Develop a Big Data Analytics Pipeline Using AWS EMR
Use AWS EMR, Spark, and S3 to process massive data volumes in a distributed environment. Extract insights, run transformations, and display results in a cloud dashboard.AWS EMR (Elastic MapReduce) enables fast, cost-efficient processing of large data volumes using frameworks like Apache Spark, Hadoop, and Hive. It’s ideal for batch analytics, ETL jobs, and machine learning workflows.
Ingest raw data into AWS S3, process it using EMR + Spark jobs, store cleaned and transformed data in S3 or RDS, and display key metrics or dashboards using QuickSight or custom React dashboards.
Data Ingestion to S3
Upload raw data files (CSV, logs, JSON) to AWS S3 buckets using CLI or Lambda automation.
ETL with Apache Spark
Launch EMR clusters to perform Extract, Transform, Load (ETL) operations and aggregate data.
Data Storage and Querying
Store cleaned data in S3, Redshift, or RDS and use Athena or Hive for query access.
Data Visualization Dashboard
Build dashboards using AWS QuickSight or React + Chart.js to display KPIs and trends.
Raw data enters AWS S3 where EMR clusters pick it up for distributed processing via Spark. Cleaned data is saved back to S3 or loaded into Redshift. A frontend or QuickSight dashboard visualizes analytics in real time.
- Data Lake: AWS S3
- Processing Engine: Apache Spark running on AWS EMR
- Orchestration: AWS Step Functions or Apache Airflow (optional)
- Querying: AWS Athena or Redshift Spectrum
- Visualization: QuickSight or custom React.js dashboard
Data Processing
AWS EMR with Apache Spark, PySpark, Hadoop, or Hive for distributed computing
Data Storage
Amazon S3 as staging and final data lake; optionally Redshift for analytics
Dashboarding
AWS QuickSight or React + Recharts / Chart.js frontend with API gateway
Automation & Triggers
AWS Lambda or Step Functions to schedule jobs and handle data pipelines
1. Upload Raw Dataset to S3
Store large datasets (e.g., sales logs, IoT metrics) in S3 buckets with folder partitioning.
2. Set Up EMR Cluster
Launch an EMR cluster with Spark and submit jobs to clean and transform the raw data.
3. Store Processed Output
Save outputs back into S3 or load into Redshift/Athena for ad-hoc querying and analysis.
4. Build Dashboard
Use QuickSight or React-based dashboard to visualize trends, aggregates, and KPIs.
5. Automate Pipeline
Schedule Spark jobs with Lambda or Step Functions to run periodically or on triggers.
Harness the Power of Big Data with Cloud
Process, analyze, and visualize massive datasets using AWS EMR, Spark, and S3—all within a highly scalable and cost-effective pipeline.
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