Table of Contents
What is the Delta Lake Architecture and What are its Key Components?
- Storage Layer: This is the layer where all the data in Delta Lake gets stored. This can be Azure Data Lake Storage, Amazon S3, Databricks File System (DBFS), Google Cloud Storage, Hadoop Distributed File System (HDFS), or other compatible storage solutions. The Delta Lake architecture is built on top of these storage systems.
- Delta Table: The Delta Table is Delta Lake’s transactional table that allows large-scale data processing. Its columnar format organizes data into directories of Parquet files. Delta tables support schema evolution, time-travel, and ACID transactions.
- Delta Log: All operations in a Delta Table and the changes made are logged into the Delta Log. This transaction log enables features like versioning, rollback, and time-travel.
- Metadata: Delta Lake maintains metadata about the Delta table, including information about schema, partitions, and statistics. This metadata is stored alongside the data and is used to optimize query performance
Data Lake vs. Delta Lake – A Detailed Comparison and How is Delta Lake on Databricks a Better Choice?
How Does the Delta Lake Architecture Work?
The ingested data in Delta Lake is stored in the Apache parquet format, a columnar storage format, for optimal compression and fast, parallel reads. Its open APIs allow full compatibility with Apache Spark, the most used big data processing engine, supporting concurrent data access.
All changes made in the Delta Table get logged in the Delta Logs.
The architecture’s support for versioning empowers users with time-travel capabilities. It allows access to historical data snapshots, helping in auditing, debugging, and recovering from errors.
Queries on the Delta Table can be performed using SQL or other programming languages. The compaction and data-skipping mechanisms significantly improve query performance by skipping irrelevant files during query execution.
The Delta Lake architecture also supports schema evolution for table structure modifications. This allows users to make changes to the table schema over time without requiring a full rewrite of the data. This flexibility is essential for accommodating evolving data structures. Additionally, the Delta Lake architecture also supports schema enforcement to ensure that the new data is as per the expected schema.
Integral to Delta Lake, ACID transactions (Atomicity, Consistency, Isolation, and Durability) maintain data integrity within data lakes, contributing to the reliability and robustness of the architecture.
These features collectively fulfill the modern big data needs of today’s businesses.
Top 3 Benefits of Delta Lake Architecture
- Cloud-native: The Delta Lake architecture is inherently cloud-native, offering the scalability, flexibility, and cost-effectiveness of Cloud.
- Schema Enforcement and Evolution: Its robust support for schema enforcement and evolution offers organizations unparalleled flexibility to adapt to evolving business needs and data structures.
- ACID Transactions for Data Integrity: Delta Lake architecture’s full ACID compliance for transactions contributes to the overall trustworthiness of the data, a critical factor for analytics, reporting, and other data-driven applications.
Use Cases for Delta Lake
- Real-time Analytics: With the integration of machine learning and AI into data management processes becoming more mainstream, an increasing number of organizations turn to the Delta Lake architecture. Its unified approach to batch and streaming processing allows organizations to seamlessly integrate real-time data pipelines with traditional batch operations. This versatility is particularly beneficial for industries requiring real-time or near-real-time analytics, as Delta Lake offers timely and actionable insights through speedy processing of data.
- Data Governance: Also, for industries that are bound by stringent regulatory requirements, Delta Lake’s built-in tools help strengthen data governance initiatives to maintain data integrity and compliance.
- Data Lakehouse: Delta Lake allows enterprises to build a data lakehouse, combining the advantages of data warehousing and machine learning within the data lake. This integration becomes beneficial for analytics, reporting, and machine learning applications.
- Change Data Capture (CDC): In a Delta Lake architecture, users can easily employ Change Data Capture, capturing and tracking the changes made to the data over time. This becomes particularly helpful in analyzing financial transactions or monitoring user activities.
Find out how a top audit firm leverages Delta Lake to add value to its business processes.
The Delta Lake architecture indeed has a lot to offer, and it is a crucial component in establishing a robust and resilient data management environment. This comprehensive solution for modern data management addresses key data challenges with its transactional consistency, versatile processing capabilities, and adaptability to evolving data schemas.
If you are looking to leverage the full potential of Delta Lake architecture, our experts can guide you in the right direction. KANINI has helped enterprises migrate their existing data from a traditional data lake to Delta Lake through careful planning and execution. Our experts have also resolved the challenges around Code Refactoring and training the teams to fill the skill gap.
Speak to us to learn how you can derive maximum value from your data and unlock new opportunities for agility and innovation.
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