S3 ETL with Etleap

Extract, transform, and combine data from AWS S3 with other sources into an analysis-ready data warehouse.

01 Challenges with S3 ETL 02 ETL data from S3 with Etleap

Inconsistent

Since S3 can store any type of file, there is no consistency in type, format, or length of available data. ETL scripts or data sync tools made to handle one format will stop working if the format changes.

Unpredictable

S3 is often used as a repository for third-party data such as vendor-provided data, application logs, and system reports. Those data sources may change output formatting at any time without notification, causing downstream errors in your ETL scripts.

Unstructured

Before data from S3 can be analyzed with SQL, BI, or data science tools it needs to be transformed to fit into database tables. Doing this manually requires required designing schemas and then building and maintaining transformation scripts.

Extract

Collect data from an entire S3 bucket or choose specific directories. Etleap will load existing files in that directory and then monitor for new files to process.

Transform

The Etleap transformation engine will analyze data structures of the files within S3 and automatically generate a transformation script to convert them into analysis-ready data.

Load

There is no data warehouse administration needed. Etleap automatically configures tables and schemas in Redshift to support the S3 data, and detects and resolves pipeline issues such as schema changes and parsing errors.

“Instead of waiting weeks or months to access data — or never getting it at all — analysts can now get it within hours.”

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