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What you would learn in Processing Streaming Data with Apache Spark on Databricks course?
In Structured Streaming, Apache Spark treats real-time data as a table being continuously appended. This creates streams processing models that utilize the same APIs used in a batch processing model. It's up to Spark to make incremental changes to the batch processing process to perform the stream. The responsibility for the process stream is the responsibility of the software, which makes it a breeze and easy to process data streaming with Spark.
In this course, Processing Streaming Data using Apache Spark in Databricks, Learn the process of streamlining and processing data with the help of abstractions made available through Spark stream-based structured streams. The first step is to comprehend the distinction between stream processing and batch processing and the different methods employed for processing streaming data. You'll also explore the structured structures and configurations of Spark stream APIs.
In the next step, you will be taught how to read data from streaming sources with the Auto Loader for Azure Databricks. Automated Loader simplifies taking streaming data from the file system and handles the managing and tracking of the processed files, making it easy to read data from other cloud storage services. It will then make aggregations and transformations of streaming data and then write it to storage using the append, complete, or update model.
In the end, you will be taught how to utilize SQL-like abstractions for input streams. Connect with an outside cloud storage service, like an Amazon S3 bucket, and load your stream using Auto Loader. Then, you will execute SQL queries to process the data. In the process, you'll be able to create a system resistant to failures by using checkpoints. You can also set up the stream processing process by creating a job on the Databricks Job Cluster.
Once you've completed the program, you'll possess the knowledge and skills regarding streaming streams in Spark required to analyze and monitor streams and identify the best use cases to transform streaming data.
Course Overview 2mins
Overview of the Streaming Architecture in Apache Spark 42mins
Applying Transformations on Streaming Data 38mins
Executing SQL Queries on Streaming Data 36mins
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