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Develop robust data pipelines using Apache Spark, Azure Databricks, and DBT. Master end-to-end data engineering with Azure, covering ingestion, integration, and transformation in a modern data architecture.
Build a real-time voting system using Apache Kafka, Spark, and Streamlit. Learn end-to-end data engineering, from voter accreditation to live result dashboards and winner determination.
Build an end-to-end data engineering project using Apache Flink, Kafka, Elasticsearch, and Docker. Learn real-time data processing, create scalable pipelines, and develop streaming dashboards for sales analytics.
Implement real-time Change Data Capture streaming using Docker, Postgres, Debezium, Kafka, Apache Spark, and Slack. Create an efficient data pipeline for responsive data engineering projects.
Develop an end-to-end sales analytics project using Apache Flink. Learn data ingestion, processing, complex transformations, and custom output formats for big data processing.
Master AWS EMR for data processing: set up, submit Spark jobs, visualize on Hadoop UI, and leverage cloud computing for efficient big data handling.
Create and submit Java Spark jobs to clusters. Learn end-to-end data engineering with Airflow, Docker, Spark, Scala, Python, and Java. Build and process jobs in multiple languages for live results.
Build end-to-end data engineering projects using Apache Airflow, Docker, Spark, and multiple programming languages. Learn to create and submit jobs to Spark clusters for processing and view real-time results.
Build a real-time data streaming pipeline using TCP/IP Socket, Apache Spark, OpenAI LLM, Kafka, and Elasticsearch. Process 7 million records for sentiment analysis and efficient data indexing.
Build a comprehensive Reddit data pipeline using AWS services, integrating Airflow, S3, Glue, Athena, and Redshift for efficient ETL processes and analytics.
Master modern data engineering with DBT and BigQuery. Set up, link, transform data, convert tables to views, seed data, write tests, and generate documentation in this comprehensive tutorial.
Set up Spark master-worker architecture in Docker on Azure, process and visualize Japan visa data using PySpark and Plotly. Clean, transform, and gain insights into visa trends through interactive data analysis.
Explore football data engineering: Extract Wikipedia data, store in Azure, migrate with Data Factory, query with Synapse, and visualize in Tableau. Master end-to-end data processes.
Extract, store, and analyze football data using Azure services and Apache Airflow. Develop end-to-end data engineering skills through hands-on implementation of a real-world project.
Extract, clean, and analyze football data using Apache Airflow, Azure services, and Tableau. Build a comprehensive data pipeline from Wikipedia to interactive visualizations.
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