A Practical Guide to Data Preparation with Databricks

Author: Tyler Faulkner

26 June, 2025

In today’s data-driven world, having access to information is not enough. To extract value from it, organizations need efficient processes for accessing, cleaning, transforming, and storing data. Databricks offers a unified analytics platform built on Apache Spark, which enables this across diverse data environments.

This guide outlines a streamlined approach to data preparation using Databricks, from connecting to data sources to storing transformed data for high-performance analytics.

Connecting to Data Sources

Organizations often deal with fragmented data spread across relational databases, cloud storage, NoSQL systems, and file formats. Databricks solutions offer many capabilities for connecting to these sources through standard connectors, such as JDBC and ODBC, as well as native integrations with AWS S3, Azure Blob Storage, and other services. Databricks also offers abstractions to JDBC by using formats such as “postgresql” and “sqlserver” to avoid needing to configure a JDBC URL if desired.

To ensure secure access, Databricks Secrets is used to manage sensitive credentials, such as database passwords and API keys. Avoid hardcoding credentials in notebooks; store them securely and use token-based authentication methods, such as OAuth, wherever possible. Role-based access control (RBAC) ensures that sensitive data is only accessible to authorized users.

Example: Connecting to a PostgreSQL Database

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName(“MyApp”).getOrCreate()

df = spark.read.format(“jdbc”) \

.option(“url”, “jdbc:postgresql://your_host:5432/your_db”) \

.option(“dbtable”, “your_table”) \

.option(“user”, “your_username”) \

.option(“password”, dbutils.secrets.get(scope = “<scope-name>”, key = “<key-name>”)) \

.load()

In production, credentials should be retrieved from Databricks Secrets rather than being hardcoded as shown above in the password field.

Loading and Exploring Data

Once data is accessible, Databricks Notebooks provide an interactive and collaborative environment for loading and exploring it. Apache Spark enables reading from various file formats like CSV, Parquet, and JSON, as well as from cloud storage or traditional databases.

The Databricks File System (DBFS) is the internal storage layer within Databricks. You can use the dbutils library to mount external storage (S3, ADLS, etc.) or explore internal paths.

Reading a CSV from DBFS:

data = spark.read.format(“csv”) \

.option(“header”, “true”) \

.option(“inferSchema”, “true”) \

.load(“dbfs:/path/to/data.csv”)

Common Data Exploration Methods:

• data.show(): View a sample of the data
• display(data): Databricks specific method to generate a UI table
• data.printSchema(): View data structure and types
• data.describe(): Basic statistics for numerical columns

These operations are essential to assess completeness, data types, missing values, and anomalies.

Transforming Data with Spark

Once loaded, data usually needs to be cleaned and transformed. Apache Spark provides two primary abstractions for transformations: RDDs (Resilient Distributed Datasets) and DataFrames.

What is a DataFrame?

A DataFrame in Spark is a collection of data that is organized into named columns, similar to a table in a relational database. It enables high-level, declarative operations such as filtering, grouping, and aggregation. Because it’s distributed, a DataFrame can handle extensive datasets across clusters.

DataFrames are optimized using Spark’s internal Catalyst engine and support multiple languages, including Python (PySpark), Scala, Java, and SQL. (With Spark Connect, this can now extend to any language).

Example:

df = spark.read.csv(“dbfs:/path/to/file.csv”, header=True, inferSchema=True)

df.filter(df[“status”] == “active”).groupBy(“region”).count().show()

This abstraction enables you to write concise, expressive code while leveraging Spark’s distributed computing power.

Core Transformations:

• filter(): Exclude rows based on conditions
• groupBy(): Aggregate data by key
• withColumn(): Create or update columns
• dropna() / fillna(): Handle missing values
• cast(): Convert data types

Transformations are lazy. They only execute when triggered by an action such as show(), which enhances performance

Storing Transformed Data

After data has been cleaned and transformed, storing it efficiently ensures it remains accessible, reliable, and performant. Databricks supports a range of storage options, including databases, cloud object stores, and its own enhanced lakehouse format: Delta Lake.

What is Delta Lake?

Delta Lake is an open-source storage layer that brings reliability and performance to data lakes. It enables ACID transactions, schema enforcement, and time travel for historical queries, capabilities traditionally missing from large-scale, file-based data lakes.

Delta Lake sits on top of existing cloud storage systems (like S3 or Azure Data Lake) and integrates seamlessly with Apache Spark.

Why Delta Lake Matters

• Transactional Consistency (ACID): Ensures that reads and writes are reliable, even under concurrent operations.
• Schema Control: Prevents bad data from corrupting your lake.
• Time Travel: Query previous versions of your dataset.

What Does ACID Mean?

These properties are essential for enterprise-grade reliability.

Writing to Delta Lake:

df.write.format(“delta”).mode(“overwrite”).save(“/mnt/delta/clean_data”)

Optimizing Data for Performance

• Use Columnar Formats: Formats like Parquet reduce disk I/O and improve compression.
• Partitioning: Dividing data by column (e.g., date, region) speeds up query performance.
• Lifecycle Policies: Implement archiving, retention, and deletion rules to manage cost and compliance.

Example: Partitioned Parquet Write

df.write.partitionBy(“region”).parquet(“s3://your-bucket/path/”)

Final Thoughts

Databricks offers a comprehensive platform for modern data preparation, combining ease of use, powerful distributed processing, and robust security. By leveraging Spark DataFrames, storing clean data in Delta Lake, and adhering to ACID principles, teams can build scalable and reliable data pipelines.

Whether you’re working toward dashboards, machine learning models, or real-time applications, a solid foundation in data preparation with Databricks is the first step toward unlocking your data’s true potential.

Ready to Accelerate Your Data Journey?

At Xorbix, we help organizations unlock the full potential of their data with modern tools like Databricks. Whether you’re just starting to unify your data sources or scaling advanced analytics and AI solutions, our team of expert consultants brings the experience, precision, and agility to help you move forward quickly.