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Spark, Hadoop, and Snowflake for Data Engineering
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Spark, Hadoop, and Snowflake for Data Engineering

This course is part of Applied Python Data Engineering Specialization.

Course Cost

Free course

Advanced

Skill Level

28 Hours

Self-paced lessons

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Applied Python Data Engineering Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

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3.8

7,839 Enrolled

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English

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olive-leaves-logo

3.8

7,839 Enrolled

olive-leaves-logo

English

What you'll learn

  • Create scalable data pipelines with modern platforms

  • Optimize data processing with clustering

  • Implement ML solutions using PySpark

  • Apply DataOps and DevOps practices

  • Manage end-to-end data engineering workflows

Skills you'll gain

Big Data
Apache Spark
Hadoop
Snowflake
PySpark
MLFlow
DataOps
DevOps
Data Pipeline
Cloud Computing

This course includes:

10.4 Hours PreRecorded video

21 quizzes

Access on Mobile, Tablet, Desktop

FullTime access

Shareable certificate

Get a Completion Certificate

Share your certificate with prospective employers and your professional network on LinkedIn.

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Certificate
Certificate

Get a Completion Certificate

Share your certificate with prospective employers and your professional network on LinkedIn.

CREATED BY

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PROVIDED BY

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Top companies offer this course to their employees

Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

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There are 4 modules in this course

This comprehensive course focuses on enterprise-scale data engineering platforms and methodologies. Students learn to build and optimize data pipelines using Hadoop, Spark, and Snowflake, while mastering PySpark for data processing. The curriculum covers advanced topics including Databricks integration, MLFlow for machine learning lifecycle management, and DataOps practices. Through hands-on projects and real-world applications, learners develop skills in implementing scalable data solutions and managing end-to-end data engineering workflows.

Overview and Introduction to PySpark

Module 1 · 7 Hours to complete

Snowflake

Module 2 · 4 Hours to complete

Azure Databricks and MLFLow

Module 3 · 5 Hours to complete

DataOps and Operations Methodologies

Module 4 · 12 Hours to complete

Fee Structure

Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Applied Python Data Engineering Specialization

Reviews

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Spark, Hadoop, and Snowflake for Data Engineering

Advanced

Skill Level

28 Hours

Self-paced lessons

Course Cost

Free course

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.