Rivery, the complete data integration platform accelerating data pipeline creation, announced the launch of two new integrations of its comprehensive ELT platform with Amazon Q and Snowflake. The integrations will help enterprises use their internal data to effectively develop specially designed AI tools, and enable fresh and complete business insights.
“With the launch of these integrations, Rivery continues to enable seamless data management across platforms, empowering organizations to deploy powerful AI applications with fewer hallucinations and derive actionable insights,” said Rivery CEO Itamar Ben Hemo. “With Snowflake as a Source and Amazon Q integration, we’re enabling our users to unlock the full potential of their data — from streamlining migrations to building data-driven, GenAI solutions that meet today’s business needs with flexibility and security.”
Amazon Q Integration
Rivery’s integration with Amazon Q allows users to create personalized GenAI chat assistants based on the full range of data available within the enterprise. Large language models trained on the internet don’t tap into proprietary data, lack security, and are highly susceptible to hallucinations. By quickly and easily leveraging Retrieval Augmented Generation (RAG) workflows in Amazon Q, companies can set up enterprise-specific GenAI chatbots that can securely answer questions, provide summaries, and generate content with fewer hallucinations.
Capabilities
- Sync all enterprise data sources to create effective AI apps based on a full range of internal data
- Easily prep enterprise data for LLM usage with an optimized structure to handle RAG workflows
- Trigger Amazon Q syncs that ensure users’ AI apps run on the freshest data
- Build data flows securely with users’ own S3 file zones without exposing data to third parties
Rivery Snowflake as a Source
Rivery Snowflake as a Source empowers data engineers and analysts to seamlessly replicate or migrate data from Snowflake to simplify cross-platform data management. Organizations can migrate and sync data as needed, regardless of the data’s source or destination. This is particularly significant given that data from warehouses is increasingly fed back into operational systems to enable more informed decision-making with tighter integration between data insights and business operations.
Capabilities
- Syncing data between multiple data warehouses
- Easily sync datasets across platforms such as BigQuery, Databricks Lakehouse, Amazon Redshift, or between Snowflake accounts
- Migrating data from Snowflake to another data lake or data warehouse
- Accelerate complex migrations by implementing an easily deployed lift and shift approach
- Activate Snowflake data in transactional databases or AI applications
- Simplify data activation or reverse ETL and sync data into AI applications such as Amazon Bedrock. Snowflake data can be replicated into an Amazon S3 dedicated dataset to serve downstream AI applications
“Our team needed to migrate Google Search Ads 360 data to Snowflake and consolidate with older data from the same source, already stored in Snowflake,” said Destinei Simpson, a data analyst at digital marketing agency Good Apple. “With Rivery Snowflake as a Source, it took us just a few clicks to set up a pipeline to replicate the data from one Snowflake dataset into another that would have otherwise required manual scripting and management. This made our job much easier and helped speed up our delivery time dramatically.”
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