Ementorhub

Spark, Hadoop, and Snowflake for Data Engineering

This course is part of Applied Python Data Engineering Specialization

Instructors: Noah Gift +2 more

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What you'll learn

Create scalable data pipelines (Hadoop, Spark, Snowflake, Databricks) for efficient data handling.

Optimize data engineering with clustering and scaling to boost performance and resource use.

Build ML solutions (PySpark, MLFlow) on Databricks for seamless model development and deployment.

Implement DataOps and DevOps practices for continuous integration and deployment (CI/CD) of data-driven applications, including automating processes.

Skills you'll gain

SQL

Big Data

Databricks

MLOps (Machine Learning Operations)

PySpark

Apache Spark

Apache Hadoop

Data Transformation

Snowflake Schema

DevOps

Data Integration

Data Quality

Data Pipelines

Data Processing

Data Warehousing

There are 4 modules in this course

This course is designed for learners who want to pursue or advance their career in data science or data engineering, or for software developers or engineers who want to grow their data management skill set. In addition to the technologies you will learn, you will also gain methodologies to help you hone your project management and workflow skills for data engineering, including applying Kaizen, DevOps, and Data Ops methodologies and best practices. With quizzes to test your knowledge throughout, this comprehensive course will help guide your learning journey to become a proficient data engineer, ready to tackle the challenges of today's data-driven world.

Snowflake

Azure Databricks and MLFLow

DataOps and Operations Methodologies

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