Data Ingestion Done Right: Streamline Your Workflow with Databricks & Delta Lake
Databricks Load Data into Delta Lake
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Loading data into Delta Lake is a foundational task in building a robust and scalable data architecture.
Delta Lake is an open-source storage layer that brings ACID (Atomicity, Consistency, Isolation, Durability) transactions to Apache Spark and big data workloads.
It’s designed to provide a reliable way for data engineering and data science teams to work with large-scale data in a transactional manner.
Here’s how you can load data into Delta Lake, focusing on a Databricks environment where Delta Lake is natively supported and optimized
Step 1: Setup Your Databricks Environment
Ensure that you have a Databricks workspace set up and a cluster running. The cluster should be running a Databricks Runtime version that supports Delta Lake.
Step 2: Read Your Source Data
Your source data might reside in various storage systems like AWS S3, Azure Data Lake Storage (ADLS), Google Cloud Storage (GCS), or traditional databases. Use Spark’s built-in capabilities to read from these sources. For example, to read a CSV file from S3 into a Spark DataFrame: