Structured Streaming in Databricks

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Note: Azure Table and Queue Storage support NoSQL and message handling for scalable cloud apps

Structured Streaming in Databricks 

Structured Streaming in Databricks is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. It allows developers to work with real-time data using high-level APIs like DataFrames and SQL, treating streaming data as an unbounded table. Databricks enhances Structured Streaming by offering a fully managed environment with auto-scaling, monitoring, and notebook support.

With Structured Streaming, you can ingest data from sources like Kafka, Azure Event Hubs, Delta tables, or file directories, process it in real time using familiar operations (filter, join, aggregate), and output it to sinks like Delta Lake, console, or cloud storage. The core abstraction is the DataFrame, and the system automatically tracks computation using a query plan.

Databricks provides features like checkpointing, stateful operations, watermarking for handling late data, and exactly-once processing. It also supports trigger intervals to control the frequency of micro-batches.

You can write a streaming query in PySpark as:

python

Copy

Edit

df = spark.readStream.format("csv").load("/input")

df.writeStream.format("console").start()

Structured Streaming in Databricks simplifies real-time analytics, ETL pipelines, and machine learning on streaming data, making it ideal for modern data architectures.

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Databricks Job Scheduling

Filestore (NFS)

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