Delta tables are tables managed using Delta Lake technology, which sits on top of data lakes to provide ACID (Atomicity, Consistency, Isolation, Durability) transactions. This technology is essential for handling large-scale data processing tasks, especially when working with massive datasets in cloud environments.
These tables are stored as versioned Parquet files in cloud storage, accompanied by a transaction log that tracks all changes made to the data. This versioning and transaction log mechanism ensures that Delta tables maintain historical accuracy, making it easier to manage data over time.
- Austin Libal, Trainer at Pragmatic Works
Austin Libal demonstrates how to create and manage Delta tables in Azure Synapse Analytics using Apache Spark. Below are the key steps covered in the video:
Before creating a Delta table, you need to ensure that your Synapse environment is set up correctly. Austin explains how to configure an Apache Spark pool in Synapse to work with Delta Lake. The pool needs to be running a compatible version of Apache Spark (version 3.3) that supports Delta Lake (version 2.2).
Austin shows how to connect to Azure Data Lake where your raw data is stored. He uses a sample dataset in CSV format for demonstration, but Delta tables can handle any type of data, including structured and unstructured data.
Once you’ve connected to the data, the next step is creating a Delta table. Austin demonstrates how to convert a CSV file into a Delta table by:
read function to load the file into a DataFrame.write function to save the DataFrame as a Delta table in the Data Lake.After running the code, a new folder called "Delta" is created in the Data Lake, containing the Delta table.
Inside the Delta table folder, there are two key components:
Austin demonstrates how to query the Delta table using SQL on-demand, showing how easy it is to interact with Delta tables through Synapse’s serverless SQL features.
Delta Lake also provides a "table history" feature that allows you to view the changes made to the Delta table over time. Using SQL commands, you can track all updates, deletions, and inserts, ensuring transparency and traceability of your data operations.
One of the benefits of Delta tables is their ability to handle large-scale data with high performance. Austin explains that Delta tables use optimized file formats and partitioning to improve query performance. Additionally, Delta Lake ensures data cleaning by purging old or irrelevant data using the vacuum function.
Delta Lake also performs automatic checkpointing every 10 commits to ensure efficient data handling. These checkpoints are stored in Parquet format, allowing easy reading and referencing.
Delta tables offer several benefits when used within Synapse Analytics, including:
Delta tables are an essential component of the modern data lake architecture, providing a reliable and scalable way to manage large datasets. With Azure Synapse Analytics and Delta Lake, organizations can easily create and manage Delta tables, ensuring high performance and data integrity for complex data workloads.
If you're interested in learning more about Delta Lake and Synapse Analytics, check out Pragmatic Works’ on-demand learning platform or sign up for one of our boot camps!
Don't forget to check out the Pragmatic Works' on-demand learning platform for more insightful content and training sessions on Delta Lake and other Microsoft applications. Be sure to subscribe to the Pragmatic Works YouTube channel to stay up-to-date on the latest tips and tricks.