Delta Lake Architecture
Delta Lake is an open-source storage layer that allows developers to build scalable and efficient data pipelines for big data workloads. Delta Lake provides reliability, performance, and flexibility to big data workflows by adding a transactional layer on top of existing data lakes. Delta Lake enables developers to store data in a versioned, append-only format and provides ACID transactions to ensure data consistency.
Delta Lake can be used with Apache Spark, which provides a scalable and distributed processing engine for big data workloads. Delta Lake can be used to store data in a variety of formats, including Parquet, Avro, and ORC, and can be accessed using standard SQL and Spark APIs.
Advantages of Using Delta Lake
Schema Evolution:
One of the key features of Delta Lake is its support for schema evolution. Delta Lake can handle changes to the schema of a table without requiring any downtime or data migrations. This allows developers to evolve their data models over time and maintain backwards compatibility with existing data.
Data Versioning
Delta Lake also provides support for data versioning. Each change to a Delta Lake table is tracked as a new version, and developers can use time-travel queries to access data at different points in time. This allows developers to debug issues in their data pipelines by accessing historical data and reproducing issues that occurred in the past.
Deduplication and automatic data optimization
Delta Lake also provides support for data deduplication and automatic data optimization. Delta Lake uses a technique called Z-ordering to optimize data storage and query performance. Z-ordering is a technique for clustering data in a way that is optimized for range queries. This allows Delta Lake to efficiently filter data and reduce the amount of data that needs to be scanned for queries.
Change data capture (CDC)
Delta Lake also provides support for change data capture (CDC). CDC is a technique for capturing changes to a data source and propagating those changes to downstream systems. Delta Lake can be used to capture changes to data sources and store those changes in a versioned, append-only format. This allows downstream systems to consume changes from Delta Lake in real-time and maintain data consistency.
Delta Lake provides three layers of functionality: the batch layer, the streaming layer, and the serving layer.
The batch layer is used for batch processing workloads. The batch layer is responsible for ingesting data from data sources, processing that data, and storing the results in Delta Lake. The batch layer can be used to process large volumes of data on a regular schedule, such as daily or hourly.
The streaming layer is used for real-time processing workloads. The streaming layer is responsible for ingesting data from data sources in real-time, processing that data, and storing the results in Delta Lake. The streaming layer can be used to process data in real-time, such as processing data from IoT sensors or monitoring social media feeds.
The serving layer is used for serving data to downstream systems. The serving layer is responsible for exposing data stored in Delta Lake to downstream systems. The serving layer can be used to serve data to web applications, data visualizations, or machine learning models.
In conclusion, Delta Lake is a powerful and flexible storage layer that enables developers to build scalable and efficient data pipelines for big data workloads. Delta Lake provides reliability, performance, and flexibility by adding a transactional layer on top of existing data lakes. Delta Lake provides support for schema evolution, data versioning, data deduplication, automatic data optimization, and change data capture. Delta Lake provides three layers of functionality: the batch layer, the streaming layer, and the serving layer, which can be used to build a variety of big data workflows.
