FRCAWS-27AWS

Building Batch Data Analytics Solutions

Name: Building Batch Data Analytics Solutions
Price: 650.00 USD
Availability: InStock

Duration · 1 dayVirtual + In-PersonInstructor-Led

$650

Course Description

This one-day instructor-led course teaches participants how to design, build, and optimize batch data analytics solutions using modern cloud-based data processing architectures. Learners gain hands-on experience with data ingestion, transformation, storage, orchestration, and analytics workflows commonly used in enterprise batch processing environments.

The course focuses on scalable and reliable batch data pipelines, data lake architectures, ETL processing, and operational best practices for analytics workloads.

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Audience Profile

This course is intended for:

Data Engineers
Analytics Engineers
ETL Developers
Data Analysts
Cloud Engineers
Database Professionals
Solutions Architects
Developers working with analytics platforms

Prerequisites

Before enrolling, you should have:

Basic understanding of databases and SQL
Familiarity with data processing concepts
General understanding of cloud computing concepts
Experience with scripting or Python is recommended

— What You'll Learn —

Learning Objectives

In this course, you will learn to:

1Understand batch analytics architecture patterns
2Design scalable batch data pipelines
3Ingest and process large datasets
4Build ETL and ELT workflows
5Store and organize analytics datasets
6Automate batch processing workflows
7Monitor and optimize analytics workloads
8Apply operational and security best practices

— Day-by-Day —

Course Outline

Module 1

Introduction to Batch Data Analytics

Topics

Overview of data analytics architectures
Batch processing fundamentals
Batch vs. streaming analytics
Common batch analytics use cases
Data engineering lifecycle overview
Components of analytics pipelines
Designing scalable batch workflows
Key Concepts Covered
Data lakes
ETL and ELT processing
Distributed data processing
Workflow orchestration
Analytics architectures

Hands-On Lab

Explore a sample analytics environment
Review batch processing architecture components

Module 2

Data Ingestion and Storage

Topics

Batch data ingestion strategies
Structured and semi-structured data processing
Data staging and landing zones
Organizing analytics datasets
Data partitioning concepts
Metadata and catalog management
Data quality validation fundamentals
Key Technologies Covered
Object storage systems
Metadata catalogs
Batch ingestion frameworks

Hands-On Lab

Ingest datasets into a centralized data repository
Organize raw and processed datasets

Module 3

Building ETL and Data Transformation Pipelines

Topics

ETL workflow fundamentals
Data cleansing and transformation
Data enrichment techniques
Schema transformation concepts
Distributed processing basics
Batch transformation optimization
Error handling and retry strategies
Key Concepts Covered
Data transformation pipelines
Distributed compute processing
Workflow dependencies
Parallel processing strategies

Hands-On Lab

Build a batch ETL pipeline
Transform and process analytics datasets

Module 4

Data Analytics and Query Processing

Topics

Analytical query concepts
Aggregation and reporting workflows
Query optimization fundamentals
Data summarization techniques
Analytical data modeling concepts
Data warehouse integration
Batch reporting strategies
Key Concepts Covered
SQL analytics
OLAP processing
Analytical datasets
Reporting architectures

Hands-On Lab

Execute analytical queries against processed datasets
Generate summarized analytics outputs

Module 5

Workflow Orchestration and Automation

Topics

Workflow orchestration fundamentals
Scheduling batch jobs
Managing pipeline dependencies
Event-driven automation concepts
Monitoring and logging
Handling failures and retries
Operational best practices
Key Concepts Covered
Job scheduling
Pipeline orchestration
Operational monitoring
Automated workflows

Hands-On Lab

Configure scheduled analytics workflows
Monitor and troubleshoot batch jobs

Module 6

Security, Governance, and Operational Excellence

Topics

Data security best practices
Access control concepts
Encryption fundamentals
Data governance principles
Compliance considerations
Cost optimization strategies
Performance tuning best practices
Key Concepts Covered
Secure analytics environments
Governance frameworks
Monitoring and observability
Cost management

Hands-On Lab

Implement security controls for analytics pipelines
Analyze performance and optimize workloads

Module 7

End-to-End Batch Analytics Capstone Exercise

Topics

Designing production-ready analytics workflows
Integrating ingestion, transformation, and reporting
Operationalizing analytics pipelines
Troubleshooting analytics environments
Applying architectural best practices
Capstone Hands-On Lab
Participants build an end-to-end batch analytics solution that includes:
Data ingestion
ETL processing
Data transformation
Analytics querying
Workflow orchestration
Monitoring and reporting
Course Wrap-Up
Final Review
Review key batch analytics concepts
Architecture and implementation best practices
Common operational challenges and solutions
Skills Validation
Practical lab reviews
Scenario-based discussions
Interactive Q&A
Next Steps
Advanced data engineering learning paths
Data warehousing and streaming analytics training
Production analytics implementation guidance

Note: Course outlines are provided as a general guide. Content, pacing, labs, and instructional emphasis may vary based on instructor expertise, student experience levels, and customer-specific learning objectives.

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