HANDS-ON-LAB

Databricks Snowflake Example

Problem Statement

This hands-on process Databricks Snowflake Example code aims to create a Lambda function to cleanse YouTube statistics reference data and store it in an S3 bucket in CSV format. Additionally, the cleansed data should be exposed in the Glue catalog. 

The statistics reference data (the JSON files) is placed in the raw S3 bucket:

s3://<raw_bucket_name>/youtube/raw_statistics_reference_data/

Tasks

  1. Configure Event Hub to Databricks Integration: Establish the connection between Azure Event Hub and Databricks. Set up the necessary credentials and configurations to access the Event Hub.

  2. Stream Data to Databricks: Implement Spark code in Databricks to consume data from the Event Hub. Write the data to a specific location in Databricks, such as a Delta table or Parquet files.

  3. Create Snowflake Table: Set up a table named "youtube_videos" in Snowflake with the appropriate schema that matches the data from the Event Hub. Define the column names and data types accordingly.

  4. Stream Data from Databricks to Snowflake: Configure a data streaming pipeline in Databricks to read the data from the location where it was written and stream it into the Snowflake table. Use Snowflake Connector to establish the connection and write data to the table.

  5. Validate Data in Snowflake: Execute SQL queries in Snowflake to retrieve and examine the data stored in the "youtube_videos" table. Verify that the data streaming process from Databricks to Snowflake was successful.


Gain practical experience in configuring the integration, streaming data, creating Snowflake tables, and validating the results.

Learnings

  • Integrating Azure Event Hub with Databricks.

  • Streaming data from Event Hub to Databricks.

  • Writing data from Databricks to a specific location.

  • Setting up a Snowflake table for data ingestion.

  • Streaming data from Databricks to Snowflake using the Snowflake Connector.

  • Querying and validating data in Snowflake.

FAQs

Q1. What is the benefit of streaming data from Azure Event Hub to Snowflake via Databricks?

Streaming data from Azure Event Hub to Snowflake via Databricks allows for real-time data ingestion and processing. It enables continuous updates to the Snowflake table, providing near real-time analytics and insights on the streaming data.

 

Q2. Can I use different data formats in the streaming pipeline?

Yes, you can use various data formats such as Delta tables or Parquet files in Databricks to store the streamed data. Snowflake supports these formats for efficient data ingestion and query performance.

 

Q3. How does Snowflake ensure data reliability and integrity during the streaming process?

Snowflake provides ACID-compliant transactions, ensuring data reliability and integrity. It guarantees that the streamed data is stored consistently in the Snowflake table, allowing for accurate and reliable analysis.