Amazon Web Services (AWS)
Building Batch Data Analytics Solutions on AWS
Process large volumes of data in batches on AWS.
1 dayIntermediate9 modules
About this course
Process large volumes of data in batches on AWS. You will launch and tune Amazon EMR clusters, transform data with Spark, Hive and AWS Glue, orchestrate pipelines, and secure and monitor your workloads.
What you'll learn
- Launch and configure clusters using Amazon EMR for batch workloads
- Transform and analyze batch data using Spark, Hive, and AWS Glue
- Secure data in transit and at rest using AWS-native tools
- Monitor and optimize performance using built-in EMR tools
- Apply cost management strategies to large-scale workloads
Curriculum
9 modules
- Overview of batch data workflows
- Define components of a modern AWS-based data pipeline
- Identify analytics use cases across business functions
- Understand how Amazon EMR supports Spark, Hadoop, Hive, and HBase
- Interactive Demo: Launching an EMR cluster
- Explore cost management and auto scaling options
- Compare techniques for data ingestion
- Optimize data storage with S3, compression, and tiering
- Integrate with AWS Glue and AWS Lake Formation
- Implement transformation and analytics with Apache Spark
- Interactive Demo: Run Spark commands using Spark shell
- Practice Lab: Use EMR Notebooks for low-latency analytics
- Query and transform structured data using Hive on Amazon EMR
- Practice Lab: Run Hive jobs for batch processing tasks
- Automate workflows with AWS Step Functions
- Catalog and transform data using AWS Glue
- Practice Lab: Orchestrate Spark jobs using Step Functions
- Protect data using EMRFS encryption and IAM
- Interactive Demo: Enable client-side encryption in EMRFS
- Monitor performance using logs, CloudWatch, and Spark History Server
- Apply cost, performance, and security tradeoffs to pipeline design
- Activity: Design a real-world batch data analytics solution
- Combine open-source and AWS services in flexible architectures
- Use Hive, HBase, and Redshift for complex batch analytics
- Integrate EMR with AWS Glue and Lake Formation
- Practice Lab: Process and analyze batch data using Hive and HBase
- Practice Lab: Coordinate Spark jobs using AWS Step Functions
- Explore real-world scenarios for enterprise-scale analytics pipelines
- Discuss how to structure architectures to support data lakes and data warehouses
Who it's for
Data engineers and analysts working with big data on AWS.
Before you start
- Students with a minimum one-year experience managing open-source data frameworks such as Apache Spark or Apache Hadoop will benefit from this course.
Related courses
More in Amazon Web Services (AWS) →Intermediate
Building Data Lakes on AWS
Amazon Web Services (AWS)
View course →Intermediate
Building Streaming Data Analytics Solutions on AWS
Amazon Web Services (AWS)
View course →Advanced
Advanced Architecting on AWS
Amazon Web Services (AWS)
View course →Advanced
Advanced Developing on AWS
Amazon Web Services (AWS)
View course →Advanced
Advanced Generative AI Development on AWS
Amazon Web Services (AWS)
View course →Advanced
Architecting on AWS
Amazon Web Services (AWS)
View course →Let's plan your next step together.
Tell us the course or certification you have in mind and we'll send the outline, upcoming dates, fees and private-team options.