A Lakehouse Implementation using Delta Lake

Categories: Digital TransformationBig Data & AnalyticsConsumer and RetailTechnology

Author

Arun

Author

Arun Viswanathan

Principal Architect

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Abstract

A data lake is a centralized repository that enables a cost–effective storage of large volumes of data that provides a single source of truth (SOT). However, organizations face numerous challenges when using data lakes built on top of cloud-native storage solutions. These challenges include a lack of data consistency, unreliable data due to incomplete and corrupt files, performance issues, and the absence of schema enforcement and validation. One of the popular implementations of Lakehouse architecture
is Databricks’ Delta Lake which overcomes these challenges with an open-source storage layer built on top of existing data lake file storage formats such as Apache Parquet. We will first explore the differences between the architectures associated with data warehouses, data lakes, and lakehouses.

Then, we take a glimpse under the hood to understand the inner workings of Delta Lake architecture.

Finally, this white paper provides insights into how Delta Lake offers solutions to common problems encountered with data lakes such as ensuring data integrity with ACID transactions, providing scalable metadata management with distributed processing, data versioning with time travel, or preventing data corruption with schema enforcement

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