Spark, Hadoop, and Snowflake for Data Engineering

Spark, Hadoop, and Snowflake for Data Engineering

Spark, Hadoop, and Snowflake for Data Engineering This course is part of Applied Python Data Engineering Specialization Taught in English Instructors: Noah Gift +2 more Close Instructors Instructor ratings We asked all learners to give feedback on our instructors based on the quality of their teaching style. Close 3.4 (5 ratings) Noah Gift Duke University

Description

In the ever-evolving field of data engineering, staying abreast of the latest technologies and tools is crucial in order to effectively process and analyze large volumes of data. Three platforms that have gained significant traction in recent years are Spark, Hadoop, and Snowflake. These platforms offer powerful capabilities for data engineering, enabling organizations to efficiently manage and manipulate data for a wide range of analytical purposes.

Apache Spark is an open-source distributed computing system that provides lightning-fast data processing capabilities. It is particularly well-suited for handling complex data processing tasks, such as real-time data streaming, machine learning, and graph processing. Spark’s in-memory processing engine allows for rapid execution of data processing tasks, making it a popular choice for data engineering tasks that require high performance and scalability.

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Spark, Hadoop, and Snowflake for Data Engineering

This course is part of Applied Python Data Engineering Specialization

Taught in English

Noah Gift
Kennedy Behrman
Matt Harrison

Instructors: Noah Gift

4,359 already enrolled

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

3.5

(17 reviews)

Advanced level

Recommended experience

29 hours (approximately)
Flexible schedule
Learn at your own pace

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.

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Assessments

21 quizzes

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