AWS Data Engineer Certification Online Training
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About Course
The AWS Data Engineer – Associate credential verifies proficiency in fundamental data-related AWS services, as well as aptitude in data ingestion and transformation, pipeline orchestration using programming ideas, data modeling, data life cycle management, and data quality assurance. Enroll now to earn your AWS Data Engineer certification.
AWS Data Engineer Training Course Syllabus
- Introduction to Cloud Computing
- Cloud Computing Deployments Models
- Amazon Web Services Cloud Platform
- The Cloud Computing Difference
- AWS Cloud Economics
- AWS Virtuous Cycle
- AWS Cloud Architecture Design Principles
- Why AWS for Big Data – Reasons
- Why AWS for Big Data – Challenges
- Databases in AWS
- Relational vs Non-Relational Databases
- Data Warehousing in AWS
- Services for Collecting, Processing, Storing, and Analyzing Big Data
- Amazon Redshift
- Amazon Kinesis
- Amazon EMR
- Amazon DynamoDB
- Amazon Machine Learning
- AWS Lambda
- Amazon Elasticsearch Service
- Amazon EC2 (big data analytics software on EC2 instances)
- Amazon Redshift
- Amazon Kinesis
- Amazon EMR
- Amazon DynamoDB
- Amazon Machine Learning
- AWS Lambda
- Amazon Elasticsearch Service
- Amazon EC2 (big data analytics software on EC2 instances)
- Key Takeaway
- Knowledge Checks
- Lesson End Project
- Objectives
- Amazon Kinesis Fundamentals
- Loading Data into Kinesis Stream
- Kinesis Data Stream High-Level Architecture
- Kinesis Stream Core Concepts
- Kinesis Stream Emitting Data to AWS Services
- Kinesis Connector Library
- Kinesis Firehose
- Transferring Data Using Lambda
- Amazon SQS
- IoT and Big Data
- IoT Framework
- AWS Data Pipeline
- AWS Data Pipeline Components
- Key Takeaway
- Knowledge Checks
- Lesson End Project
- Objectives
- Introduction to AWS Big Data Storage Services
- Amazon Glacier
- Glacier and Big Data
- DynamoDB Introduction
- The Architecture of the DynamoDB Table
- DynamoDB in AWS Ecosystem
- DynamoDB Partitions
- Data Distribution
- Local Secondary Index (LSI) **
- Global Secondary Index (GSI) **
- DynamoDB GSI vs LSI
- DynamoDB Stream
- Cross-Region Replication in DynamoDB
- Partition Key Selection
- Snowball & AWS Big Data
- AWS DMS
- AWS Aurora in Big Data
- Key Takeaway
- Knowledge Checks
- Lesson End Project
- Objectives
- Introduction to AWS Big Data Processing Services
- Amazon Elastic MapReduce (EMR)
- Apache Hadoop
- EMR Architecture
- Storage Options
- EMR File Storage and Compression
- Supported File Format and File Size
- Single-AZ Concept
- EMR Operations
- EMR Releases
- AWS Cluster
- Launching a Cluster
- Advanced EMR Setting Option
- Choosing Instance Type
- Number of Instances
- Monitoring EMR
- Resizing of Cluster
- Using Hue with EMR
- Setup Hue for LDAP
- Hive on EMR
- Hive Use Cases
- Key Takeaway
- Knowledge Checks
- Lesson End Project
- HBase with EMR
- HBase Use Cases
- Comparison of HBase with Redshift and DynamoDB
- HBase Architecture HBase on S3
- HBase and EMRFS
- HBase Integration
- HCatalog
- Presto with EMR
- Advantages of Presto
- Presto Architecture
- Spark with EMR
- Spark Use Cases
- Spark Components
- Spark Integration With EMR
- AWS Lambda in AWS Big Data Ecosystem
- Limitations of Lambda
- Lambda and Kinesis Stream
- Lambda and Redshift
- Key Takeaway
- Knowledge Checks
- Lesson End Project
- Objectives
- Introduction to AWS Big Data Analysis Services
- RedShift
- RedShift Architecture
- RedShift in the AWS Ecosystem
- Columnar Databases
- RedShift Table Design
- RedShift Workload Management
- RedShift Loading Data
- RedShift Maintenance and Operations
- Key Takeaway
- Knowledge Checks
- Lesson End Project
- Machine Learning
- Machine Learning – Use Cases
- Algorithms
- Amazon SageMaker
- Elasticsearch
- Amazon Elasticsearch Service
- Loading of Data into Elasticsearch
- Logstash
- Kibana
- RStudio
- Characteristics
- Athena
- Presto and Hive
- Integration with AWS Glue
- Comparison of Athena with Other AWS Services
- Lab Run Query on S3 Using Serverless Athena
- Key Takeaway
- Knowledge Checks
- Objectives
- Introduction to AWS Big Data Visualization Services
- Amazon QuickSight
- Amazon QuickSight – Use Cases
- LAB Create an Analysis with a Single Visual Using Sample Data
- Working with Data
- Assisted Practice: TBD
- QuickSight Visualization
- Big Data Visualization
- Apache Zeppelin
- Jupyter Notebook
- Comparison Between Notebooks
- D3.js (Data-Driven Documents)
- MicroStrategy
- Key Takeaway
- Knowledge Checks
- Objectives
- Introduction to AWS Big Data Security Services
- EMR Security
- Roles
- Private Subnet
- Encryption At Rest and In Transit
- RedShift Security
- KMS Overview
- SloudHSM
- Limit Data Access
- STS and Cross Account Access
- Cloud Trail
- Key Takeaway
- Knowledge Checks
| Live Instructor Based Training With Software |
| Lifetime access and 24×7 support |
| Certification Oriented content |
| Hands-On complete Real-time training |
| Get a certificate on course completion |
| Flexible Schedules |
| Live Recorded Videos Access |
| Study Material Provided |
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Learn As A Full Day Schedule With Discussions, Exercises,
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AWS Data Engineer Training FAQ'S
The primary duty of an AWS Data Engineer is to design, develop, oversee, and improve an organization’s data infrastructure. This includes setting up systems for storing and processing data, linking various data sources, and guaranteeing the reliability and efficiency of the data pipeline.
Among the many challenges faced by AWS data engineer include complex data pipelines, enormous data management, integrating disparate data sources, and preserving the dependability and performance of the data infrastructure. Managing real-time data processing, interacting with remote systems, and handling privacy and security concerns all provide extra challenges.
- Data ingestion
- Storage
- Data integration
- Data visualization tools
- Data warehouse
By learning AWS Data Engineer through VISWA Online Trainings, advance in your job.
AWS Data ENgineer
AWS Data Engineer
Scalable and reasonably priced data storage is provided by Amazon Simple Storage Service (Amazon S3), an object storage service. Among its common applications are data lakes, backup and recovery, and disaster recovery.
Scalable cloud computing is provided by a web service known as Amazon Elastic Compute Cloud (Amazon EC2). Among its common uses are batch processing, hosting websites and applications, and other compute-intensive processes.
