Data Ingestion on Databricks

May 1, 2025 min read
Data Ingestion on Databricks

Introduction

Apache Spark, which Databricks is built upon, provides powerful and general-purpose capabilities for large-scale data processing. This guide will walk you through the basics of data ingestion, transformation, and writing data back out using Databricks.

Prerequisites

Before you begin, ensure you have:

  1. A Databricks workspace and cluster set up.
  2. The necessary credentials to access your data source(s).
  3. Basic familiarity with Python and PySpark.

Understanding Spark in Databricks

Apache Spark is an open-source distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. Databricks enhances Spark by offering a collaborative environment, optimized performance, and seamless integration with cloud platforms like AWS, Azure, and Google Cloud.

Key Concepts in Spark

  1. Resilient Distributed Dataset (RDD): The fundamental data structure in Spark, which is immutable and distributed across the cluster.
  2. DataFrame: A higher-level abstraction built on top of RDDs, similar to a table in a relational database.
  3. Spark SQL: A module for structured data processing using SQL queries.
  4. Transformations and Actions: Transformations (e.g., map, filter) define a new dataset, while actions (e.g., count, collect) trigger computation.

Connecting to Data Sources

Databricks supports a wide range of data sources, including cloud storage, relational databases, and NoSQL databases. Below are examples of connecting to popular cloud storage services.

Example: Reading from AWS S3

df = spark.read.format("csv").option("header", "true").load('s3a://<bucket-name>/<path-to-file>')

Example: Reading from Azure Blob Storage

df = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("<connection-string>@<container-name>/<path-to-file>")

Example: Reading from Google Cloud Storage

df = spark.read.format("csv").option("header", "true").load('gs://<bucket-name>/<path-to-file>')

Transforming Data

Once data is ingested, it often requires cleaning or transformation. PySpark provides a rich set of APIs for data manipulation.

Example: Cleaning and Transforming Data

from pyspark.sql.functions import col, when

# Fill null values in 'column_name'
df = df.na.fill({'column_name': 'default_value'})

# Replace specific values in 'column_name'
df = df.withColumn('column_name', when(col('column_name') == 'old_value', 'new_value').otherwise(col('column_name')))

Example: Adding a New Column

from pyspark.sql.functions import lit

# Add a new column with a constant value
df = df.withColumn('new_column', lit('constant_value'))

Writing Data Back Out

After processing, you can write the data back to your desired storage location. Databricks supports various formats like CSV, Parquet, and Delta.

Example: Writing to CSV

df.write.format("csv").option("header", "true").save("<output-path>")

Example: Writing to Delta Lake

Delta Lake is an open-source storage layer that brings ACID transactions to Spark. It is highly recommended for reliable and scalable data storage.

df.write.format("delta").mode("overwrite").save("<output-path>")

Best Practices for Using Spark in Databricks

  1. Leverage Delta Lake: Use Delta Lake for efficient and reliable data storage.
  2. Optimize Cluster Configuration: Choose the right cluster size and instance types based on your workload.
  3. Use Caching: Cache intermediate results to speed up iterative computations.
  4. Monitor Performance: Use the Databricks UI to monitor job execution and optimize performance.
  5. Write Modular Code: Break your code into reusable functions for better maintainability.

Conclusion

Databricks simplifies the process of working with Apache Spark, enabling you to ingest, transform, and write data at scale. By following the steps and best practices outlined in this guide, you can efficiently process large datasets and unlock valuable insights from your data.