AWS Data Engineering - AWS Data Engineering Training Institute
                     Introduction to Big Data 
Engineering on AWS
This presentation explores big data engineering on AWS, 
covering the lifecycle, core services, and real-world 
applications. We'll examine how cloud solutions streamline 
data processing from ingestion to analytics, highlighting 
AWS's role in scaling big data workloads.
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Core AWS Services for Big Data
Amazon S3 Amazon EMR AWS Glue
Scalable and durable object Managed Hadoop framework Serverless data integration 
storage for data lakes. for big data processing. and ETL service.
Amazon Redshift Amazon Kinesis
Petabyte-scale cloud data Real-time data streaming 
warehousing. and processing.
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Data Ingestion Strategies
Batch Ingestion Diverse Data Sources
• AWS Glue for ETL pipelines into S3. • Integrating external APIs and databases.
• Efficiently loading large datasets. • Handling semi-structured (JSON, XML) and 
unstructured data.
Real-time Ingestion
Monitoring & Logging
• Kinesis Data Streams for continuous data flow.
• Low-latency processing for immediate insights. • CloudWatch for ingestion health.
• Ensuring data integrity and audit trails.
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Data Storage and Lake 
Architecture
Building a robust data lake is crucial. Amazon S3 forms the 
foundation for scalable storage. AWS Lake Formation centralizes 
security and access control, ensuring data governance. Efficient 
partitioning and cataloging via Glue Data Catalog optimize query 
performance and cost.
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Data Processing and ETL
AWS Glue EMR & Spark Step Functions
Serverless ETL for data Parallel processing for large-scale Orchestrate complex ETL 
transformation. data. workflows.
Automated schema detection. Customizable compute State management and error 
environments. handling.
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Data Warehousing and Analytics
Amazon Redshift ML-Powered Analytics
• Columnar storage for analytical queries. • Redshift ML for in-database machine learning.
• Scalable clusters for diverse workloads. • Predictive analytics without data movement.
Querying & BI Optimization
• Redshift Spectrum for S3 data. • Workload management for performance.
• Athena for serverless query on data lakes. • Cost efficiency through elastic scaling.
• QuickSight for interactive dashboards.
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Real-Time Data Streaming
IoT Logs Fraud
IoT Data Application Logs Fraud Detection
Sensor data for immediate insights. Real-time monitoring and anomaly Instantaneous transaction analysis.
detection.
Kinesis Data Streams vs. Firehose: Choose streams for custom processing, Firehose for simple delivery. Use Kinesis 
with Lambda for real-time ETL.
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Best Practices & Career Outlook
Key Practices Career Growth
• Strategic tool selection for each data stage. • AWS Data Engineer certification enhances expertise.
• Focus on scalability, security, and cost-efficiency. • Roles in data architecture, MLOps, and analytics.
• Prioritize data governance and compliance. • Continuous learning is vital in this evolving field.
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Contact
GCP Data Engineer
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Ph. No: +91-7032290546 
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E-Mail: [email protected]
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