Batch Data Pipeline with Spark

Data Manipulation you own this product

This project is part of the liveProject series End-to-End Batch Data Pipeline with Spark
prerequisites
beginner Python • basics of Jupyter Notebook • intermediate SQL • basic data processing in Python (with pandas or similar libraries)
skills learned
perform operations and transformations on data using multiple functions offered by Spark’s SQL module • use Spark’s UDFs to enrich data and process it in a user-defined way • compare the performance of different UDF-implementation approaches • understand the importance of the serving layer
Mahdi Karabiben
1 week · 4-6 hours per week · BEGINNER
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Look inside

Step into the role of a data engineer working at an enterprise. Your task is to build a data lake’s serving layer and ensure that business queries run on it in a matter of seconds. You’ll start with reading cleansed data that’s already sitting in the curated layer. Then you’ll transform it, enrich it, aggregate it, and denormalize it using Apache Spark. When you’re finished, you’ll have multiple output tables that make up the serving layer of the data lake.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Mahdi Karabiben

Mahdi is a senior data engineer at Zendesk. With four years of experience in data engineering, he has worked on multiple large-scale projects within the AdTech and financial sectors. He's a Cloudera-certified Apache Spark developer and works with Big Data technologies on a daily basis, designing and building data pipelines, data lakes, and data services that rely on petabytes of data. Thanks to his degree in software engineering (with a minor in big data), he is comfortable with a wide range of technologies and concepts. He additionally writes for major Medium publications (Towards Data Science, The Startup) and technology websites (TheNextWeb, Software Engineering Daily, freeCodeCamp).

prerequisites

The liveProject is for software engineers and data professionals interested in onboarding big data processing skills including processing large amounts of data and building cloud-based data lakes. To begin these liveProjects you'll need to be familiar with the following:


TOOLS
  • Beginner Python
  • Basics of Jupyter Notebook
  • Intermediate SQL
TECHNIQUES
  • Basic distributed computing
  • Basic data processing in Python (with pandas or similar libraries)
  • Basic SQL

you will learn

In this liveProject, you’ll learn to use multiple Apache Spark functionalities and features to generate the tables of the data lake’s serving layer.


  • Perform operations and transformations on data using multiple functions offered by Spark’s SQL module
  • Use Spark’s UDFs to enrich data and process it in a user-defined way
  • Compare the performance of different UDF-implementation approaches
  • Optimize job execution with Spark UI
  • Understand the characteristics and importance of the serving layer

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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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