Optimize Costs by Migrating ELT from Cloud Data Warehouses to Data Lakehouses
Blog post from Onehouse
As organizations increasingly utilize cloud warehouse engines like Snowflake, Amazon Redshift, and Google BigQuery for ELT (extract, load, and transform) operations, many are unaware of the potential cost savings and flexibility offered by a data lakehouse approach. The lakehouse architecture, which integrates the strengths of both data lakes and data warehouses, allows for the storage and analysis of a wide range of data types and supports various analytics capabilities, including BI, machine learning, and AI. By performing ELT operations in a data lakehouse, such as one powered by Apache Hudi, organizations can achieve significant cost reductions and enhanced performance. A real customer case highlighted a potential $960K annual saving by transitioning from a cloud data warehouse to a Onehouse data lakehouse, reducing data latency and improving data processing capabilities. This transformation not only offers cost benefits but also provides a unified and interoperable data architecture, ensuring a reliable source of truth and future-proofing against vendor lock-in. This approach is gaining traction as a scalable, efficient solution for modern data processing needs.
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