Make your cloud data analytics-ready in days. No coding required.

Upsolver revolutionizes the way you work with cloud data. By providing a self-service, high-speed compute layer between your data lake and the analytics tools of your choice, Upsolver automates menial data pipeline work, so you can focus on developing analytics and real-time applications.

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Key Benefits

100x

faster queries against cloud storage (S3)

300x

Improvement in data freshness

95%

reduction in data lake engineering time

Leave Spark and Hadoop behind and go cloud-native with Upsolver.

OPEN SOURCE

Manage infrastructure
Coding in Scala / Java
Big data engineers only
Intensive DataOps
9-12 months to production

MANAGED SPARK

Managed service
Coding in Scala / Java
Big data engineers only
Intensive DataOps
6-9 months to production

UPSOLVER

Managed service
SQL, Python, or Visual UI
Any engineer or analyst
No DataOps
16 days to production (avg.)

Your open cloud architecture starts here.
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Upsolver makes data lakes easy

Solve your most difficult data processing challenges in just a few clicks: data ingestion, performance optimization, real-time processing and upserts into cloud storage.

How to Use Upsolver

Create your free account

Connect a data source

e.g., Amazon Kinesis, Apache Kafka, Amazon DMS

Choose a data output target

e.g., Amazon Athena, Amazon Redshift, Apache Presto

Define transformations using Visual SQL IDE

Preview and run continuously

Create your free account to get started on the Community Edition.

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Key Features

Deliver no code cloud analytics

Built-in connectors: Seamlessly ingest data from message brokers, object storage, or databases. See sources
Visual SQL IDE: Replace obtuse coding in Apache Spark or Hudi with pre-written functions or simple SQL.
Schema on read detection: Get instant insight into the data you’re storing in the lake, with no coding required.
Fully-managed processing jobs execution: Write declarative ETL without worrying about server management and orchestration.
Automatic scaling, recovery and reprocessing: Grow from terabyte to petabyte-scale without skipping a beat.

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The power to handle any use case

High throughput stream processing: Process millions of events into analytics-ready datasets in near real-time.
Stateful transformations: Joins, aggregations, deduplication: Work with cloud object storage as if it was a relational database.
Upserts and deletes in the data lake: Update, delete or insert data into S3 or Azure Storage without additional coding or tools.
Continuous performance optimization of Apache Parquet storage: Upsolver handles partitioning, compaction and compression so your queries always run super-fast.
Python UDFs: Need to go beyond SQL? Extend your data transformations with Python functions.

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Built for enterprise production at scale

Single sign on (SSO): Support for SSO with the Upsolver Professional Edition.
Role-based access control (RBAC): Ensure governance and security in your data lake dev team.
Private data never leaves your VPC: All data is stored in your cloud account (AWS S3 or Azure Data Lake Storage), with processing optionally also in your account.
Open source file storage: Scale without worrying about vendor lock-in – your data is stored in open-source Parquet and easily accessible by your entire data ecosystem.

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