Apache Spark Data Analytics Best Practices & Troubleshooting
Perform analytics on real-time data by discovering techniques to test and parallelize Spark jobs & solve common problems
3.30 (5 reviews)
97
students
10 hours
content
Apr 2019
last update
$19.99
regular price
What you will learn
Implement high-velocity streaming and data processing use cases while working with streaming API.
Dive into MLlib– the machine learning functional library in Spark with highly scalable algorithms.
Create machine learning pipelines to combine multiple algorithms in a single workflow.
Create highly concurrent Spark programs by leveraging immutability.
Re-design your jobs to use reduceByKey instead of groupBy.
Create robust processing pipelines by testing Apache Spark jobs.
Solve repeated problems by leveraging the GraphX API.
Solve long-running computation problems by leveraging lazy evaluation in Spark.
Avoid memory leaks by understanding the internal memory management of Apache Spark.
Troubleshoot real-time pipelines written in Spark Streaming, APIs for joins - DataFrames or DataSets.
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2336452
udemy ID
4/24/2019
course created date
5/21/2023
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