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.
Extract, transform, and combine data from AWS S3 with other sources into an analysis-ready data warehouse.
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.
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.
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.
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.
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.
There is no data warehouse administration needed. Etleap automatically configures tables and schemas in Redshift or Snowflake to support the S3 data, and detects and resolves pipeline issues such as schema changes and parsing errors.
“Just as we’ve moved from on-prem to AWS because we don’t want to be in the business of building data centers, we’re leveraging Etleap because we don’t want to be in the business of building data pipelines.”
