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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.
Master running Apache Spark on Kubernetes without permanent clusters - deploy jobs that auto-scale, self-cleanup, and eliminate costly always-on infrastructure.
Master building a distributed data lakehouse from scratch using Apache Iceberg, Trino, Airflow, DBT, MinIO, and Nessie in this comprehensive hands-on project.
Discover Apache Iceberg's powerful open table format for big data analytics, exploring its key features, benefits, and differences from Delta Lake and Hudi in just 10 minutes.
Master building AI-powered data pipelines with Apache Airflow 3.0 and Ollama that automatically detect, diagnose, and heal data quality issues in real-time without manual intervention.
Master building a complete churn prediction system using Apache Airflow, MLFlow, and Streamlit with industry best practices for ML model development and deployment.
Master production-grade sales forecasting ML pipelines using Astro and Apache Airflow, from data ingestion to real-time inference with Streamlit UI deployment.
Develop an end-to-end data engineering project for real-time stock market anomaly detection using Quix Streams, Redpanda, and Docker. Learn to build a complete data pipeline and deploy an advanced ML model.
Design and implement a real-time data warehouse with Apache Airflow, Kafka, and Pinot. Create custom hooks, ingest batch and streaming data, and visualize evolving data using Apache Superset.
Optimize SQL queries for data engineering projects using dbForge Studio. Learn database restoration, data generation, profiling, analysis, and design techniques in this comprehensive walkthrough.
Develop an end-to-end algorithmic trading system using Apache Flink, Kafka, and Python. Learn real-time data processing, custom algorithms, and financial market insights.
Design, implement, and maintain secure, scalable lakehouse architectures using Apache Spark, Kafka, Flink, Delta Lake, AWS, and open-source tools. Unlock data's potential through advanced analytics and machine learning.
Design, implement, and maintain scalable lakehouse architectures using Apache Spark, Kafka, Flink, Delta Lake, AWS, and open-source tools. Enhance data potential through advanced analytics and machine learning.
Build a real estate data engineering pipeline using AI, WebSocket, Docker, Kafka, Spark, and Cassandra. Learn to gather, process, and store property data in real-time with advanced tools and techniques.
Master end-to-end data warehouse design, from logical architecture to physical implementation. Learn AWS Redshift, data loading, and medallion architecture for efficient data engineering.
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