This 1-day course is for data engineers, analysts, architects, data scientist, software engineers, IT operations, and technical managers interested in a brief hands-on overview of Apache Spark.
The course provides an introduction to the Spark architecture, some of the core APIs for using Spark, SQL and other high-level data access tools, as well as Spark’s streaming capabilities and machine learning APIs. The class is a mixture of lecture and hands-on labs.
Each topic includes lecture content along with hands-on labs in the Databricks notebook environment. Students may keep the notebooks and continue to use them with the free Databricks Community Edition offering after the class ends; all examples are guaranteed to run in that environment.
After taking this class, students will be able to:
Use a subset of the core Spark APIs to operate on data.
Articulate and implement simple use cases for Spark
Build data pipelines and query large data sets using Spark SQL and DataFrames
Create Structured Streaming jobs
Understand how a Machine Learning pipeline works
Understand the basics of Spark’s internals
Who Can Benefit
Data engineers, analysts, architects, data scientist, software engineers, and technical managers who want a quick introduction into how to use Apache Spark to streamline their big data processing, build production Spark jobs, and understand and debug running Spark applications.
Some familiarity with Apache Spark is helpful but not required. Knowledge of SQL is helpful. Basic programming experience in an object-oriented or functional language is highly recommended but not required. The class can be taught concurrently in Python and Scala.
Introduction to Spark SQL and DataFrames, including:
Reading & Writing Data
The DataFrames/Datasets API
Caching and caching storage levels
Overview of Spark internals
How Spark schedules and executes jobs and tasks
Shuffling, shuffle files, and performance
The Catalyst query optimizer
Spark Structured Streaming
Sources and sinks
Structured Streaming APIs
Windowing & Aggregation
Checkpointing & Watermarking
Reliability and Fault Tolerance
Overview of Spark’s MLlib Pipeline API for Machine Learning
https://www.exitcertified.com/it-training/databricks/apache-spark-overview-56015-detail.htmlDB100Apache Spark Overviewhttps://assets.exitcertified.com/assets/CourseImages/188d379ad4/AdobeStock_215949089__FitMaxWzEwMDAsMTAwMF0.jpg1500.00USDInStock/Training/DatabricksThis 1-day course is for data engineers, analysts, architects, data scientist, software engineers, IT operations, and technical...1500.00DatabricksClassroomGTRApr 21, 2020 | iMVP2019-02-05T18:48:10+00:00USD