Harnessing the Power of Delta Lake on Databricks for Robust Data Management

Introduction to Delta Lake on Databricks

ByOscar Dyremyhr
~ 4 min read

In the ever-evolving landscape of data engineering and data science, managing and processing large volumes of data efficiently is paramount. Delta Lake, an open-source storage layer that brings reliability to Data Lakes, has emerged as a robust solution for handling big data challenges. In this blog post, we will explore how you can leverage Delta Lake on Databricks to enhance your data management and analytics capabilities.

What is Delta Lake?

Delta Lake is a storage layer that runs on top of your existing data lake and is fully compatible with Apache Spark APIs. It provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. Delta Lake runs on top of your existing data lake and fully leverages Spark’s distributed processing power.

Key Features of Delta Lake

  • ACID Transactions: Ensures data integrity with ACID transactions on Spark.
  • Scalable Metadata Handling: Handles millions of files, providing a quick response time to operations like read, write, and metadata.
  • Time Travel (Data Versioning): Tracks historical data enabling rollbacks, audits, and reproductions.
  • Schema Enforcement: Automatically enforces schema to ensure data quality.
  • Unified Batch and Streaming Sink and Source: A table in Delta Lake can be a batch table, a streaming source, and a streaming sink simultaneously.

Getting Started with Delta Lake on Databricks

To demonstrate how to use Delta Lake, we’ll walk through a Python code example where we’ll set up a Delta table, perform some data operations, and query the data using Databricks.

Prerequisites Before we start, make sure you have:

  • A Databricks workspace.
  • A cluster running on Databricks with Delta Lake support.

Step 1: Setting up a Delta Table

Let’s create a Delta table from a sample data frame:

1# Import necessary libraries 2from pyspark.sql import SparkSession 3from pyspark.sql.functions import * 4 5# Start a Spark session 6spark = SparkSession.builder.appName("DeltaLakeExample").getOrCreate() 7 8# Sample data 9data = [ 10 (1, "Chicago", 3.0), 11 (2, "San Francisco", 5.0), 12 (3, "New York", 6.1) 13] 14 15# Create a DataFrame 16df = spark.createDataFrame(data, ["id", "city", "rating"]) 17 18# Write the DataFrame as a Delta Lake table 19df.write.format("delta").save("/delta/events") 20

Step 2: Reading from the Delta Table

Reading data from the Delta table is as simple as reading a regular Spark DataFrame:

1# Load the data back as a Delta Lake table 2df ="delta").load("/delta/events") 3 4# Show the data 6

Step 3: Modifying Data and Time Travel

Delta Lake allows you to modify the table with full transactional integrity.

1# Update data in the Delta table 2df_updated = df.withColumn("rating", col("rating") + 1) 3df_updated.write.format("delta").mode("overwrite").save("/delta/events") 4 5# Read the updated data 6df_updated ="delta").load("/delta/events") 8 9# Time travel to previous version 10df_version0 ="delta")\ 11 .option("versionAsOf", 0).load("/delta/events") 13

Step 4: Querying with SQL

You can also run SQL queries directly on Delta tables using Databricks:

1# Register the Delta table as a SQL table 2spark.sql("CREATE TABLE events USING DELTA LOCATION '/delta/events'") 3 4# Run SQL query 5spark.sql("SELECT * FROM events WHERE city = 'Chicago'").show() 6

Step 5: Streaming Data into Delta Lake

Delta Lake seamlessly integrates with Spark Structured Streaming.

1# Define the streaming DataFrame 2streaming_df = spark.readStream.format("rate").load() 3 4# Stream data into the Delta table 5query = ( 6 streaming_df 7 .writeStream 8 .format("delta") 9 .option("checkpointLocation", "/delta/events/_checkpoints") 10 .outputMode("append") 11 .start("/delta/events") 12) 13 14# Remember to stop the stream when you're done 15query.stop() 16


Delta Lake provides a rich set of features that can significantly enhance your data management and analytics workflows on Databricks. Whether you are dealing with batch or streaming data, Delta Lake ensures consistency, reliability, and performance.

By integrating Delta Lake into your Databricks environment, you can overcome common challenges associated with big data processing and achieve seamless, transactional data operations. With its robust tooling and native integration with Apache Spark, Delta Lake is a compelling choice for data engineers and scientists looking to streamline their data pipelines.

Start experimenting with Delta! Now that you have gathered intel on Delta Lake why not get a Lakehouse Fundamental badge 😃

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