A tanfolyamról
Data is everywhere and the 24 hours Big Data on AWS course prepares you to design and create cloud-based Big Data solutions. Use tools from the AWS suite like Amazon EMR and Hadoop tools like Hive and Hue to process data, and Amazon Redshift and Amazon Kinesis to design and create Big Data environments that are built for cost efficiency, stability, and security.
This instructor-led, engaging Big Data on AWS course, is taught by Amazon Certified trainers with industry experience. The mix of hands-on lab sessions and classroom lessons prepares you to effectively use tools from the AWS suite for big data workloads. Professionals who want to attempt the AWS Certified Data Analytics - Specialty exam will benefit from this training.
Who Should Attend the Course
- Solutions architects
- SysOps administrators
- Data scientists
- Data analysts
What You Will Learn
-
Fundamentals of Big-Data
Learn the fundamentals of cloud-based big data solutions, including the usage of Apache Hadoop with Amazon EMR -
Launch and Configure
Master the skill of launching and configuring an Amazon EMR cluster -
Master Common Programming Frameworks
Use common programming frameworks for Amazon EMR, including Hive, Pig, and Streaming -
Improve Usage
Learn to use Hue to improve and make the usage of Amazon EMR easy -
Understand and Optimize AWS Services
Understand how services like AWS Glue, Amazon Athena, and Amazon QuickSight can be used with big data workloads -
Learn In-memory Analytics
Understand and learn to use in-memory analytics with Spark on Amazon EMR
By the end of this course, you will learn to:
- Use Apache Hadoop with Amazon EMR
- Launch and configure an Amazon EMR cluster
- Use common programming frameworks for Amazon EMR, including Hive, Pig, and Streaming
- Use Hue to improve the ease-of-use of Amazon EMR
- Use in-memory analytics with Spark on Amazon EMR
- Understand how services like AWS Glue, Amazon Kinesis, Amazon Redshift, Amazon Athena, and Amazon QuickSight can be used with big data workloads
We provide this course in English.
Tematika
Curriculum
1. Overview of Big Data
- What is big data
- The big data pipeline
- Big data architectural principals
2. Big Data ingestion and transfer
- Overview: Data ingestion
- Transferring data
3. Big data streaming and Amazon Kinesis
- Stream processing of big data
- Amazon Kinesis
- Amazon Kinesis Data Firehose
- Amazon Kinesis Video Streams
- Amazon Kinesis Data Analytics
Hands-on lab: Streaming and Processing Apache Server Logs Using Amazon Kinesis
4. Big data storage solutions
- AWS data storage options
- Storage solutions concepts
- Factors in choosing a data store
5. Big data processing and analytics
- Big data processing and analytics
- Amazon Athena
Hands-on lab: Using Amazon Athena to Analyze Log Data
6. Apache Hadoop and Amazon EMR
- Introduction to Amazon EMR and Apache Hadoop
- Best practices for ingesting data
- Amazon EMR
- Amazon EMR architecture
Hands-on lab: Storing and Querying Data on Amazon DynamoDB
7. Using Amazon EMR
- Developing and running your application
- Handling output from your completed jobs
- Launching your cluster
8. Hadoop programming frameworks
- Hadoop frameworks
- Other frameworks for use on Amazon EMR
Hands-on lab: Processing Server Logs with Hive on Amazon EMR
9. Web interfaces on Amazon EMR
- Hue on Amazon EMR
- Monitoring your cluster
Hands-on lab: Running Pig Scripts in Hue on Amazon EMR
10. Apache Spark on Amazon EMR
- Apache Spark
- Using Spark
Hands-on lab: Processing NY Taxi Data Using Apache Spark
11. Using AWS Glue to automate ETL workloads
- What is AWS Glue?
- AWS Glue: Job orchestration
12. Amazon Redshift and big data
- Data warehouses vs. traditional databases
- Amazon Redshift
- Amazon Redshift architecture
13. Securing your Amazon deployments
- Securing your Amazon deployments
- Amazon EMR security overview
- AWS Identity and Access Management (IAM) overview
- Securing data
- Amazon Kinesis security overview
- Amazon DynamoDB security overview
- Amazon Redshift security overview
14. Managing big data costs
- Total cost considerations for Amazon EMR
- Amazon EC2 pricing models
- Amazon Kinesis pricing models
- Cost considerations for Amazon DynamoDB
- Cost considerations and pricing models for Amazon Redshift
- Optimizing cost with AWS
15. Visualizing and orchestrating big data
- Visualizing big data
Kinek ajánljuk
Előfeltételek
Prerequisites
- Basic knowledge of big data technologies
- Working knowledge of core AWS services
- Completed Data Analytics Fundamentals training
- Completed the AWS Technical Essentials training