Batch and Streaming Data Lake ETL with SQL

Extract, transform and load live data streams and historical big data into Athena, Redshift, ElasticSearch and RDS - with familiar ANSI SQL. Your data lake has never been more accessible.

Key Benefits and Feature Highlights

Fully Secure

Self-service for
data consumers

Data analysts and developers can build streaming data pipelines. No Spark/Hadoop required.

Elastic Scaling

Frictionless
data preparation

Easily prepare terabytes to petabytes of data for analysis, including joins between multiple streams.

Shockingly Easy

Instant access to
fresh data

Write your SQL once to continuously get data that is always stays fresh, up-to-date and optimized.

Unlock the value of your data.

Prepare to be astounded by how easy it is to prepare your data streaming.

Feature Highlights

Complex transformations in a familiar syntax

Need to analyze streaming data? Forget about writing hundreds of lines in Scala, clusters and workflow orchestration. If you know SQL, you’re good to go – including native support for nested data, rolling aggregations and window-based joins.

Complex transformations in a familiar syntax

High cardinality joins between streams and data lake storage

With Upsolver, you have a single, SQL-based platform for all your ETL – batch, micro-batch and real-time – and for all your historical and live streaming data, leveraging Upsolver’s unique data lake indexing.

High cardinality joins between streams and data lake storage

Fresh, up-to-the-minute data without ever updating your query

Write your SQL once and get low-latency data forever. Your query will return fresh data even as your data changes over time, with live data from Kafka available in Athena within seconds to minutes.

up-to-the-minute data without ever updating your query

Scalable, repeatable workflows

Copy and paste SQL to quickly iterate and create new data pipelines at the speed of your business.

Scalable, repeatable workflows

Optimized data for consumption with no additional effort

Focus on your analytic workflows while Upsolver does all the data engineering magic under the hood: partitioning, compaction, indexing, and more.

Optimized data for consumption with no additional effort

Learn more about Upsolver SQL:

Joining Impression and Click Streams in Minutes using only SQL

Read the Case Study »

Joining Impression and Click Streams in Minutes using only SQL

Read the Case Study »

Joining Impression and Click Streams in Minutes using only SQL

Read the Case Study »

Joining Impression and Click Streams in Minutes using only SQL

Read the Case Study »
data lake ETL Demo

Let’s get personal:
See Upsolver on your data in a live demo.

Schedule a free, no-strings-attached demo to discover how Upsolver can radically simplify data lake ETL in your organization.

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