Low Code Data Pipelines
for Cloud Data Lakes

Speeding Delivery of Analytics-Ready Data

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Powering over 10,000 Big Data Ingestion Pipelines

Eliminate the Complexity of Data Pipeline Engineering

  • Reduce 90% of ETL and custom pipeline development
  • Deliver queryable data from streaming & batch sources
  • Scale pipelines from GB’s to PB’s with near-zero IT
  • Lower cloud compute & engineering costs by 50-75%

Build High-Performance, Declarative Data Pipelines with a Visual IDE

  • Define pipelines using only SQL on auto-generated schema-on-read
  • Easy visual IDE to accelerate building pipelines
  • Add Upserts and Deletes to data lake tables
  • Blend streaming and large-scale batch data
  • Automated schema evolution and reprocessing from previous state

Automate Pipeline Orchestration and Data Lake Table Management

  • Automatic orchestration of pipelines (no DAGs)
  • Fully-managed execution at scale
  • Strong consistency guarantee over object storage
  • Near-zero maintenance overhead for analytics-ready data
  • Built-in hygiene for data lake tables including columnar formats, partitioning, compaction and vacuuming

High Performance at Scale on Complex Data

  • 100,000 events per second (billions daily) at low cost
  • Continuous lock-free compaction to avoid “small files” problem
  • Parquet-based tables for fast queries
  • Enables low-latency dimension tables using streaming upserts

UI-Driven Ingestion from Streams, Files, Databases

  • Automatic schema-on-read and data profiling
  • Data lineage visibility from source to lake to target
  • Streaming data from Kafka, Amazon Kinesis and Azure Event Hub
  • Transactional data from databases using JDBC or CDC
  • Logs and files from cloud object storage such as Amazon S3, Google Cloud Storage or Azure Data Lake Storage

Continuously Serve Data to Lakes, Warehouses, Databases and Streaming Systems

  • Integration with lake query engines: Amazon Athena, Redshift Spectrum, Dremio, Starburst, Presto, Trino & more
  • Cloud data platforms including Redshift, Snowflake and Synapse
  • Databases such as ElasticSearch, MySQL and PostgresDB
  • Write to Amazon S3, Google Cloud, Azure Data Lake Storage
  • Stream processing engines: Amazon Kinesis, Kafka, Azure Event Hub

50-75% Lower Cloud Compute and Data Engineering Costs

  • Automated use of low cost Spot instances
  • Automated use of low cost cloud object storage
  • Continuous, high-integrity table management
  • Automated vacuum of stale and intermediate data

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Customer Stories

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Integrations and Connectors

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All Templates

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