Delta Lake - datacouch-io/spark-java GitHub Wiki

Delta Lake is an open-format storage layer designed to bring structure and governance to an organization's data lake while seamlessly integrating with Apache Spark. It offers ACID (Atomicity, Consistency, Isolation, Durability) compliance, making it a reliable choice for data storage and processing.

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Challenges of Traditional Data Lakes

While data lakes provide flexibility, cost-effectiveness, and the ability to store various data types, they come with their own set of challenges:

  1. Data Swamps: Over time, data lakes can become cluttered, making them challenging to manage and navigate effectively.

  2. Data Consistency: Ensuring data consistency during read and write operations can be unreliable and often requires complex workarounds.

  3. Job Failures: Jobs may fail midway, making it difficult to recover the correct state of the data and wasting valuable time on troubleshooting.

  4. Data Modification: Traditional data lakes were designed for write-once, read-many use cases, making data modification or deletion complex.

  5. Historical Versioning: Keeping historical versions of data can be expensive and complex, particularly with large-scale data.

  6. Large Metadata: Handling large metadata can lead to processing delays and increased overhead costs.

  7. File Proliferation: Query performance can suffer when data is distributed across too many small files.

  8. Performance Tuning: Achieving optimal performance in traditional data lakes often requires extensive job tuning.

  9. Data Quality: Traditional data lakes lack built-in data quality checks, which can lead to costly and inaccurate analysis results.

How Delta Lake Addresses These Challenges

Delta Lake resolves these challenges with the following features and capabilities:

  1. ACID Transactions: Delta Lake ensures that each transaction is atomic, consistent, isolated, and durable. Data is appended with each write, creating new versions of the table. New data is only visible after a transaction is complete, and failed jobs can be discarded.

  2. Schema Management: Delta Lake allows you to specify and enforce schemas, validating them during writes. It throws exceptions if extra data is added and enables changes to table schemas.

  3. Scalable Metadata Handling: Metadata processing is distributed, similar to regular data processing.

  4. Unified Batch and Streaming Data: Delta Lake supports both streaming and batch processes, creating a new table version for each micro-batch transaction.

  5. Data Versioning and Time Travel: Historical data versions are maintained and efficiently processed using Spark to scan transaction logs.

Why Choose Delta Lake

Delta Lake brings the structure and governance of data warehouses to data lakes, laying the foundation for a Lakehouse—a unified platform for structured and unstructured data.

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Key Features of Delta Lake

  • ACID Transactions: Delta Lake supports ACID transactions on Apache Spark, ensuring data integrity.
  • Scalable Metadata Handling: It efficiently manages metadata, handling both data and metadata as part of distributed processing.
  • Streaming and Batch Unification: Delta Lake seamlessly integrates batch and streaming processes, maintaining data consistency.
  • Schema Enforcement: Schema validation during writes prevents unexpected data changes.
  • Time Travel: Historical data versions are accessible for auditing and analysis.
  • Upserts and Deletes: Delta Lake supports upserts and deletes, allowing for data updates and removals.
  • Fully Configurable/Optimizable: Users have flexibility to configure and optimize Delta Lake according to their needs.
  • Structured Streaming Support: It is compatible with structured streaming for real-time data processing.

Delta Lake Storage Layer

Delta Lake's storage layer is highly performant and ensures data persistence. It leverages low-cost, easily scalable object storage while guaranteeing data consistency and maintaining flexibility.

Elements of Delta Lake

Delta Files

Delta Files use Parquet files to store customer data and provide additional functionality such as data versioning, metadata storage, transaction logs, and ACID transactions.

Delta Tables

Delta Tables are collections of data maintained using Delta Lake technology. They consist of Delta Files containing data stored in object storage, Delta Tables registered in a Metastore, and delta transaction logs saved alongside Delta Files in object storage.

Delta Optimization Engine

The Delta Engine is a high-performance query engine designed to efficiently process data within data lakes.

Delta Transaction Log

The Delta transaction log is an ordered record of transactions performed on a Delta table. It serves as a single source of truth for the table and guarantees atomicity during data operations.