Build Modern Data Analytics Solutions on AWS Training Course
Length
4 days / 4 weeks
Price
$2199
Days
Mon - Fri
Why Choose This Course
Build Modern Data Analytics Solutions on AWS is an instructor-led course focused on how to design, build, and operate modern analytics workloads using core AWS data services. It is taught as a structured collection of four one‑day classes that cover data lakes, batch analytics, Redshift data warehousing, and streaming analytics, giving learners a complete view of how to ingest, transform, secure, and analyze data at scale on AWS. The curriculum aligns to current AWS guidance and uses services such as AWS Lake Formation, AWS Glue, Amazon EMR, Amazon Kinesis, and Amazon Redshift.
You learn how to modernize data solutions end to end, including cataloging and governance for data lakes, batch and streaming pipelines, cost‑aware storage choices, and performance‑tuned analytics on Redshift. Hands‑on practice reinforces design decisions such as partitioning, data formats, security controls, and workload orchestration so you can apply the concepts in real environments.
The course emphasizes practical value: designing secure, governed data lakes, building resilient pipelines for batch and streaming use cases, tuning Redshift workloads, and implementing monitoring and cost optimization. The structure makes it suitable for data engineers, data platform engineers, and solutions architects who want exam‑aligned content and hands‑on practice using AWS services. A certificate of course attendance is included.
Prerequisites
- There are no formal prerequisites for this course.
Exam
- Candidates can achieve this certification by passing the following exam(s).
AWS Certified Data Engineer – Associate (DEA‑C01).
Books
Delivery
- Live virtual online training attend in real-time from anywhere
Skills Gained
- Design modern AWS data architectures across data lake, batch, and streaming patterns.
- Build secure data lakes with AWS Lake Formation, including permissions and governance.
- Create and maintain catalogs with AWS Glue crawlers and the Glue Data Catalog.
- Ingest batch data and transform datasets using AWS Glue and Amazon EMR with Apache Spark.
- Implement streaming ingestion and processing using Amazon Kinesis services.
- Model, ingest, and optimize analytics workloads on Amazon Redshift.
- Apply partitioning, compression, and columnar formats for query performance and cost efficiency.
- Orchestrate pipelines and implement operational monitoring and troubleshooting.
- Implement data security, encryption, and access control in analytics solutions.
- Plan data lifecycle management and storage classes for lake and warehouse data.
- Tune analytics queries and workloads for performance on Redshift.
- Apply exam‑aligned best practices across ingestion, stores, operations, and governance.
Audience
- Data engineers and data platform engineers building pipelines and analytics solutions on AWS.
- Solutions architects responsible for designing secure, scalable data platforms.
- Developers working on streaming analytics and real‑time data applications.
Course Schedule & Pricing
Choose the schedule that fits your life — all options include full course materials & certification support
Full-time immersion for rapid certification readiness.
Balance your career while you upgrade your skills.
Maximum flexibility for busy working professionals.
Outline
- Data lake concepts and reference architectures on AWS.
- Data ingestion, cataloging, and preparation for lake workloads.
- Data processing patterns for lakes, including serverless and Spark‑based ETL.
- Building a governed data lake with AWS Lake Formation.
- Additional Lake Formation configurations and workflows.
- Architecture review and lake design considerations.
- Overview of analytics data pipelines and core components.
- Amazon EMR foundations and cluster concepts.
- Ingestion and storage patterns for EMR batch pipelines.
- High‑performance batch analytics using Apache Spark on EMR.
- Processing and analyzing batch data with Hive on EMR.
- Serverless data processing options in analytics pipelines.
- Security and monitoring for EMR environments.
- Designing batch analytics solutions and modern data architectures.
- Using Amazon Redshift in the analytics pipeline and core Redshift concepts.
- Data ingestion, storage, and optimization techniques for Redshift.
- Security and monitoring for Redshift clusters.
- Designing data warehouse analytics solutions on AWS.
- Streaming analytics overview and streaming services in the pipeline.
- Real‑time data processing with Amazon Kinesis.
- End‑to‑end integration considerations across lake, batch, warehouse, and streaming.
Terms & Conditions
Frequently Asked Questions (FAQ's)
Is this course aligned to an AWS certification
What AWS services are covered in the hands‑on practice
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Our Partnership
Reliable certification testing is vital for validating professional skills in today’s tech-driven world. As a Pearson VUE Authorised Centre, we provide a secure environment for globally recognised IT exams. This partnership ensures convenient access to certifications with the highest standards of integrity and accuracy.
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