ELT tools fail to address end-to-end data pipeline challenges by focusing solely on data ingestion (extract and load). You must still integrate these pipelines with data models, handle transformations, ensure data quality, set up automated alerts, and audit changes for end-to-end orchestration of your pipelines.
You can use Workato, DBT, and Snowflake to deliver trusted data, design pipelines that adapt to schema drift at the source, and track changes downstream. Furthermore, you can automate action in your CRM, marketing and customer experience platforms by syncing insights with Reverse ETL. By reusing existing components, you can develop scalable data delivery and build resilient pipelines that maintain data quality effortlessly, improve productivity, and reduce costs.
Join this session for real-world examples, best practices, and strategies for optimizing your data pipeline architecture.
What will you learn?
Advanced pipelines for data ingestion
Automating transformations with DBT
Monitoring, and maintaining data quality
Building resiliency with error handling
Closing the loop with Reverse ETL pipelines
The post From ingestion to insight – Rethinking data pipelines with Workato, DBT and Snowflake appeared first on Workato Product Hub.
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