases using natural language. It generates execution plans, visualizes data, and anticipates exploration needs, bypassing ETL pipelines and SQL. It works with multiple live sources like Snowflake and PostgreSQL.

What databases does eRAG support?

eRAG supports live connections to Google BigQuery, MSSQL, MySQL, Oracle, PostgreSQL, Snowflake, DB2 for IBM i, Oracle11, and SAP HANA. The Advanced plan connects up to 15 Level 1 sources and 1 Level 2 source, with no upfront data modeling required.

How much does eRAG cost per month?

eRAG's Basic plan costs $2,000 per month for up to 10 users with a 12-month minimum term. The Advanced plan is $5,000 per month for up to 20 users. Annual billing is available at $130 per user per month for Basic or $195 for Advanced.

Does eRAG support unstructured data like PDFs?

No, eRAG focuses exclusively on structured databases and does not natively support unstructured data such as PDFs or images. This limits its use to querying live operational databases, making it unsuitable for tasks involving document analysis or image-based data.

How does eRAG ensure data governance and compliance?

eRAG integrates with Amazon SageMaker and IBM Watsonx.governance for real-time monitoring and risk assessment of GenAI interactions with databases. This provides audit trails and consistent answers across departments via its semantic reasoning layer, a compliance advantage for regulated industries.

What are the main differences between eRAG and ThoughtSpot?

eRAG queries live operational databases directly without ETL or pre-built models, while ThoughtSpot requires pre-built models for AI-driven analytics. eRAG offers a ChatGPT-like experience with managed governance, but ThoughtSpot may be more established in the market for ad hoc analytics.

Alternatives

How eRAG compares

Direct head-to-head against 2 competitors. Picked by 7wData.

This tool

eRAG

Pricing
Basic: $2,000/month for up to 10 users (12-month min); Advanced: $5,000/month for up to 20 users (12-month min); Enterprise: custom pricing, contact required. Annual billing available at $130/user/month (Basic) or $195/user/month (Advanced).
Target
eRAG is an enterprise data interaction platform developed by GigaSpaces that enables non-technical users to query live operational databases using natural language, bypassing the need
Strength
eRAG eliminates the need for ETL pipelines or data pre-processing, allowing users to query live operational databases directly with natural language.
Watch for
The Basic plan costs $2,000 per month for up to 10 users with a 12-month minimum term, making it expensive for small teams or short-term projects.

GigaSpaces

Pricing
Custom/Contact sales
Target
Real-time operational data querying for enterprises
Deployment
On-premise or cloud
Strength
In-memory data grid for low-latency analytics
Watch for
Complex setup and high total cost of ownership

LM-Kit.NET

Pricing
Custom/Contact sales
Target
Natural language querying of structured data for developers
Deployment
On-premise or cloud
Strength
Developer-friendly SDK for custom NLQ integrations
Watch for
Limited out-of-the-box enterprise connectors

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Sources

Reporting on this tool draws on these publicly available sources.

  1. www.gigaspaces.com
  2. aws.amazon.com
  3. www.cioreview.com
  4. www.gigaspaces.com