This course is all about big data. It’s for students with SQL experience that want to take the next step on their data journey by learning distributed computing using Apache Spark. Students will gain a thorough understanding of this open-source standard for working with large datasets. Students will gain an understanding of the fundamentals of data analysis using SQL on Spark, setting the foundation for how to combine data with advanced analytics at scale and in production environments. The four modules build on one another and by the end of the course you will understand: the Spark architecture, queries within Spark, common ways to optimize Spark SQL, and how to build reliable data pipelines.
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- 5 stars65.62%
- 4 stars23.63%
- 3 stars6.83%
- 2 stars1.75%
- 1 star2.14%
来自DISTRIBUTED COMPUTING WITH SPARK SQL的热门评论
This has been an amazing course. What is worth mentioning is how the content was delivered. Nice hands on. Highly recommended for anyone who is new to Spark
Great introduction to Spark SQL and ML Flow. I love that they give extra resources if you want to learn more. It was a fun learning journey.
Amazing course that really cuts through the fundamentals of using distributed computing power to analyze and manipulate data. Well organised structure on fundamentals
A good introduction to Spark SQL.
A pity that the course assignment was not very challenging but more focused on understanding of Spark itself.
关于 Learn SQL Basics for Data Science 专项课程
This Specialization is intended for a learner with no previous coding experience seeking to develop SQL query fluency. Through four progressively more difficult SQL projects with data science applications, you will cover topics such as SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, Delta Lake and more. These topics will prepare you to apply SQL creatively to analyze and explore data; demonstrate efficiency in writing queries; create data analysis datasets; conduct feature engineering, use SQL with other data analysis and machine learning toolsets; and use SQL with unstructured data sets.