AWS Data Engineer online course - AWS Data Engineering online

1. - “Harnessing the Power of AWS to Build Scalable, Real-Time Big Data and Streaming Solutions” AWS Data Engineering for Big Data & Streaming Use-Cases +91-7032290546

2. - 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. Introduction to AWS Data Engineering +91-7032290546

3. - 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. Key AWS Services for Data Engineering +91-7032290546

4. - 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. AWS Architecture for Big Data Workloads +91-7032290546

5. - 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. Streaming Data Use-Cases on AWS +91-7032290546

6. - 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. Big Data Use-Cases on AWS +91-7032290546

7. - 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. Best Practices for AWS Data Engineering +91-7032290546

8. - 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. Challenges and Solutions +91-7032290546

9. - 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. Conclusion & Future Outlook +91-7032290546

10. 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: online@visualpath.in Contact +91-7032290546

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