AWS Associate Data Engineer Training Course in Oxford

AWS Associate Data Engineer Training Course Outline

Domain 1: Data Ingestion and Transformation

• Perform Data Ingestion
  • Throughput and Latency Characteristics for AWS Services that Ingest Data
  • Data Ingestion Patterns
  • Streaming Data Ingestion
  • Batch Data Ingestion
  • Replayability of Data Ingestion Pipelines
  • Stateful and Stateless Data Transactions
• Transform and Process Data
  • Creation of ETL Pipelines Based on Business Requirements
  • Volume, Velocity, and Variety of Data
  • Cloud Computing and Distributed Computing
  • How to Use Apache Spark to Process Data
  • Intermediate Data Staging Locations
• Orchestrate Data Pipelines
  • How to Integrate Various AWS Services to Create ETL Pipelines
  • Event-driven Architecture
  • How to Configure AWS Services for Data Pipelines Based on Schedules or Dependencies
  • Serverless Workflows
• Apply Programming Concepts
  • Continuous Integration and Continuous Delivery
  • SQL Queries
  • Infrastructure as Code
  • Distributed Computing
  • Data Structures and Algorithms
  • SQL Query Optimisation

Domain 2: Data Store Management

• Choose a Data Store
  • Storage Platforms and Their Characteristics
  • Storage Services and Configurations for Specific Performance Demands
  • Data Storage Formats
  • How to Align Data Storage with Data Migration Requirements
  • How to Determine the Appropriate Storage Solution for Specific Access Patterns
  • How to Manage Locks to Prevent Access to Data
• Understand Data Cataloguing Systems
  • How to Create a Data Catalog
  • Data Classification Based on Requirements
  • Components of Metadata and Data Catalogs
• Manage the Lifecycle of Data
  • Appropriate Storage Solutions to Address Hot and Cold Data Requirements
  • How to Optimise the Cost of Storage Based on the Data Lifecycle
  • How to Delete Data to Meet Business and Legal Requirements
  • Data Retention Policies and Archiving Strategies
  • How to Protect Data with Appropriate Resiliency and Availability
• Design Data Models and Schema Evolution
  • Data Modelling Concepts
  • How to Ensure Accuracy and Trustworthiness of Data by Using Data Lineage
  • Best Practices for Indexing, Partitioning Strategies, Compression, and Other Data Optimisation Techniques
  • How to Model Structured, Semi-structured, and Unstructured Data
  • Schema Evolution Techniques

Domain 3: Data Operations and Support

• Automate Data Processing by Using AWS Services
  • How to Maintain and Troubleshoot Data Processing for Repeatable Business Outcomes
  • API Calls for Data Processing
  • Which Services Accept Scripting
• Analyse Data by Using AWS Services
  • Tradeoffs Between Provisioned Services and Serverless Services
  • SQL Queries
  • How to Visualise Data for Analysis
  • When and How to Apply Cleansing Techniques
  • Data Aggregation, Rolling Average, Grouping, and Pivoting
• Maintain and Monitor Data Pipelines
  • How to Log Application Data
  • Best Practices for Performance Tuning
  • How to Log Access to AWS Services
  • Amazon Macie, AWS CloudTrail, and Amazon CloudWatch
• Ensure Data Quality
  • Data Sampling Techniques
  • How to Implement Data Skew Mechanisms
  • Data Validation
  • Data Profiling

Domain 4: Data Security and Governance

• Apply Authentication Mechanisms
  • VPC Security Networking Concepts
  • Differences Between Managed Services and Unmanaged Services
  • Authentication Methods
  • Differences Between AWS Managed Policies and Customer Managed Policies
• Apply Authorisation Mechanisms
  • Authorisation Methods
  • Principle of Least Privilege as It Applies to AWS Security
  • Role-based Access Control and Expected Access Patterns
  • Methods to Protect Data from Unauthorised Access Across Services
• Ensure Data Encryption and Masking
  • Data Encryption Options Available in AWS Analytics Services
  • Differences Between Client-side Encryption and Server-side Encryption
  • Protection of Sensitive Data
  • Data Anonymisation, Masking, and Key Salting
• Prepare Logs for Audit
  • How to Log Application Data
  • How to Log Access to AWS Services
  • Centralised AWS Logs
• Understand Data Privacy and Governance.
  • How to Protect Personally Identifiable Information
  • Data Sovereignty

Who should attend this AWS Associate Data Engineer Training Course?

This AWS Associate Data Engineer Training Course is ideal for individuals eager to enhance their expertise in cloud-based data engineering using AWS technologies. It is particularly beneficial for:

• Data Engineers
• Database Administrators
• Big Data Analysts
• IT Professionals working with large datasets
• Software Developers interested in data processing
• Data Architects
• Cloud Solution Architects

Prerequisites of AWS Associate Data Engineer Training Course

There are no formal prerequisites to attend this AWS Associate Data Engineer Training Course.

AWS Associate Data Engineer Training Course Overview

Amazon Web Services (AWS) is a comprehensive cloud computing platform that offers a broad set of infrastructure services, such as computing power, storage options, and networking capabilities. Its importance lies in its flexibility, scalability, and cost-efficiency, which make it essential for businesses of all sizes that aim to optimise their IT operations and reduce overheads. For organisations, this AWS Certified Data Engineer – Associate Training provides a strategic advantage by enhancing their team’s ability to efficiently manage and analyse large data sets within the AWS ecosystem. For individuals, the training offers deep insights and practical skills in handling data engineering tasks, improving their proficiency and effectiveness in their current roles.

In the AWS Associate Data Engineer Training course, delegates will gain a robust understanding of data engineering principles on the AWS platform, focusing on data ingestion, transformation, storage, and security. The course is designed to equip participants with the skills to build, maintain, and optimise data systems and architectures in AWS, using the latest tools and technologies for effective data processing and analysis.

Course Objectives

• To perform data ingestion and establish data pipelines
• To design and implement ETL solutions on AWS
• To manage data storage and lifecycle efficiently
• To ensure data security and compliance within AWS
• To apply automation in data operations using AWS services
• To utilise AWS tools for data analysis and visualisation

After attending this training, delegates will be able to design and implement comprehensive data engineering solutions within the AWS cloud environment. They will have the skills to handle data at every stage of its lifecycle—from ingestion through to analysis and archiving—using AWS services and best practices. Additionally, they will be proficient in setting up secure, scalable, and cost-effective data infrastructures.

What’s included in this AWS Associate Data Engineer Training Course?

• World-Class Training Sessions from Experienced Instructors
• AWS Associate Data Engineer Training Certificate
• Digital Delegate Pack