Data Engineering with MS Azure Synapse Apache Spark Pools faq

instructor Instructor: Microsoft instructor-icon
duration Duration: duration-icon

This course provides an introduction to data engineering with MS Azure Synapse Apache Spark Pools, a powerful tool for in-memory cluster computing that can significantly improve the performance of big-data analytic applications.

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Course Feature Course Overview Pros & Cons Course Provider Discussion and Reviews
Go to class

Course Feature

costCost:

Free

providerProvider:

Coursera

certificateCertificate:

Paid Certification

languageLanguage:

English

start dateStart Date:

8th May, 2023

Course Overview

❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [February 21st, 2023]

What skills and knowledge will you acquire during this course?
By taking this course, learners will acquire the knowledge and skills to perform data engineering with Azure Synapse Apache Spark Pools. This includes understanding the differences between Apache Spark, Azure Databricks, HDInsight, and SQL Pools, as well as the use-cases of data-engineering with Apache Spark in Azure Synapse Analytics. Learners will also gain the ability to design and implement data solutions that use Microsoft Azure data services. Additionally, they will develop the skills to pursue a career in data engineering, data science, or data analysis. Suggested learning activities include taking courses related to data engineering, such as Data Science, Machine Learning, and Big Data, as well as working on projects related to data engineering and analytics. It is also recommended that learners stay up-to-date with the latest trends in the field by reading industry publications and attending conferences.

How does this course contribute to professional growth?
This course provides learners with the knowledge and skills to perform data engineering with Azure Synapse Apache Spark Pools, which can help them to boost the performance of big-data analytic applications by in-memory cluster computing. It also provides learners with the opportunity to differentiate between Apache Spark, Azure Databricks, HDInsight, and SQL Pools and understand the use-cases of data-engineering with Apache Spark in Azure Synapse Analytics. By taking this course, learners can pursue possible development paths such as becoming a Data Engineer, Data Scientist, or Data Analyst. They can also pursue a certification in Data Engineering on Microsoft Azure (beta) and specialize in designing and implementing data solutions that use Microsoft Azure data services. This course can contribute to professional growth by providing learners with the opportunity to practice their skills by working on projects related to data engineering and analytics, as well as staying up-to-date with the latest trends in the field by reading industry publications and attending conferences.

Is this course suitable for preparing further education?
This course is suitable for preparing further education in the field of data engineering. Learners will gain the knowledge and skills to perform data engineering with Azure Synapse Apache Spark Pools, which will enable them to boost the performance of big-data analytic applications by in-memory cluster computing. They will also be able to differentiate between Apache Spark, Azure Databricks, HDInsight, and SQL Pools and understand the use-cases of data-engineering with Apache Spark in Azure Synapse Analytics. Possible development paths include becoming a Data Engineer, Data Scientist, or Data Analyst. Additionally, learners can pursue a certification in Data Engineering on Microsoft Azure (beta) and specialize in designing and implementing data solutions that use Microsoft Azure data services. To further prepare for further education, learners should take courses related to data engineering, such as Data Science, Machine Learning, and Big Data. They should also practice their skills by working on projects related to data engineering and analytics, and stay up-to-date with the latest trends in the field by reading industry publications and attending conferences.

Pros & Cons

Pros Cons
  • pros

    Good overview of architecture and options.

  • pros

    Beginner level course.

  • pros

    Learn by doing.

  • cons

    Lack of sandbox.

  • cons

    Wrong answers in tests.

  • cons

    Lack of VM environment.

Course Provider

Provider Coursera's Stats at AZClass

Discussion and Reviews

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