Databricks hits $188bn valuation in Coatue-led round, landing above its own $175bn talks

Introduction

In the wake of Databricks' recent $188 billion valuation, it's clear that the company's data platform technology is at the forefront of the modern data ecosystem. This tutorial will guide you through setting up and using Databricks' Delta Lake, a key component of their platform that enables scalable data warehousing and analytics. You'll learn how to create a Delta table, perform data operations, and understand how this technology supports the kind of large-scale data processing that makes companies like Databricks so valuable.

Prerequisites

• A Databricks workspace (you can sign up for a free trial at databricks.com/try-databricks)
• Basic understanding of Python and SQL
• Access to a Databricks cluster with Python or SQL notebook
• Some familiarity with data lakes and data warehousing concepts

Step-by-Step Instructions

1. Set Up Your Databricks Environment

First, ensure you have access to a Databricks workspace with a cluster running. Create a new notebook in your workspace and select Python as your language. This environment will allow us to interact with Delta Lake tables using Python APIs.

2. Install Required Libraries

While Databricks clusters typically come with Delta Lake pre-installed, it's good to verify and ensure we have the right version. Run the following command in a notebook cell:

%pip install delta-spark

This installs the Delta Lake library that provides the APIs for working with Delta tables.

3. Create Sample Data

Before creating a Delta table, we need some data. Let's create a simple dataset using Python:

import pandas as pd

data = {
    'id': [1, 2, 3, 4, 5],
    'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
    'age': [25, 30, 35, 40, 45],
    'salary': [50000, 60000, 70000, 80000, 90000]
}

# Create a Pandas DataFrame
pandas_df = pd.DataFrame(data)

# Display the data
pandas_df

This creates a simple employee dataset that we'll use to demonstrate Delta Lake functionality.

4. Write Data to Delta Table

Now we'll write this data to a Delta table. Delta Lake tables are optimized for analytics and provide ACID transactions, data versioning, and schema enforcement:

from pyspark.sql import SparkSession

# Create a Spark DataFrame from Pandas DataFrame
spark_df = spark.createDataFrame(pandas_df)

# Write to Delta table
spark_df.write.format('delta').mode('overwrite').save('/delta/employees')

print('Data written to Delta table successfully')

The delta format tells Spark to use Delta Lake, and the overwrite mode ensures we're starting fresh. The path /delta/employees is a Databricks filesystem path where the table will be stored.

5. Read and Query Delta Table

After writing the data, we can read it back and perform queries:

# Read the Delta table back
read_df = spark.read.format('delta').load('/delta/employees')

# Display the table
read_df.show()

# Perform a simple SQL query
read_df.createOrReplaceTempView('employees')
spark.sql('SELECT * FROM employees WHERE age > 30').show()

This demonstrates how Delta Lake tables can be queried using both DataFrame APIs and SQL syntax.

6. Demonstrate Delta Lake Features

Delta Lake's power lies in its advanced features. Let's explore versioning by modifying the data and seeing how Delta Lake tracks changes:

# Create a new DataFrame with updated data
updated_data = {
    'id': [1, 2, 3, 4, 5, 6],
    'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve', 'Frank'],
    'age': [25, 30, 35, 40, 45, 50],
    'salary': [50000, 60000, 70000, 80000, 90000, 100000]
}

updated_pandas_df = pd.DataFrame(updated_data)
updated_spark_df = spark.createDataFrame(updated_pandas_df)

# Append new data to existing Delta table
updated_spark_df.write.format('delta').mode('append').save('/delta/employees')

# Show the updated table
spark.read.format('delta').load('/delta/employees').show()

The append mode adds new records to the existing table, and Delta Lake maintains a complete history of all changes.

7. Explore Table History

Delta Lake keeps track of all changes to tables. Let's examine the version history:

# Show table history
spark.sql("DESCRIBE HISTORY '/delta/employees'").show(truncate=False)

This command reveals the version history of the table, showing when changes were made, what operations were performed, and the size of each version.

8. Time Travel Queries

One of Delta Lake's most powerful features is time travel, allowing you to query data as it existed at a specific point in time:

# Query data from a specific version
spark.read.format('delta').option('versionAsOf', 0).load('/delta/employees').show()

This retrieves the table as it existed in version 0, demonstrating how Delta Lake can maintain data lineage and enable rollback capabilities.

Summary

This tutorial demonstrated how to work with Delta Lake, a core technology in Databricks' platform that supports the kind of scalable data processing that led to their $188 billion valuation. You learned how to create Delta tables, perform basic operations, and leverage advanced features like versioning and time travel. These capabilities are essential for companies handling massive datasets and require the robust infrastructure that Databricks provides, making it a valuable platform for enterprise data analytics.