Spark, Hadoop, and Snowflake for Data Engineering

 

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.

Join Free: Spark, Hadoop, and Snowflake for Data Engineering

There are 4 modules in this course

e.g. This is primarily aimed at first- and second-year undergraduates interested in engineering or science, along with high school students and professionals with an interest in programmingGain the skills for building efficient and scalable data pipelines. Explore essential data engineering platforms (Hadoop, Spark, and Snowflake) as well as learn how to optimize and manage them. Delve into Databricks, a powerful platform for executing data analytics and machine learning tasks, while honing your Python data science skills with PySpark. Finally, discover the key concepts of MLflow, an open-source platform for managing the end-to-end machine learning lifecycle, and learn how to integrate it with Databricks.

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.

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Hadoop Platform and Application Framework

 

There are 5 modules in this course

This course is for novice programmers or business people who would like to understand the core tools used to wrangle and analyze big data. With no prior experience, you will have the opportunity to walk through hands-on examples with Hadoop and Spark frameworks, two of the most common in the industry. You will be comfortable explaining the specific components and basic processes of the Hadoop architecture, software stack, and execution environment.   In the assignments you will be guided in how data scientists apply the important concepts and techniques such as Map-Reduce that are used to solve fundamental problems in big data.  You'll feel empowered to have conversations about big data and the data analysis process.

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Hadoop Tutorial for Beginners | Hadoop Tutorial | Big Data Hadoop Tutorial for Beginners | Hadoop

Data Analysts and Hadoop experts are in high demand. The Big Data and Data Analytics industry is growing fast and wide, with a growth rate of 23% through 2026 and average salaries of $85,000. IT mavens like Google, Amazon and IBM as well as growing start-ups are always in search of Big Data experts. So we have come up with a tutorial on Apache Hadoop to help you get started on your Data Analytics path!

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The topics that will covered in the course include:
1. Introduction to Big Data - 03:03
2. ETL (Extract-Transform-Load) - 18:10
3. Introduction to Hadoop - 32:38
4. Distributed Computing - 46:20
5. Hadoop Architecture - 54:52
6. HDFS File Storage - 01:01:51
7. Introduction to Oozie and HDFS Processing - 01:15:01
8. Hadoop Clusters - 01:20:26
9. Hadoop Ecosystem - 01:39:09
10. Introduction to MapReduce - 02:07:18
11. Understanding MapReduce with an example - 02:20:32
12. MapReduce Practical Example - 02:37:33
13. Comparing MapReduce programming with Java - 02:49:07
14. MapReduce Hands On Word Count Program - 02:53:59
15. Word Count Program Code - 03:14:09
16. Apache Hadoop YARN - 03:54:37

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