Streamliner is a Data Pipeline Automation tool that simplifies the process of ingesting data onto a new platform. It is not a data ingestion tool in and of itself; rather, it automates other commonly used tools that ingest and manipulate data on platforms like Snowflake, Amazon Redshift, Cloudera, and Databricks.
Streamliner uses SchemaCrawler to query database table metadata through JDBC. It then generates pipeline code from a series of templates, which can be used to ingest or transform data.
For example, in order to perform Change Data Capture (CDC) -based continuous replication from a source system to Snowflake, Streamliner could be used to generate the SnowSQL needed to create the required Snowpipe tasks, staging tables, and Snowflake Streams, and merge tasks.
Alternatively, Streamliner might also be used in a very different use case to facilitate data ingestion from relational databases (DBMS) to Hadoop by generating the necessary Sqoop ingest scripts and Impala merge statements.
Why use Streamliner #
- Ingest hundreds or thousands of data sources quickly and easily
- Quickly develop highly complex, templated, and reusable data pipelines into Snowflake, Amazon Redshift, Cloudera, and Databricks.
- Automate the ingestion of business and technical metadata, and the generation of data catalog artifacts, including documentation, ERDs, and integration code.
- Quickly respond to changing requirements like new columns, changing metadata, and new data sources.