AWS Data Engineer online course - AWS Data Engineering online
                     AWS Data Engineering for 
Big Data & Streaming Use-
Cases
 - “Harnessing the Power of AWS to Build 
Scalable, Real-Time Big Data and 
Streaming Solutions”
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Introduction to AWS Data 
Engineering
 - AWS Data Engineering enables scalable, reliable, 
and cost-efficient data processing in the cloud.
 - Supports batch, real-time, and streaming data 
pipelines for analytics and AI.
 - Helps design, automate, and monitor end-to-end 
workflows.
 - Ideal for handling structured, semi-structured, and 
unstructured data.
 - Essential for organizations seeking insights from Big 
Data and real-time analytics.
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Key AWS Services for Data 
Engineering
 - Amazon S3 – Central data lake storage for 
structured and raw data.
 - AWS Glue – ETL and data cataloging service for data 
preparation.
 - Amazon Redshift – High-performance cloud data 
warehouse for analytics.
 - Amazon Kinesis – Real-time streaming data 
ingestion and processing.
 - AWS Lambda – Serverless compute for event-driven 
processing.
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AWS Architecture for Big 
Data Workloads
 - Data is ingested from multiple sources into 
Amazon S3 or Kinesis.
 - AWS Glue transforms and cleans data for 
analytics or ML use.
 - Processed data is stored in Amazon Redshift or 
DynamoDB.
 - Visualization is achieved using Amazon 
QuickSight.
 - Workflows are automated and monitored through 
AWS Step Functions and CloudWatch.
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Streaming Data Use-Cases 
on AWS
 - Real-Time Analytics – Monitor user behavior, 
transactions, or IoT data.
 - Log & Event Processing – Stream logs for instant 
anomaly detection.
 - Recommendation Engines – Deliver personalized 
content in real-time.
 - Operational Dashboards – Real-time KPIs using 
Redshift and QuickSight.
 - Fraud Detection – Identify patterns using streaming 
data and ML.
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Big Data Use-Cases on 
AWS
 - Data Lake Implementation – Centralize all enterprise 
data in Amazon S3.
 - Predictive Analytics – Build ML models on 
SageMaker using historical data.
 - Batch ETL Workflows – Process large-scale data in 
Glue or EMR.
 - Customer 360 View – Integrate CRM, ERP, and web 
data sources.
 - Enterprise Reporting – Generate insights using 
Redshift and QuickSight.
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Best Practices for AWS 
Data Engineering
 - Design modular and reusable data pipeline 
components.
 - Optimize storage using partitioning, compression, 
and Parquet format.
 - Secure data with IAM roles, encryption, and VPC 
configurations.
 - Automate CI/CD pipelines using AWS CodePipeline 
and CodeBuild.
 - Monitor performance with CloudWatch and manage 
costs with Budgets.
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Challenges and Solutions
 - Challenge: Managing large-scale ingestion → Use 
Kinesis for auto-scaling.
 - Challenge: Schema consistency → Use AWS Glue 
Data Catalog.
 - Challenge: Data latency → Use Redshift 
Spectrum for faster queries.
 - Challenge: Cost optimization → Apply lifecycle 
policies and reserved instances.
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Conclusion & Future 
Outlook
 - AWS simplifies Big Data and Streaming workflows 
with automation.
 - Real-time insights enable faster and smarter 
decisions.
 - AI/ML integration (SageMaker, Bedrock) enhances 
predictive analytics.
 - Future trends: Serverless data engineering and real-
time AI pipelines.
 - Adopting AWS Data Engineering ensures agility and 
innovation.
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Contact
AWS Data Engineering
Address:- Flat no: 205, 2nd Floor, Nilgiri Block, 
Aditya Enclave, Ameerpet, Hyderabad-1 
 Ph. No: +91-7032290546 
 Visit: www.visualpath.in 
 E-Mail: [email protected]
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Contact
THANK 
YOU
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