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Want to Analyze Kinesis Data in Athena?
Your continuous SQL pipeline is ready.
Simplify Your Kinesis Pipelines
Amazon Athena is the go-to for querying Kinesis data stored on S3, but querying high volumes of raw event data can be complex and expensive.
Upsolver lets you instantly launch a continuous data pipeline that transforms data from Kinesis and writes it to analytics-ready Athena tables, while storing your data as optimized Parquet on Amazon S3 and automating all data lake best practices.
Key Features and Benefits
Make your continuous data analytics-ready
Easily deploy and manage streaming data pipelines that just work, with data that’s always structured and fresh. Set it and forget it – no data plumbing required.
Build low-code pipelines using SQL and automated orchestration
Forget complex Scala coding and stop struggling with Airflow workflows. With Upsolver, building high-performance data infrastructure is as simple as SQL.
Improve query performance and costs
Avoid the high costs of reading millions of small files. Upsolver compacts, partitions and compresses your data to ensure Athena queries return quickly and cost-effectively.
Focus on analytics, not pipelines
Don’t waste your top engineering talent on writing Spark pipelines. Ingest, transform and make data available in Athena, in a fraction of the time and cost
Scale to petabytes easily
Upsolver’s continuous SQL pipelines scale to petabytes without skipping a beat, allowing your data platform to scale as fast as your business does.
Streaming joins, transformations and ingestion made easy
Upsolver makes all your biggest data engineering pains disappear: from exactly-once processing to dealing with small files to joining two streams in-flight.
Trusted by Data-Intensive Companies
“We want to minimize the time our engineering teams, including DevOps, spend on infrastructure and maximize the time spent developing features. Upsolver has saved thousands of engineering hours and significantly reduced total cost of ownership, which enables us to invest these resources in continuing our hypergrowth rather than data pipelines.”
Seva Feldman, VP of R&D
See a live pipeline example in our interactive, end-to-end demo.
- Play with an SQL data pipeline instantly
- See how you can join two streaming sources into an Athena table
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