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Introduction
Introduction (13:24)
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1 The Basics
1.1 Introduction to Data Engineering
1.2 Computer Science Fundamentals
1.3 Introduction to Python
1.4 Python for Data Engineers
1.5 Data Preparation & Cleaning for Machine Learning
1.6 Docker Fundamentals
1.7 Successful Job Application
1.8 SQL for Data Engineers
2 Platform & Pipeline Design Fundamentals
2.1 Platform & Pipeline Design
2.2 Platform & Pipeline Security
2.3 Choosing Data Stores
2.4 Data Modeling 1
2.5 Data Modeling 2: Relational Data Modeling
2.6 Data Modeling 3: Dimensional Data Modeling
3 Fundamental Tools
3.1 API Design with FastAPI
3.2 Apache Airflow Workflow Orchestration
3.3 Apache Spark Fundamentals
3.4 Data Engineering on Databricks
3.5 Apache Kakfa
3.6 MongoDB Fundamentals
3.7 Log analysis with Elasticsearch
3.8 Snowflake for Data Engineers
3.9 dbt for Data Engineers
4 Hands-On Example Projects
4.1 Streaming Kafka, Spark, MongoDB, FastAPI and Streamlit
4.2 Data Engineering on AWS
4.3 Data Engineering on Azure
4.4 Data Engineering on GCP
4.5 Modern Data Warehouses & Data Lakes
4.6 Machine Learning & Containerization on AWS
4.7 Storing & Visualizing Time Series Data
4.8 Contact tracing with Elasticsearch
4.9 Data Engineering on Hadoop
4.10 Dockerized ETL with AWS TDengine and Grafana
4.11 Azure Data Pipelines with Terraform
5 Certifications
5.1 Apache Spark & Kafka Bootcamp
4.11 Azure Data Pipelines with Terraform
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