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As data continues to dominate modern business processes, the use of real-time SQL streaming and data processing becomes increasingly critical. In this post, we explore how Databricks’ Delta Lake can facilitate this need through hands-on exercises and SQL code examples.
What is Databricks’ Delta Lake?
Databricks’ Delta Lake is a unified data management system that brings reliability and performance to your data lakes. It allows processing of massive amounts of data, often in petabytes, with SQL queries, and supports popular stream-batch unified frameworks like Apache Spark.
Setting up Delta Lake
Breathe easy knowing that Delta Lake can be setup on Databricks workspace in just a few easy steps. Here’s how:
Step 1: Create a Databricks workspace
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-- Assuming you have the Databricks CLI set up, use the following command to create a cluster: databricks clusters create --json ' { "cluster_name": "delta-lake", "spark_version": "8.2.x-scala2.12", "node_type_id": "i3.xlarge", "autotermination_minutes": 120, "enable_elastic_disk": true }' |
Step 2: Install Delta Lake
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-- To install Delta Lake on Databricks, create a library with the Maven coordinates `io.delta:delta-core_2.12:<version>`, and attach the library to your cluster. |
Getting Started with SQL Streaming and Delta Lake
Now that you’ve set up Delta Lake, let’s dive into how we can process streaming data with SQL in real-time.
Step 1: Ingesting data in delta format
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-- This code shows an example of reading data in CSV format from a location and storing it in delta format in a defined location. The data includes records of products reviews. CREATE TABLE productReviews ( review_id STRING, product_id STRING, review_date TIMESTAMP, review_body STRING ) USING delta LOCATION '/delta/productReviews' |
Step 2: Creating Streaming Data
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-- This code creates a stream from the product review data. The stream will update every time new data is written to the /delta/productReviews path. CREATE OR REPLACE STREAM product_review_stream USING delta OPTIONS ( 'streamName' 'productReviewUpdates', 'path' '/delta/productReviews' ) |
Step 3: Querying Streaming Data
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-- This SQL query will listen to the stream and compute the count of reviews in real-time. SELECT COUNT(*) FROM product_review_stream |
Wrap Up
Databricks’ Delta Lake provides an efficient and effective solution for managing large volumes of data and leveraging the power of SQL for real-time processing and analysis. With these hands-on exercises, you now have the basic know-how to implement real-time SQL data streaming using this robust tool.
Remember, practice makes perfect. So, dive in, explore, and perfect your SQL streaming data processing skills with Delta Lake!