Unlocking Delta Lake Insights with Sling: Efficient Read-Only Access

In the ever-evolving landscape of big data, Delta Lake has emerged as a powerful open-source storage layer that brings reliability and performance to data lakes. Today, we're thrilled to announce that Sling, our versatile data integration tool, now supports reading Delta Lake format, opening up new avenues for data engineers and analysts to harness the power of Delta tables.

What is Sling?

Sling aims to augment the exporting/loading data process into a positive and potentially enjoyable experience. It offers both CLI and YAML-based configurations for easy setup and management of data flows, by focusing on 3 data types interfaces:

• From File Systems to Databases

• From Databases to Databases

• From Databases to File Systems

The list of connections that Sling supports continues to grow. You can see the full list here, but it supports all the major platforms including Clickhouse, DuckDB, Google BigQuery, Google BigTable, MariaDB, MongoDB, MotherDuck, MySQL, Oracle, PostgreSQL, Prometheus, Redshift, Snowflake, SQL Server, SQLite, StarRocks and Trino.

Delta Lake: A Game-Changer in Data Lakes

Delta Lake, developed by Databricks, addresses many of the challenges faced by traditional data lakes. It introduces ACID transactions, scalable metadata handling, and time travel capabilities to big data workloads. These features make Delta Lake an attractive choice for organizations dealing with large-scale data processing and analytics.

Key benefits of Delta Lake include:

1. ACID Transactions: Ensures data consistency even with concurrent reads and writes.
2. Schema Evolution and Enforcement: Allows for easy schema changes and maintains data quality.
3. Time Travel: Enables querying data as it existed at a specific point in time.
4. Unified Batch and Streaming: Seamlessly handles both batch and real-time data processing.
5. Optimized Performance: Leverages various optimizations for faster queries on large datasets.

Sling's Delta Lake Integration: Read-Only Power

Sling now offers robust support for reading Delta Lake tables, leveraging the power of DuckDB under the hood. This integration allows users to easily incorporate Delta Lake data into their existing data pipelines and analytics workflows.

It's important to note that Sling's current implementation is read-only. While you can't write or modify Delta tables using Sling, you can efficiently extract data from Delta Lake for further processing or analysis.

How Sling Reads Delta Tables

Sling utilizes DuckDB's Delta Lake reader to efficiently process Delta tables. Here's a brief overview of how it works:

1. DuckDB Integration: Sling uses DuckDB's built-in Delta reader, allowing for direct querying of Delta tables without additional dependencies.
2. Delta Scan Function: Sling leverages DuckDB's delta_scan function to read Delta metadata and data files.
3. Query Optimization: Sling constructs optimized SQL queries using the delta_scan function, ensuring efficient data retrieval.
4. Streaming Results: Results are streamed from DuckDB, enabling efficient processing of large Delta tables without loading the entire dataset into memory.

Using Sling with Delta Lake: Practical Examples

Let's explore how you can use Sling to read Delta Lake tables in various scenarios.

Reading Delta Tables with Sling CLI

To read Delta Lake files using Sling's command-line interface, you can use the following commands:

# Read a local Delta table
sling run --src-stream file://path/to/table \
  --src-options '{format: delta}' \
  --stdout --limit 100

# Read a Delta table from AWS S3
sling run --src-conn aws_s3 \
  --src-stream path/to/table \
  --src-options '{format: delta}' \
  --stdout --limit 100

These commands will read the specified Delta table and output the first 100 rows to the console.

Incorporating Delta Lake in Replication YAML

For more complex data integration tasks, you can specify Delta as a format in your replication.yaml file:

source: aws_s3
target: postgres

defaults:
  mode: full-refresh
  source_options:
    format: delta

streams:
  path/to/delta/table:
    object: my_schema.delta_table

  path/to/delta_tables/*:
    object: my_schema.{stream_file_name}

This configuration reads data from Delta tables stored in AWS S3 and loads it into PostgreSQL tables. The full-refresh mode indicates that the target table will be completely replaced with the data from the source Delta table during each replication run.

To execute the replication, use:

sling run -d -r replication.yaml

See docs here to get started with Sling!

Real-World Use Case: Analytics on E-commerce Data

Imagine you're working with a large e-commerce platform that stores its transaction data in Delta format on AWS S3. You need to perform daily analytics on this data using your PostgreSQL data warehouse. Here's how you could use Sling to streamline this process:

1. Set up a replication YAML file to read from your Delta tables and write to PostgreSQL.
2. Schedule daily Sling runs to keep your analytics database up-to-date.
3. Leverage Delta Lake's time travel feature by specifying a timestamp in your Sling configuration to analyze historical data.
4. Use Sling's column selection feature to optimize data transfer by only reading the columns you need for your analytics.

This setup allows you to take advantage of Delta Lake's reliability and performance while using Sling's simplicity and flexibility for your data integration needs.

Conclusion

Sling's new capability to read Delta Lake format opens up exciting possibilities for data engineers and analysts. By combining Delta Lake's robust features with Sling's efficient data integration capabilities, organizations can streamline their data workflows and gain valuable insights from their data lakes.

While the current implementation is read-only, it provides a powerful tool for extracting and analyzing data stored in Delta format. As we continue to develop Sling, we're excited about the potential for expanding our Delta Lake support in the future via DuckDB.