Explore our expert-made templates & start with the right one for you.
Free eBook
6 Key Factors when Comparing Modern Data Pipeline Tools
Actionable tips for evaluating and comparing data pipeline platforms in the age of cloud data lakes.
Robust data pipelines are key to providing timely, clean data to the analytics platforms used to drive insights. There are many ETL, ELT and data transformation tools out there, but modern data challenges require modern solutions that can answer the challenges posed by complex data and the cloud data lake ecosystem.
Download our free white paper to learn:
- Differences between DAG-based and declarative data pipeline tools
- Best practices in data pipeline engineering
- How to evaluate data pipeline tools when you're dealing with streaming or complex data
- Important factors to consider such as schema evolution and integration with metadata catalogs
Get the eBook
Who should read this guide?
- Data architects and engineering leaders looking to improve data freshness and availability while reducing engineering overhead
- Data engineers who want to build more efficient ingestion, ETL and data replication flows on their data lake
- Data scientists who need self-service access semi-structured data from cloud storage using SQL
