Lecture
Apache Spark Ecosystem: Basics and Operations
In course
DEMO: cillum pariatur culpa fugiat
Et aute veniam eiusmod sunt aliquip laboris ad mollit tempor aliqua voluptate reprehenderit. Et excepteur ut qui Lorem officia reprehenderit proident adipisicing eu. Sit eiusmod irure adipisicing enim in occaecat. In sunt Lorem consequat aute dolore deserunt consectetur pariatur et. Do id do reprehenderit est id veniam amet fugiat cupidatat dolor duis adipisicing laborum ipsum. Cupidatat nulla quis aliqua mollit consectetur excepteur consequat ad magna eu et enim.
Description
This lecture covers the basics of the Apache Spark ecosystem, including in-house applications like Cancer Genomics and Energy Debugging, as well as various components such as Spark Core, Spark SQL, MLlib, and Spark Streaming. It also delves into RDD operations, Spark Context, and Spark-submit commands, providing a comprehensive overview of working with Spark.
Instructor
irure mollit proident
Cillum ex quis proident mollit est commodo in et culpa amet. Nulla velit minim esse consectetur voluptate deserunt eu quis sunt anim ea. Sint esse commodo Lorem exercitation nulla ex deserunt cupidatat pariatur. Pariatur qui tempor occaecat deserunt velit ea aliqua cupidatat anim.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (54)
General Introduction to Big Data
Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.
Introduction to R Programming for Genetics & Genomics
Introduces a course on Genetics & Genomics, focusing on R programming with interactive exercises.
Spark Data Frames
Covers Spark Data Frames, distributed collections of data organized into named columns, and the benefits of using them over RDDs.
Big Data Best Practices and Guidelines
Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.
Analytics on Data at Rest and Data in Motion
Explores combining data at rest with data in motion, emphasizing the Lambda architecture complexities and quality assessment of streams and batches.