Cloud Hadoop: Scaling Apache Spark

Generate genuine business insights from big data. Learn to implement Apache Hadoop and Spark workflows on AWS.

Introduction

• Scaling Apache Hadoop and Spark
• What you should know
• Using cloud services

1. Hadoop and Spark Fundamentals

• Modern Hadoop and Spark
• File systems used with Hadoop and Spark
• Apache or commercial Hadoop distros
• Hadoop and Spark libraries
• Hadoop on Google Cloud Platform
• Spark Job on Google Cloud Platform

2. AWS Cloud Spark Environments

• Sign up for Databricks Community Edition
• Add Hadoop libraries
• Databricks AWS Community Edition
• Load data into tables
• Hadoop and Spark cluster on AWS EMR
• Run Spark job on AWS EMR
• Review batch architecture for ETL on AWS

3. Spark Basics

• Apache Spark libraries
• Spark data interfaces
• Select your programming language
• Spark session objects
• Spark shell

4. Using Spark

• Tour the Databricks Environment
• Tour the notebook
• Import and export notebooks
• Calculate Pi on Spark
• Run WordCount of Spark with Scala
• Import data
• Transformations and actions
• Caching and the DAG
• Architecture: Streaming for prediction

5. Spark Libraries

• Spark SQL
• SparkR
• Spark ML: Preparing data
• Spark ML: Building the model
• Spark ML: Evaluating the model
• Advanced machine learning on Spark
• MXNet
• Spark with ADAM for genomics
• Spark architecture for genomics

6. Spark Streaming

• Reexamine streaming pipelines
• Spark Streaming
• Streaming ingest services
• Advanced Spark Streaming with MLeap

7. Scaling Spark on AWS and GCP

• Scale Spark on the cloud by example
• Build a quick start with Databricks AWS
• Scale Spark cloud compute with VMs
• Optimize cloud Spark virtual machines
• Use AWS EKS containers and data lake
• Optimize Spark cloud data tiers on Kubernetes
• Build reproducible cloud infrastructure
• Scale on GCP Dataproc or on Terra.bio

Conclusion

• Continue learning for scaling