Building Data Lakes on AWS
In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR.
This course is intended for:
- Data platform engineers
- Architects and operators who build and manage data analytics pipelines
Students with a minimum one-year experience managing open-source data frameworks such as Apache Spark or Apache Hadoop will benefit from this course.
In this course you will learn to:
- Compare the features and benefits of data warehouses, data lakes, and modern data architectures
- Design and implement a batch data analytics solution
- Identify and apply appropriate techniques, including compression, to optimize data storage
- Select and deploy appropriate options to ingest, transform, and store data
- Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case
- Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
- Secure data at rest and in transit
- Monitor analytics workloads to identify and remediate problems
- Apply cost management best practices
Register 21 days before class start date and save 10%! Enter discount code EARLY10 during registration.
Register 21 days before class start date and save $250! Enter discount code EARLY250 during registration.
Module A: Overview of Data Analytics and the Data Pipeline
- Data analytics use cases
- Using the data pipeline for analytics
Module 1: Introduction to Amazon EMR
- Using Amazon EMR in analytics solutions
- Amazon EMR cluster architecture
Interactive Demo 1: Launching an Amazon EMR cluster
Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage
- Storage optimization with Amazon EMR
- Data ingestion techniques
Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR
- Apache Spark on Amazon EMR use cases
- Why Apache Spark on Amazon EMR
- Spark concepts
Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell
Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR
Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive
- Using Amazon EMR with Hive to process batch data
- Transformation, processing, and analytics
Practice Lab 2: Batch data processing using Amazon EMR with Hive
Module 5: Serverless Data Processing
- Serverless data processing, transformation, and analytics
- Using AWS Glue with Amazon EMR workloads
Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions
Module 6: Security and Monitoring of Amazon EMR Clusters
- Securing EMR clusters
Interactive Demo 3: Client-side encryption with EMRFS
Demo: Reviewing Apache Spark cluster history
Module 7: Designing Batch Data Analytics Solutions
- Batch data analytics use cases
Is there a discount available for current students?
UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Online courses are excluded from this offer.
What is the cancellation and refund policy?
Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.