Distributed Computing with Spark SQL

Go to Course: https://www.coursera.org/learn/spark-sql

Introduction

### Course Review: Distributed Computing with Spark SQL on Coursera #### Overview In today’s data-driven world, the ability to manage and analyze large datasets is an essential skill. Coursera's **Distributed Computing with Spark SQL** course is an excellent opportunity for those with a foundational understanding of SQL to advance their data manipulation skills and dive into the realm of distributed computing using Apache Spark. This course not only equips students with the knowledge of working with big data but also sets them up for success in real-world applications by leveraging the power of Spark. #### Course Content The syllabus is well-structured, with the following key modules: 1. **Introduction to Spark**: This module serves as the foundation for understanding distributed computing. You will delve into the core concepts and learn how to effectively use Spark’s DataFrame, the primary data structure. By interacting with the collaborative Databricks workspace, you’ll gain hands-on experience executing SQL code across a cluster of machines, providing insight into distributed data handling. 2. **Spark Core Concepts**: Here, you'll explore critical elements of Spark, such as improving query performance through data caching and modifying configurations. The practical aspect of utilizing Spark UI to analyze performance and identify bottlenecks greatly enhances your ability to optimize queries with Adaptive Query Execution. This section is particularly valuable for those aiming to work in environments where efficiency is paramount. 3. **Engineering Data Pipelines**: This module focuses on the architecture of data applications. You will learn to access data in various formats and understand the implications of these choices. The exploration of semi-structured JSON data is particularly relevant in modern data environments, allowing you to construct a comprehensive end-to-end data pipeline that encompasses reading, transforming, and saving data—all crucial skills for any aspiring data professional. 4. **Data Lakes, Warehouses, and Lakehouses**: This section illuminates the differences and characteristics of data lakes, warehouses, and the innovative lakehouse architecture. By incorporating technologies like Delta Lake, students will learn how to build a production-grade lakehouse that balances scalability and performance. This knowledge bridges the gap between traditional data management and modern approaches, making it a vital part of the curriculum. #### Learning Experience The learning experience in this course is enhanced by engaging video lectures, practical assignments, and community discussions, allowing students to connect with fellow learners. Coursera’s platform is user-friendly, providing a seamless experience from start to finish. The practical assignments particularly reinforce theoretical knowledge and give students the confidence to implement what they’ve learned. #### Recommendation I highly recommend the **Distributed Computing with Spark SQL** course for anyone seeking to elevate their data skills. Whether you are a data analyst, data engineer, or simply someone interested in big data technologies, this course lays a solid foundation in distributed computing while specifically focusing on Spark SQL. With the increasing reliance on data analytics in various industries, the skills acquired in this course will undoubtedly enhance your career prospects. By completing this course, you will not only become proficient in SQL on Spark but also gain an understanding of modern data architecture, making you a valuable asset in any data-driven organization. Get ready to embark on a transformative data journey!

Syllabus

Introduction to Spark

In this module, you will be able to discuss the core concepts of distributed computing and be able to recognize when and where to apply them. You'll be able to identify the basic data structure of Apache Spark™, known as a DataFrame. Additionally, you will use the collaborative Databricks workspace and write SQL code that executes against a cluster of machines.

In this module, you will be able to explain the core concepts of Spark. You will learn common ways to increase query performance by caching data and modifying Spark configurations. You will also use the Spark UI to analyze performance and identify bottlenecks, as well as optimize queries with Adaptive Query Execution.

In this module, you will be able to identify and discuss the general demands of data applications. You'll be able to access data in a variety of formats and compare and contrast the tradeoffs between these formats. You will explore and examine semi-structured JSON data (common in big data environments) as well as schemas and parallel data writes. You will be able to create an end-to-end pipeline that reads data, transforms it, and saves the result.

In this module, you will identify the key characteristics of data lakes, data warehouses, and lakehouses. Lakehouses combine the scalability and low-cost storage of data lakes with the speed and ACID transactional guarantees of data warehouses. You will build a production grade lakehouse by combining Spark with the open-source project, Delta Lake. Whoever said time travel isn't possible hasn't been to a lakehouse!