Overview
Use AWS Glue crawlers to build a Data Catalog and run serverless ETL (Extract, Transform, Load) jobs., Run serverless SQL queries directly on data in S3 using Amazon Athena., Build and share interactive Business Intelligence (BI) dashboards from your data using Amazon QuickSight., Deploy and query a high-performance data warehouse using Amazon Redshift.
Developers who need to build data processing pipelines or analytics features into their applications.
An active AWS account (Free Tier recommended) is essential to follow along with the hands-on demos., A basic familiarity with data concepts (like what a database is, what SQL stands for, and the idea of ETL) is highly recommended.
Stop reading dry documentation and start seeing AWS Analytics services in action!
The AWS Analytics ecosystem is incredibly powerful, but learning it from theory alone is difficult. To truly understand how to build a data pipeline, you need to see these services working together.
This course is built on one simple principle: learn by watching practical, hands-on demos.
We skip the long, boring theory slides and get straight into the AWS console. This course is a comprehensive collection of over-the-shoulder lab demonstrations where I walk you through the setup, configuration, and real-world use of the most important AWS Analytics services. You will see how to build, why we're clicking each button, and what the end result looks like.
This is not a "what is" course; this is a "how-to" course.
Join me as we build, configure, and run end-to-end analytics solutions. You will get detailed, practical demos of:
Amazon Athena: Running serverless SQL queries directly on your S3 data.
AWS Glue: Using crawlers to build a Data Catalog and running serverless ETL (Extract, Transform, Load) jobs.
Amazon EMR: Launching and managing a big data cluster (like Spark) to process massive datasets.
Amazon Redshift: Setting up a high-performance data warehouse and running complex analytical queries.
Amazon QuickSight: Building and sharing interactive Business Intelligence (BI) dashboards to visualize your data.
Amazon OpenSearch Service: Deploying a cluster for powerful log analytics, monitoring, and real-time search.
Amazon MSK (Managed Kafka) & Kinesis: Understanding and configuring services for real-time data streaming.
AWS Lake Formation: Building, securing, and managing a data lake in a matter of minutes.
...and more!
Who is this course for?
Data Engineers and Data Analysts who want to learn to build pipelines on AWS.
Solutions Architects who need to design modern data analytics solutions.
Developers who need to integrate data streaming or analytics into their applications.
Individuals preparing for the AWS Certified Data Analytics - Specialty exam.
Any tech professional who learns best by "seeing" and "doing" rather than just reading slides.
If you're ready to gain practical, hands-on confidence in the AWS Analytics stack, this course is for you.
Enroll today and let's start building!
Deepak Dubey
Highly Skillful, Knowledgeable and Result-Driven IT Professional with over 20 years of Experience (Totally Hands-on).
AWS Cloud Architecture, Design, Development, DevOps, SysOps - EC2, ECS, EKS, Elastic Beanstalk, Lambda, S3, Glacier, Storage Gateway, RDS, DynamoDB, ElastiCache, Redshift, VPC, ELB, Route 53, CloudWatch, CloudFormation, CloudTrail, OpsWorks, Elastic MapReduce, Kinesis, Data Pipeline, SWF, SQS, SNS, SES
Big Data Engineering - Hadoop, HDFS, YARN, Hive, Spark, Kafka, MongoDB, HBase, Cassandra, Storm, ZooKeeper, Redis, Elastisearch, Logstash, Kibana, Grafana
Data Science, Machine Learning, AI, Python, TensorFlow, Natural Language Processing
Java/Java EE/Web/Web Services, Spring, Spring MVC, Microservices, RESTful API, Spring Boot, Hibernate Development
Full Stack Development experience using MERN (MongoDB, Express, ReactJS, Node) Stack. Proficient with Angular, HTML5, CSS3, Bootstrap, Ant Design
DevOps - Kubernetes, Docker, Jenkins, Maven, Gradle, GIT, JIRA, Terraform, Puppet, Chef, Ansible, Prometheus
Google Cloud Platform - Cloud SQL, Datastore, Bigtable, Cloud Spanner, Cloud Dataflow, Dataproc, BigQuery, AI Platform, Datalab, Dataprep, Data Studio, Cloud Composer
I hold 50+ Internationally recognized Professional Certifications listed as below:-
Amazon Web Services (9X Certified)
AWS Certified Solutions Architect - Professional
AWS Certified DevOps Engineer - Professional
AWS Certified Machine Learning - Specialty
AWS Certified Security - Specialty
AWS Certified Advanced Networking - Specialty
AWS Certified Data Analytics - Specialty
AWS Certified Database - Specialty
Google Cloud Certified Professional Machine Learning Engineer
TensorFlow Developer Certificate
Google Cloud Certified Professional Data Engineer
Microsoft Azure
Microsoft Certified: Azure Solutions Architect Expert
Microsoft Certified: Azure AI Engineer Associate
Big Data
Elasticsearch, Logstash, Kibana Certified Engineer
Cloudera Certified Associate (CCA) Spark and Hadoop Developer
Confluent Certified Developer For Apache Kafka
Confluent Certified Operator for Apache Kafka
DevOps
Certified Kubernetes Application Developer
Certified Kubernetes Administrator
Docker Certified Associate
Certified Jenkins Engineer 2018
Python
Certified Python Associate
Java
OCP Java SE 6 & 8 Programmer
SOA and Web Services
Oracle SOA Suite 12c Certified Implementation Specialist
Oracle Certified Expert, Java EE 6 Web Services Developer
Security
CISSP & CISM
Enterprise Architecture and Management
TOGAF 9 Certified, PRINCE2 and ITIL