AWS Data Analytics Training - Data Engineering course in Hyderabad
                     Introduction to End-to-
End Data on AWS
This presentation explores building a comprehensive, end-to-end 
data solution on Amazon Web Services (AWS). We'll cover 
everything from ingestion to visualization, emphasizing cloud-
native architecture for modern data engineering.
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Data Ingestion Frameworks
Real-time Streaming Database Migration
Leverage Amazon Kinesis for high-throughput, real- Utilize AWS DMS for seamless, continuous data 
time data ingestion and processing, ideal for replication and migration from various databases to 
immediate insights. AWS.
Batch & Cataloging Large-Scale Import
AWS Glue offers robust capabilities for batch ETL For massive data volumes, AWS Snowball and 
operations and automatic data cataloging across Snowcone provide secure, physical data transfer 
your data lake. services.
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Data Storage Options on AWS
Central Data Lake
Amazon S3 serves as the foundational data lake, offering scalable, durable, and cost-effective object 
storage for all data types.
Data Warehousing
Amazon Redshift provides a fast, fully managed, petabyte-scale cloud data warehouse for analytical 
workloads.
Governed Access
AWS Lake Formation simplifies building, securing, and managing data lakes with fine-grained access 
control.
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Structured Data
Data Processing & Transformation
ETL & Prep Serverless Processing
AWS Glue provides serverless data integration for AWS Lambda enables lightweight, event-driven 
ETL, data preparation, and catalog management. processing of data, executing code without 
provisioning servers.
Big Data Workloads Workflow Orchestration
Amazon EMR is a managed cluster platform for AWS Step Functions allows visual orchestration of 
running big data frameworks like Apache Spark and complex data processing workflows and 
Hadoop. microservices.
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Real-Time & Streaming Data Pipelines
Managed Kafka
Kinesis
Amazon MSK offers a fully 
Process and analyze real-time 
managed Apache Kafka service for 
data streams with Kinesis Data 
building and running event-driven 
Streams and Firehose for efficient 
applications.
ingestion.
Use Cases Lambda Transforms
Enable use cases such as IoT AWS Lambda performs real-time 
analytics, fraud detection, and transformations and aggregations 
real-time clickstream data on streaming data events.
processing.
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Data Visualization & Insights
QuickSight Dashboards
Amazon QuickSight is a scalable, serverless BI service for 
creating interactive dashboards and reports.
Data Integration
Seamlessly integrate with data sources like Amazon Redshift, 
Athena, and RDS for comprehensive insights.
Real-time Viz
QuickSight supports real-time visualizations directly from 
streaming data sources for immediate operational insights.
Secure Sharing
Embed analytics into applications and securely share insights 
with role-based access controls.
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Security, Governance & Monitoring
Access Control Pipeline Monitoring
Implement fine-grained access policies using IAM Utilize Amazon CloudWatch and AWS CloudTrail for 
roles and AWS Lake Formation for secure data comprehensive monitoring, logging, and auditing of 
access. data pipelines.
Data Encryption Compliance & Audit
Ensure data protection with encryption at rest (e.g., Leverage tagging strategies and detailed audit logs 
S3 SSE, KMS) and in transit (e.g., TLS). for compliance requirements and cost allocation.
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Best Practices & Architecture Patterns
• Design modular and loosely coupled data pipelines for flexibility and resilience.
• Prioritize serverless services to reduce operational overhead and optimize cost efficiency.
• Automate all data workflows with CI/CD practices for consistent and reliable deployments.
• Continuously monitor resource usage and optimize for cost performance using AWS cost management tools.
• Evolve towards a self-service data platform, empowering users with governed access to data.
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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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