Micro-Batch Streaming in Databricks

 

I’ve mentioned it before, but it’s worth repeating: Databricks truly stands out as the top platform for rapidly moving from data ingestion through transformation to actionable insights (forgive my enthusiasm!)

 

With Spark, you get the flexibility of both batch and structured streaming. And with Delta Live Tables (DLT), Databricks simplifies building secure and testable data pipelines. Using DLT’s declarative framework, you define transformations, and Databricks handles the orchestration, automatically maintaining dependencies between tables. 

 

DLT also offers built-in data quality features, known as “expectations,” which can be applied using both SQL and Python. For an even richer development experience, you can integrate additional tools like DBT into your pipeline.

 

Databricks Jobs further streamlines orchestration, allowing you to schedule and manage multiple data pipelines effortlessly. For data governance, Unity Catalog offers a unified approach. Meanwhile, MLflow makes it straightforward to manage machine learning workflows, from experiments to deployment, seamlessly integrating model serving into the Databricks ecosystem.

 

In this post, I’ll walk you through a proof of concept based on a recent project.

 

Use Case

A Danish startup aims to integrate energy data into its workflow to enhance the predictive power of its machine learning models. The data is provided through the Danish Energy Data Service API, offering insights like CO2 emissions from electricity production, wind and solar energy output, and electricity imports and exports to and from Denmark. The API updates every 5 minutes, providing a dynamic, near-real-time view of energy data.

This proof of concept focuses on the following objectives:

1. Continuous Ingestion of energy data from the API.
2. Data Transformation to prepare for analysis and visualization.
3. Dashboard Creation for real-time insights on CO2 emissions, renewable energy production, and electricity imports/exports.
4. Setting up Machine Learning Pipelines for experiments.
5. Dashboarding Predictions to visualize insights.

In this post, I’ll guide you through steps 1, 2, and 3.

Step 1: Create an Ingestion Task for Raw API Data

Using a Python function, we make a call to the Danish Energy Data Service API endpoint, storing the payload directly in Unity Catalog. This enables streamlined access and governance of raw data across the Databricks workspace.

Task Creation uses Databricks Worklows

 

 

Step 2: Set Up a Delta Live Table Pipeline for Transformation

 

Delta Live Tables is then used to define a transformation pipeline on the raw data. This powerful declarative framework ensures that transformations are secure, testable, and scalable. With Delta Live Tables managing dependencies, along with built-in schema evolution and schema enforcement, the pipeline is highly robust and adaptable, automatically accommodating any changes in data structure.

 

 

 

After configuring your tasks, you can choose between a simple schedule or a more advanced, customized timing for your job, giving you full control over its execution frequency.

 

Step 3: Create a Dashboard

Building a dashboard in Databricks is highly intuitive. You can create datasets either by writing SQL queries or by selecting tables directly from Unity Catalog. The Unity Catalog icon is conveniently located in the UI, allowing you to quickly browse your data without leaving the SQL workspace. Once your dataset is ready, you can design your dashboard using an interactive, user-friendly canvas. This interface makes it easy to build visualizations that automatically update as new data streams in, providing real-time insights at a glance.

 

 

In summary, Databricks provides an all-in-one platform that simplifies data ingestion, transformation, and analysis. With Delta Live Tables, teams can build robust, scalable data pipelines that handle dependencies and schema changes effortlessly, ensuring data quality and consistency. Unity Catalog, on the other hand, offers centralized data governance, enabling secure, organized access and management across the platform. Databricks’ integration with Spark supports both batch and real-time processing, while its intuitive UI makes dashboard creation straightforward. Additionally, robust scheduling options allow for flexible task orchestration. Built-in tools like MLflow make managing machine learning workflows seamless, from experimentation to deployment. Overall, Databricks empowers teams to focus on delivering insights and value quickly, making it an invaluable asset for data-driven projects.