Mô tả

Learn Apache Spark From Scratch To In-Depth


From the instructor of successful Data Engineering courses on "Big Data Hadoop and Spark with Scala" and "Scala Programming In-Depth"


  • From Simple program on word count to Batch Processing to Spark Structure Streaming.

  • From Developing and Deploying Spark application to debugging.

  • From Performance tuning, Optimization to Troubleshooting


Contents all you need for in-depth study of Apache Spark and to clear Spark interviews.


Taught in very simple English language so any one can follow the course very easily.


No Prerequisites, Good to know basics about Hadoop and Scala


Perfect place to start learning Apache Spark


Apache Spark is a unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.


Speed

Run workloads 100x faster.

Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine.


Ease of Use

Write applications quickly in Java, Scala, Python, R, and SQL.

Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells.


Generality

Combine SQL, streaming, and complex analytics.

Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application.


Runs Everywhere

Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources.


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1 sections

Apache Spark In-Depth (With Scala)

27 lectures
Introduction to Data Engineering Career Path
01:46
Day 1 - Introduction to Spark
02:00:41
Day 2 - Introduction to Spark
01:43:46
Day 3 - Spark Installation on Linux VM
01:54:14
Day 4 - RDD Day 1
01:51:13
Day 5 - RDD Day 2
02:06:03
Day 6 - RDD Day 3
02:01:10
Day 7 - RDD Day 4
01:38:26
Day 8 - RDD Day 5
01:34:49
Day 9 - Dataframe Day 1
01:30:38
Day 10 - Dataframe Day 2
02:21:41
Day 11 - Dataframe Day 3
01:32:24
Day 12 - Dataframe Day 4
02:00:26
Day 13 - Dataframe Day 5
01:40:25
Day 14 - Dataframes Day 6
01:41:14
Day 15 - Dataframes - Spark SQL
02:04:43
Day 16 - Datasets
51:48
Day 17 - Spark Application Development and Deployment
02:04:46
Day 18 - Spark Application Development and Deployment
01:55:15
Day 19 - Performance Tuning and Optimization
01:17:15
Day 20 - Common Errors and Debugging
01:13:23
Day 21 - Spark Streaming D 1
49:34
Day 22 - Spark Streaming D 2
01:52:49
Day 23 - Spark Streaming D 3
01:35:24
Day 24 - Project
47:46
Day 25 - What Next, Job Assistance and How to Prepare for Interview
27:13
Career Guidance
02:50

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