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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.
Build a real-time anomaly detection system using streaming data engineering techniques. Learn end-to-end implementation for monitoring and identifying unusual patterns in data streams.
Discover how to architect and monitor high-throughput data pipelines using Kafka, Spark, ELK Stack, and Prometheus for processing billions of records efficiently in real-time.
Master real-time log processing by building a scalable system using Apache Airflow, Kafka, and Elasticsearch. Learn system architecture, website log production, and cluster setup for handling billions of records.
Master high-throughput data pipeline monitoring using Elasticsearch, Filebeat, and Logstash. Set up real-time tracking for systems processing 1.2B records/hour while implementing effective log management and querying capabilities.
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.
Build a smart city real-time data pipeline using AWS, Apache tools, and Docker. Learn end-to-end data engineering from ingestion to visualization with PowerBI, covering IoT, streaming, and cloud technologies.
Master Kubernetes for data engineering: set up, deploy dashboard, run Apache Airflow with Helm charts. Learn kubectl, manage clusters, and optimize workflows for scalable data processing.
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