Building Batch Data Analytics Solutions on AWS Course
Design batch data analytics solutions using Amazon EMR & Spark
8 hours of course duration
Includes nine extensive & well-structured modules
Grasp in-demand skills for data engineering roles
Receive guided sessions from expert instructors
Learn through practice labs and interactive demos
Easy-to-fit weekend sessions for your hectic calendar
Convenient payment options with monthly instalments
4.9/55825 EnrolledEnrolled Learners
4.9/55825 EnrolledEnrolled Learners
Overview
What will you learn from us:
- Develop large-scale batch data processing solutions with Amazon EMR
- Explore data ingestion methods using tools like Apache Hive and HBase
- Optimise data storage and management within Amazon EMR clusters
- Work with Apache Spark on EMR to analyse massive datasets
- Use Apache Hive to process and transform batch data workflows
- Get experience building machine learning models with EMR Notebooks
- Implement key security practices to protect Amazon EMR cluster environments
Upcoming sessions
Curriculum
1
Data analytics use cases
2
Using the data pipeline for analytics
1
Using Amazon EMR in analytics solutions
2
Amazon EMR cluster architecture
3
Interactive Demo 1: Launching an Amazon EMR cluster
4
Cost management strategies
1
Storage optimization with Amazon EMR
2
Data ingestion techniques
1
Apache Spark on Amazon EMR use cases
2
Why Apache Spark on Amazon EMR
3
Spark concepts
4
Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell
5
Transformation, processing, and analytics
6
Using notebooks with Amazon EMR
7
Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR
1
Using Amazon EMR with Hive to process batch data
2
Transformation, processing, and analytics
3
Practice Lab 2: Batch data processing using Amazon EMR with Hive
4
Introduction to Apache HBase on Amazon EMR
1
Serverless data processing, transformation, and analytics
2
Using AWS Glue with Amazon EMR workloads
3
Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions
1
Securing EMR clusters
2
Interactive Demo 3: Client-side encryption with EMRFS
3
Monitoring and troubleshooting Amazon EMR clusters
4
Demo: Reviewing Apache Spark cluster history
1
Batch data analytics use cases
2
Activity: Designing a batch data analytics workflow
1
Modern data architectures
Meet your Trainer
Our Trainers
Learners Point has a reputation for high-quality training that makes a difference in people's lives. We undertake a practical and innovative approach to working closely with businesses to improve their workforce. Our expertise is wide-ranging with ample support from our expert trainers who are globally recognized and hold a diverse set of experiences in their field of expertise. We are proud of our instructors who take ownership of our distinctive and comprehensive training methodologies, help our students imbibe those with ease, and accomplish gracefully.
We at Learners Point believe in encouraging our students to embark upon a journey of lifelong learning and self-development, with the aid of our comprehensive and distinctive courses tailored to current market trends. The manifestation of our career-oriented approach is what we assure through a pleasant professional enriched environment with cutting-edge technology, and an outstanding while highly acknowledged training staff that uses up-to-date methodologies and quality course material. With our aim to mold professionals to be future leaders, our industry expert trainers provide the best in town mentorship to our students while endowing them with the thirst for knowledge and inspiring them to strive for professional and human excellence.
Learning Outcomes
After completion of this course, you will be able to:
1
Gain expertise in designing and implementing batch data analytics solutions using Amazon EMR and Apache Spark
2
Use AWS Step Functions to coordinate and automate complex data processing workflows
3
Follow best practices to enhance security, performance and cost-efficiency within EMR environments
4
Integrate tools like Apache Hive, HBase and AWS Glue for smooth and scalable data processing operations
5
Improve storage efficiency and cluster performance in Amazon EMR to deliver cost-optimised solutions
6
Get hands-on experience working with Spark and Hadoop for real-time data analysis and insights
Learners Point Certificate
Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.
Prerequisites
To enrol in this Building Batch Data Analytics Solutions on AWS Course, candidates must fulfil these eligibility requirements:
- A minimum one-year experience in managing open-source data frameworks such as Apache Spark or Apache Hadoop
- Completed either AWS Technical Essentials or Architecting on AWS
- Completed either Building Data Lakes on AWS or Getting Started with AWS Glue
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Frequently asked questions
The Building Batch Data Analytics Solutions on AWS Course is designed to teach professionals how to design, build and manage scalable batch data processing solutions using various Amazon Web Services (AWS) tools. We focus on transforming raw data into data insights through automated, scheduled and event-driven processing workflows.
To enrol in this Building Batch Data Analytics Solutions on AWS Course, candidates must fulfil these eligibility requirements:
1. A minimum of one year of experience in managing open-source data frameworks such as Apache Spark or Apache Hadoop
2. Completed either AWS Technical Essentials or Architecting on AWS
3. Completed either Building Data Lakes on AWS or Getting Started with AWS Glue
Our training is structured to help you gain both conceptual understanding and hands-on expertise in creating scalable, automated batch data pipelines using AWS services. By the end of the course, you will learn these:
1. Design and implement batch data processing architectures using AWS best practices
2. Use AWS Glue to prepare and transform data at scale
3. Automate workflows using AWS Step Functions, Lambda and Amazon EventBridge
4. Store, catalog and query data using Amazon S3, AWS Glue Data Catalog and Amazon Athena
5. Run ETL jobs and analytics using Amazon EMR, AWS Glue and Amazon Redshift
6. Monitor, debug and optimise batch pipelines for performance and cost
During the AWS Training, you will gain several technical skills needed to design, develop and optimise batch data analytics pipelines using AWS services. The key skills you will learn are:
1. Designing Batch Data Architectures
2. Data Ingestion & Storage Management
3. Metadata Management & Data Cataloging
4. ETL Job Development with AWS Glue
5. Workflow Orchestration and Automation
6. Data Querying and Analysis
After completing this training program, you will be well-prepared for a variety of in-demand cloud and data engineering roles. Top career roles you can pursue are:
1. Data Engineer
2. Cloud Data Engineer
3. ETL Developer or Big Data Developer
4. Data Analytics Engineer
5. Data Platform Engineer
6. Solutions Architect
The Building Batch Data Analytics Solutions on AWS Training covers a set of core topics that guide you through designing, building and optimising batch data analytics pipelines using various AWS services. The main topics covered are:
1. Introduction to Batch Data Processing on AWS
2. Data Ingestion and Storage
3. Metadata and Data Cataloging
4. Data Transformation and ETL with AWS Glue
5. Workflow Orchestration and Automation
6. Data Querying and Analytics
No prior EMR experience is needed. This AWS course begins with an introduction to batch data processing on Amazon EMR and guides you through setup, configuration, and optimisation techniques. You get hands-on practice using EMR Notebooks, which support machine learning workflows too.
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