Interact with Amazon S3 using SQL with BryteFlow Blend
SQL Based Data Management
Run and schedule complex Hadoop/SPARK data transformations by simply using SQL, no PySpark coding required.
Workflow Automation
Run complex jobs and orchestrate your dependencies using SQL and integration with Amazon Simple Notification Service (SNS).
Data-as-a-service
Build an environment of analytics ready data assets for consumers of data.
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.
