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.

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.
See all integrations

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

1

Create your free account

2

Connect a data source

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

3

Choose a data output target

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

4

Define transformations using Visual SQL IDE

5

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.

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.

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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Start for free with the
Upsolver Community Edition.

Build working solutions for stream and batch processing on your data lake in minutes.

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