Data ingestion and integration

  • 7 minutes

Data ingestion and integration form the foundational layer for effective data processing in Delta Live Tables (DLT) within Azure Databricks. This ensures that data from various sources is accurately and efficiently loaded into the system for further analysis and processing.

DLT facilitates data ingestion and integration through:

  • Multi-source ingestion: allows you to collect data from various sources.
  • Stream and batch data processing: enables you to process data either continuously or in grouped intervals.
  • Schema management: ensures that your data is well-structured and easy to manage.
  • Data quality and governance: helps you maintain the integrity and compliance of your data
  • Pipeline automation and orchestration: streamlines and controls the sequence of your data processing tasks
  • Integration with Azure ecosystem: allows you to interact smoothly with various Azure tools and services
  • Performance optimization: enhances your ability to process data quickly and effectively
  • Monitoring and lineage tracking: helps you track the data's journey and monitor its movement through the system.

Ingest and integrate data into Delta Lake

To get started ingesting data into Delta Lake, the example below walks through ingesting and integrating data from a sample source into Delta Lake using DLT by:

  1. Setting up your Azure Databricks environment.
  2. Creating Delta tables.
  3. Ingesting data from CSV and JSON files.
  4. Transforming and integrating data.
  5. Querying integrated data.

Setting up your Azure Databricks environment

Ensure that your Azure Databricks environment is set up and configured to use Delta Live Tables. The setup involves preparing the appropriate clusters and ensuring that DLT is enabled in your workspace.

Creating the Delta Tables

To create Delta Tables you can use the CREATE TABLE SQL statement, as shown in the following example.

-- Create Delta table for customer data
CREATE TABLE customer_data (
    customer_id INT,
    customer_name STRING,
    email STRING
);

-- Create Delta table for transaction data
CREATE TABLE transaction_data (
    transaction_id INT,
    customer_id INT,
    transaction_date DATE,
    amount DOUBLE
);

Ingesting data from CSV and JSON files

You can use Databricks SQL to read data from a CSV file and a JSON file, and then insert it into Delta tables.

-- Load customer data from CSV
CREATE OR REPLACE TEMPORARY VIEW customer_data_view AS
SELECT * FROM csv.`/path/to/customer_data.csv`
OPTIONS (header "true", inferSchema "true");

-- Insert data into customer Delta table
INSERT INTO customer_data
SELECT * FROM customer_data_view;

-- Load transaction data from JSON
CREATE OR REPLACE TEMPORARY VIEW transaction_data_view AS
SELECT * FROM json.`/path/to/transaction_data.json`;

-- Insert data into transaction Delta table
INSERT INTO transaction_data
SELECT * FROM transaction_data_view;

Transforming and integrating data

You can perform transformations and join data from multiple tables to create a unified view.

-- Create a unified view of customer transactions
CREATE OR REPLACE TEMPORARY VIEW customer_transactions AS
SELECT
    t.transaction_id,
    t.customer_id,
    c.customer_name,
    c.email,
    t.transaction_date,
    t.amount
FROM
    transaction_data t
JOIN
    customer_data c
ON
    t.customer_id = c.customer_id;

-- Create a Delta table for the integrated data
CREATE TABLE integrated_data USING DELTA AS
SELECT * FROM customer_transactions;

Querying the integrated data

Then, you can query the integrated data for analysis.

-- Query the integrated data
SELECT
    customer_name,
    SUM(amount) AS total_spent
FROM
    integrated_data
GROUP BY
    customer_name
ORDER BY
    total_spent DESC;

By following these steps, you can effectively ingest, transform, and integrate data from various sources in Azure Databricks using SQL. This process ensures that your data is stored in a structured and queryable format, enabling powerful data analysis and insights.