Building a Real-Time ETL Pipeline with AWS, Apache Spark, and Snowflake | Adzuna Job Data Project скачать в хорошем качестве

Building a Real-Time ETL Pipeline with AWS, Apache Spark, and Snowflake | Adzuna Job Data Project

Here's a professional and detailed *YouTube video description* for your project, incorporating all the services used in the ETL pipeline: 🎥 *Building a Real-Time ETL Pipeline with AWS, Apache Spark, and Snowflake | Adzuna Job Data Project* In this end-to-end data engineering project, we demonstrate how to build a *scalable, event-driven ETL pipeline* using **AWS services**, **Apache Spark**, and **Snowflake**, powered by real-time job listings from the **Adzuna API**. Learn how to orchestrate, transform, and load public job market data using a fully serverless architecture with modern cloud tools. 🔧 *Used Services and Tools:* Please go through this medium article for any more detail.   / building-a-scalable-etl-pipeline-using-aws...   *Adzuna API* – Public job listing data used as the source for ETL. *AWS Cloud* – The foundation for deploying and managing all services. *AWS IAM* – Manages roles and permissions for secure access across services. *Amazon CloudWatch* – Monitors Lambda logs and metrics for observability. *Amazon EventBridge* – Triggers scheduled workflows to automate the ETL process. *AWS Step Functions* – Orchestrates a multi-step ETL workflow using Lambda. *AWS Lambda* – Executes custom Python code to extract, transform, and load data. *Amazon S3* – Acts as the staging layer for extracted and transformed data files. *AWS Glue + Apache Spark* – Performs distributed transformations on raw JSON to generate clean Parquet files. *Snowflake* – Centralized cloud data warehouse for scalable querying and analysis. *Snowpipe* – Automates continuous data ingestion from S3 to Snowflake in near real-time. 📊 *What You’ll See in the Video:* End-to-end ETL architecture design Trigger-based scheduling with EventBridge Lambda-based Python code for API data ingestion Spark-powered data transformation using AWS Glue Continuous data loading with Snowpipe Best practices for monitoring and permissions setup 💡 *Ideal For:* Data Engineers Cloud Architects ETL Developers Anyone interested in real-time data pipelines using AWS & Snowflake 📚 *Project Outcome:* A fully automated, cloud-native pipeline that processes live job data and enables real-time analytics in Snowflake – with minimal operational overhead. 👍 *Like, share, and subscribe* for more cloud-native data engineering projects! \#AWS #ApacheSpark #Snowflake #ETL #DataPipeline #DataEngineering #Serverless #StepFunctions #Snowpipe #Glue #Lambda #S3 #CloudWatch #EventBridge #Adzuna #BigData