Building Batch Data Analytics Solutions on AWS
Here 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.
1 Day / 8 Hours
Live Class
Certificate on completion
You will learn about the following
- Compare the attributes and advantages of data lakes, data warehouses, and contemporary data architectures.
- Create and put into action a batch data analytics system.
- Determine the most effective methods for data storage, including compression, and use them.
- Choose and implement the best alternatives for ingesting, transforming, and storing data.
- For a specific business use case, select the proper clusters, auto-scaling, instance and node kinds, and network topology.
- Recognize how data processing and storage impact the analysis and visualization techniques required to produce relevant business insights.
- Secure analytics workloads to find and fix issues with data in transit and at rest.
- Implement best practices for cost management
What experience you need
- Completed either AWS Technical Essentials or Architecting on AWS
- Completed either Building Data Lakes on AWS or Getting Started with AWS Glue.
Who should take this course
- Platform engineers for data
- Designers and managers of the data analytics pipelines
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
- Cost management strategies
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
- Transformation, processing, and analytics
- Using notebooks with Amazon EMR
- 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
- Introduction to Apache HBase on Amazon EMR
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
- Monitoring and troubleshooting Amazon EMR clusters
- Demo: Reviewing Apache Spark cluster history
Module 7: Designing Batch Data Analytics Solutions
- Batch data analytics use cases
- Activity: Designing a batch data analytics workflow
Module B: Developing Modern Data Architectures on AWS
- Modern data architectures
Talk to a Learning Advisor
Popular AWS Courses
FAQs
To enroll in this course, choose the starting date and make an online payment. Once your payment is confirmed, our team will reach out to you.
You may reach out at the contact number listed on our official website or write us at info@cloudwizardconsulting.com
Wire Transfer, Credit Card, Debit Card, UPI & Purchase Order.
There is no minimum number of candidates required, we are happy to train 1 to 1 . With regards to the maximum number, we can accomodate 30 learners in one batch.
- Training Delivered by an Amazon Authorized Instructor.
- AWS Content E-Kit
- Hands-on-labs for 30 days
- Class attendance certificate
You will get the access to course content & lab on first day of your training session.
The course Completion Certificate will be issued to your email id within 2 weeks of completing your course.
A one-day course could be delivered over two half day sessions (4 hours a day), or a three-day course could be delivered over five days (4 hours a day)
MOBILE LAYOUT
Building Batch Data Analytics Solutions on AWS
1 Day / 8 Hours
Live Class
Certificate on completion
Objectives
Here’s what you’ll learn in this AWS course:
- Compare the attributes and advantages of data lakes, data warehouses, and contemporary data architectures.
- Create and put into action a batch data analytics system.
- Determine the most effective methods for data storage, including compression, and use them.
- Choose and implement the best alternatives for ingesting, transforming, and storing data.
- For a specific business use case, select the proper clusters, auto-scaling, instance and node kinds, and network topology.
- Recognize how data processing and storage impact the analysis and visualization techniques required to produce relevant business insights.
- Secure analytics workloads to find and fix issues with data in transit and at rest.
- Implement best practices for cost management
Prerequisites
To qualify for taking up this course, you’ll need the following:Â
- Completed either AWS Technical Essentials or Architecting on AWS
- Completed either Building Data Lakes on AWS or Getting Started with AWS Glue.
Intendend Audience
You can take on this course if you belong to the following category of individuals:Â
- Platform engineers for data
- Designers and managers of the data analytics pipelines
Module Breakdown
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
- Cost management strategies
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
- Transformation, processing, and analytics
- Using notebooks with Amazon EMR
- 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
- Introduction to Apache HBase on Amazon EMR
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
- Monitoring and troubleshooting Amazon EMR clusters
- Demo: Reviewing Apache Spark cluster history
Module 7: Designing Batch Data Analytics Solutions
- Batch data analytics use cases
- Activity: Designing a batch data analytics workflow
Module B: Developing Modern Data Architectures on AWS
- Modern data architectures
Talk to a Learning Advisor
Tablet View
Popular AWS Courses
FAQs
To enroll in this course, choose the starting date and make an online payment. Once your payment is confirmed, our team will reach out to you.
You may reach out at the contact number listed on our official website or write us at info@cloudwizardconsulting.com
Wire Transfer, Credit Card, Debit Card, UPI & Purchase Order.
There is no minimum number of candidates required, we are happy to train 1 to 1 . With regards to the maximum number, we can accomodate 30 learners in one batch.
- Training Delivered by an Amazon Authorized Instructor.
- AWS Content E-Kit
- Hands-on-labs for 30 days
- Class attendance certificate
You will get the access to course content & lab on first day of your training session.
The course Completion Certificate will be issued to your email id within 2 weeks of completing your course.
A one-day course could be delivered over two half day sessions (4 hours a day), or a three-day course could be delivered over five days (4 hours a day)
Building Batch Data Analytics Solutions on AWS
Here 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.
1 Day / 8 Hours
Live Class
Certificate on completion
Objectives
Here’s what you’ll learn in this AWS course:
- Compare the attributes and advantages of data lakes, data warehouses, and contemporary data architectures.
- Create and put into action a batch data analytics system.
- Determine the most effective methods for data storage, including compression, and use them.
- Choose and implement the best alternatives for ingesting, transforming, and storing data.
- For a specific business use case, select the proper clusters, auto-scaling, instance and node kinds, and network topology.
- Recognize how data processing and storage impact the analysis and visualization techniques required to produce relevant business insights.
- Secure analytics workloads to find and fix issues with data in transit and at rest.
- Implement best practices for cost management
Prerequisites
To qualify for taking up this course, you’ll need the following:Â
- Completed either AWS Technical Essentials or Architecting on AWS
- Completed either Building Data Lakes on AWS or Getting Started with AWS Glue.
Intended Audience
You can take on this course if you belong to the following category of individuals:Â
- Platform engineers for data
- Designers and managers of the data analytics pipelines
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
- Cost management strategies
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
- Transformation, processing, and analytics
- Using notebooks with Amazon EMR
- 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
- Introduction to Apache HBase on Amazon EMR
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
- Monitoring and troubleshooting Amazon EMR clusters
- Demo: Reviewing Apache Spark cluster history
Module 7: Designing Batch Data Analytics Solutions
- Batch data analytics use cases
- Activity: Designing a batch data analytics workflow
Module B: Developing Modern Data Architectures on AWS
- Modern data architectures
Talk to a Learning Advisor
FAQs
Yes, we are an AWS Advanced Tier Training Partner
Anyone who wants to start a profession in AWS cloud is fit to enroll in this course. No prior knowledge of coding or other technical skills is required.
To enroll in this course, choose the starting date and make an online payment. Once your payment is confirmed, our team will reach out to you.
You may reach out at the contact number listed on our official website or write to us at info@cloudwizard.wpenginepowered.com.
Wire Transfer, Credit Card, Debit Card, UPI & Purchase Order
There is no minimum number of candidates required, we are happy to train 1 to 1 should you wish. With regard to the maximum number, we can accommodate 30 learners in one batch.
1. Training delivered by an Amazon Authorised Instructor
2. AWS Content E-Kit
3. Hands-on labs- 30 days
4. Class attendance certificate
You will get the access to course content & lab on first day of your training session.
The course completion certificate will be issued to your email id within 2 weeks of completing your course.
A one-day course could be delivered over two half day sessions (4 hours a day), or a three-day course could be delivered over five days (4 hours a day).
