Rethink how you refine your pipeline. Join the discussion focused on turning raw CI/CD data into metrics that show efficiency levels.
Data quality isn't just a technical issue: It impacts an organization's compliance, operational efficiency, and customer satisfaction.
Apache Sqoop was designed for bulk transfer of data from relational databases into Hadoop. It provides an easy and economical way for organizations that are just getting started with Data Lake initiatives.
But as organizations grow to larger deployments, they quickly hit the scalability, latency and efficiency limitations of this open-source tool. In this webinar, we'll explore the architecture and use cases for change data capture (CDC), which more and more enterprises are implementing to close the Sqoop gap. This software solution continuously identifies and captures incremental data changes from a variety of sources into data lakes, where data is transformed and delivered for analytics. Designed and implemented effectively, CDC can meet the scalability, efficiency, real-time and zero-impact requirements of modern data architectures.
Attend to learn:
- To Sqoop or not to Sqoop - when to introduce CDC to your data environment
- Scalability, latency and efficiency limitations of Sqoop
- Data Lake Ingestion Maturity Model
- Common CDC use cases
- Capabilities and value of Attunity Replicate CDC
Additionally, we'll present a live product demonstration of Attunity Replicate with CDC technology, highlighting data ingestion from Oracle to Hadoop.
