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Low Code Data Pipelines
for Cloud Data Lakes
Speeding Delivery of Analytics-Ready Data
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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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