MP4 | Video: AVC, 1280x720 30 fps | Audio: AAC, 48 KHz, 2 Ch | Duration: 3h 13m
Skill Level: Bner | Genre: eLearning | Language: English + Subtitles | Size: 477 MB
Apache Hadoop and Spark make it possible to generate genuine business insights from big data.
The Amazon cloud is natural home for this powerful toolset, providing a variety of services for running large-scale data-processing workflows. Learn to implement your own Apache Hadoop and Spark workflows on AWS in this course with big data architect Lynn La. Explore deployment options for production-scaled jobs using virtual machines with EC2, managed Spark clusters with EMR, or containers with EKS. Learn how to configure and manage Hadoop clusters and Spark jobs with Databricks, and use Python or the programming language of your choice to import data and execute jobs. Plus, learn how to use Spark libraries for machine learning, genomics, and streaming. Each lesson helps you understand which deployment option is best for your workload.
Topics include:
File systems for Hadoop and Spark
Working with Databricks
Loading data into tables
Setting up Hadoop and Spark clusters on the cloud
Running Spark jobs
Importing and exporting Python notebooks
Executing Spark jobs in Databricks using Python and Scala
Importing data into Spark clusters
Coding and executing Spark transformations and actions
Data caching
Spark libraries: Spark SQL, SparkR, Spark ML, and more
Spark streaming
Scaling Spark with AWS and GCP
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