Interact with Amazon S3 using SQL with BryteFlow Blend

Key Features

Choices of destinations

Persist Data Assets in Amazon S3 and optionally export data assets to Amazon Redshift, Amazon Aurora or Snowflake.

Versioning of SQL

SQL statements integrated with AWS CodeCommit for version control

Simple flow chart interface

Data Preparation on your BryteFlow Data Lake on Amazon S3 with a self-service point-and-click workbench to select, join and transform data.

Integrate with Amazon cloudwatch logs

Get monitoring and alerting capabilities through integration with Amazon CloudWatch Logs.

Handshaking with BryteFlow ingest

Integrates with BryteFlow Ingest to run data transformation jobs when required.

View landed Amazon S3 data as tables

View Amazon S3 data from within the workbench.

Full metadata and data lineage

All data assets will have automated metadata and data lineage.

Classification of sensitive data

Blend allows users to see the data they have access to.

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