I recently developed a proof of concept for streaming video game data into Azure Databricks using Azure Event Hubs. Once ingested, the data is transformed and continuously visualized in real-time through a dashboard in Databricks.
I must say, working with streaming data in Databricks is remarkably straightforward. The architecture for this solution is outlined below:
Case Study
Video game studios often lack detailed insights into how players interact with their games. These insights are invaluable for refining future game versions, enhancing player engagement, understanding player demographics, and much more.
During the game development and testing phases, these insights can be even more crucial, as they enable developers to make incremental improvements and fine-tune the player experience.
In this proof of concept, I demonstrate how Azure Event Hubs, paired with Databricks Structured Streaming and Delta Live Tables, can be used to effortlessly ingest, preprocess, and visualize video game player data in real-time.
1 - Create an Azure Event Hubs resource
The first step is to create an Azure Event Hubs kafka compatible endpoint.
Azure Event Hubs is a cloud-native data-streaming service capable of processing millions of events per second with low latency. It allows seamless streaming from any source to any destination and is fully compatible with Apache Kafka, enabling you to run your existing Kafka workloads without needing to modify your code. [1]
2 – Simulate a stream of video game player data with Python and create a topic
I use a Python script to generate simulated player data, create a topic, and send the data to that topic in Azure Event Hubs.
3 – Implement a Spark Streaming task in an Azure Databricks notebook: Task 1
This code reads player data from a Kafka-compatible Azure Event Hub topic and writes the stream into a table managed in Databricks Unity Catalog. For security purposes, the Event Hub name (topic) and connection strings are stored as secrets in a Databricks scope. It is recommended by Databricks to use secret names to reference tokens or connection strings, ensuring secure access. In this case, both the connection string and Event Hub name are saved in a scope named player_scope. We then use dbutils.secrets.get() to securely retrieve these values within the notebook.
4 – Implement a Delta Live Table pipeline in an Azure Databricks notebook to transform the raw data: Task 2
This is where we preprocess the raw data. The data from Azure Event Hub is ingested as binary data, with the header labeled as ‘body’. First, we cast the binary data to a string, then convert the string into JSON format. Since we have already specified the data schema, we use it to extract all the columns. Once the data is in a suitable format, we can perform aggregations. In this example, we aggregate the data to find the number of players by country and compute the average score per player.
5 – In Azure Databricks Workflows, create a job that includes both Task 1 and Task 2.
After running the job in Azure Databricks Workflows, a graph is generated (for Task 2), visualizing the various transformations applied in the Delta Live Table.
In the graph, you can observe the Medallion architecture in action. The bronze table stores the raw data, the silver table holds the cleaned and processed data, and the gold table contains the aggregated data ready for analytics.
6 – Dashboard
Note that this dashboard will automatically update according to the schedule specified in the job configuration.
Conclusion
This proof of concept demonstrates how Azure Event Hubs, in combination with Azure Databricks, provides a powerful and scalable solution for processing streaming video game data in real-time. By leveraging the Kafka-compatible capabilities of Event Hubs and the structured streaming features in Databricks, we can seamlessly ingest, transform, and visualize data. The use of Delta Live Tables further simplifies data transformation, while the Medallion architecture ensures a structured approach to data management—from raw ingestion to refined analytics. With automated workflows and dashboards, this pipeline offers game studios valuable insights into player behavior, which can be used to enhance future game development and player engagement.
References
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