Here’s a breakdown of ETL using Databricks, including key concepts, advantages, and steps involved:

What is ETL?

ETL (Extract, Transform, Load) is the fundamental process of building data pipelines in analytics and business intelligence. Here’s what each step entails:

• Extract: Gathering data from various sources like databases, APIs, flat files (CSV, JSON, etc.), or streaming sources.
• Transform: Cleaning, standardizing, enriching, and converting data into an analysis-ready state.
• Load:  Storing the transformed data into data warehouses, data lakes, or other systems for consumption by analytics tools or dashboards.

Why Databricks for ETL?

Databricks, with its unified data analytics platform, offers a robust and scalable environment for ETL processes:

• Apache Spark: Databricks leverages Apache Spark’s distributed processing capabilities to handle large-scale ETL tasks efficiently.
• Delta Lake: Delta Lake provides reliability and ACID transactions, enhancing data lake integrity and reducing ETL complexity.
• Cloud Integration: Seamless integration with cloud storage like AWS S3, Azure Data Lake Storage, and cloud-based data warehouses.
• Scalability: Databricks auto-scaling clusters adapt to workload demands for cost-effective ETL.
• Languages: Supports Python, Scala, SQL, and R for flexible data transformations.
• Delta Live Tables: Streamlines ETL development with declarative pipelines, error handling, and monitoring.

Everyday ETL Tasks on Databricks

• Data Cleaning:  Fixing errors, inconsistencies, and missing values.
• Data Conversion and Validation: Enforcing data types and business rules.
• Data Aggregation: Computing summaries and statistics.
• Joining and Merging: Combining data from multiple sources.
• Slowly Changing Dimensions (SCDs): Handling historical changes in data effectively.

ETL Steps with Databricks

1. Setup:
   • Create a Databricks workspace.
   • Establish connections to your data source(s) and target destination.
2. Extract:
   • Use Databricks connectors or Spark APIs to read data into DataFrames.
3. Transform:
   • • Employ Spark DataFrames, Spark SQL, or Python/Scala functions to Clean and standardize data
     • Apply business logic
     • Enrich data with external lookups
4. Load:
   • Write transformed data to target systems using Databricks connectors or Spark’s write capabilities.
   • Consider Delta Lake for optimized storage and ACID transactions.
5. Orchestration and Scheduling (Optional):

• Use Databricks Jobs to schedule ETL workflows at regular intervals.
• Consider Delta Live Tables for streamlined pipeline development and management.

Example (Python)

# Extract data from a CSV file
df = spark.read.csv(“path/to/data.csv”, header=True, inferSchema=True)

# Transform data
df = df.withColumn(“new_column”, df[“existing_column”] * 2) \
.dropna()

# Load to a Delta Lake table
df.write.format(“delta”).save(“path/to/delta/table”)