Databricks Structured Streaming Guide (Step-by-Step)

Introduction

Structured Streaming in Databricks allows organizations to process real-time data streams efficiently using Apache Spark. It enables continuous ingestion and transformation of data from sources such as Kafka, cloud storage, or IoT devices.

Step 1: Understand Streaming Data

Streaming data refers to continuously generated data such as logs, sensor data, financial transactions, or social media feeds.

Step 2: Read Streaming Data

In Databricks, streaming data can be read using Spark Structured Streaming APIs.

df = spark.readStream.format("json").load("/mnt/stream_data")
display(df)

Step 3: Process the Streaming Data

Apply transformations such as filtering, aggregations, or joins.

df_filtered = df.filter("amount > 100")

Step 4: Write Streaming Output

Streaming data can be written to Delta tables.

df_filtered.writeStream
  .format("delta")
  .option("checkpointLocation", "/mnt/checkpoints")
  .start("/mnt/delta/output")

Conclusion

Databricks Structured Streaming enables reliable and scalable real-time data processing. By combining Spark streaming with Delta Lake, organizations can build robust real-time analytics pipelines.