Data Lake has become a mainstay in data analytics architectures. By storing data in its native format, it allows organizations to defer the effort of structuring and organizing data upfront. This promotes data collection and serves as a rich platform for data analytics. Most data lakes are also backed by a distributed file system that enables massively parallel processing (MPP) and scales with even the largest of data sets. The increase of data privacy regulations and demands on governance requires a new strategy. Simple tasks such as finding, updating or deleting a record in a data lake can be difficult. It requires an understanding of the data and typically involves an inefficient process that includes re-writing the entire data set. This can lead to resource contention and interruptions in critical analytics workloads.
Apache Spark has become one of the most adopted data analytics platforms. Earlier this year, the largest contributor, Databricks, open-sourced a library called Delta Lake. Delta Lake solves the problem of resource contention and interruption by creating an optimized ACID-compliant storage repository that is fully compatible with the Spark API and sits on top of your existing data lake. Files are stored in Parquet format which makes them portable to other analytics workloads. Optimizations like partitioning, caching and data skipping are built-in so additional performance gains will be realized over native formats.
DeltaLake is not intended to replace a traditional domain modeled data warehouse. However, it is intended as an intermediate step to loosely structure and collect data. The schema can remain the same as the source system and personally identifiable data like email addresses, phone numbers, or customer IDs can easily be found and modified. Another important DeltaLake capability is Spark Structured Stream support for both ingest and data changes. This creates a unified ETL for both stream and batch while helping promote data quality.
Data Lake Lifecycle
- Ingest Data directly from the source or in a temporary storage location (Azure Blob Storage with Lifecycle Management)
- Use Spark Structured Streaming or scheduled jobs to load data into DeltaLake Table(s).
- Maintain data in DeltaLake table to keep data lake in compliance with data regulations.
- Perform analytics on files stored in data lake using DeltaLake tables in Spark or Parquet files after being put in a consistent state using the `VACCUUM` command.
Data Ingestion and Retention
The concept around data retention is to establish policies that ensure that data that cannot be retained should be automatically removed as part of the process.
By default, DeltaLake stores a change data capture history of all data modifications. There are two settings `delta.logRetentionDuration` (default interval 30 days) and `delta.deletedFileRetentionDuration` (default interval 1 week)
%sql ALTER table_name SET TBLPROPERTIES ('delta.logRetentionDuration'='interval 240 hours', 'delta.deletedFileRetentionDuration'='interval 1 hours') SHOW TBLPROPERTIES table_name
Load Data in DeltaLake
The key to DeltaLake is a SQL style `MERGE` statement that is optimized to modify only the affected files. This eliminates the need to reprocess and re-write the entire data set.
%sql MERGE INTO customers USING updates ON customers.customerId = updates. customerId WHEN MATCHED THEN UPDATE email_address = updates.email_address WHEN NOT MATCHED THEN INSERT (customerId, email_address) VALUES (updates.customerId, updates.email_address)
Maintain Data in DeltaLake
Just as data can be updated or inserted, it can be deleted as well. For example, if a list of opted_out_consumers was maintained, data from related tables can be purged.
%sql MERGE INTO customers USING opted_out_customers ON opted_out_customers.customerId = customers.customerId WHEN MATCHED THEN DELETE
In summary, Databricks DeltaLake enables organizations to continue to store data in Data Lakes even if it’s subject to privacy and data regulations. With DeltaLakes performance optimizations and open parquet storage format, data can be easily modified and accessed using familiar code and tooling. For more information, Databricks DeltaLake and Python syntax references and examples see the documentation. https://docs.databricks.com/delta/index.html