Data Engineering with Spark Databricks Delta Lake Lakehouse
FutureX Skills
2:07:54
Description
Apache Spark Databricks Lakehouse Delta Lake Delta Tables Delta Caching Scala Python Data Engineering for beginners
What You'll Learn?
- Acquiring the necessary skills to qualify for an entry-level Data Engineering position
- Developing a practical comprehension of Data Lakehouse concepts through hands-on experience
- Learning to operate a Delta table by accessing its version history, recovering data, and utilizing time travel functionality
- Optimizing a delta table with various techniques like caching, partitioning, and z-ordering for faster analytics
- Obtaining practical knowledge in constructing a data pipeline through the usage of Apache Spark on the Databricks platform
Who is this for?
More details
DescriptionData Engineering is a vital component of modern data-driven businesses. The ability to process, manage, and analyze large-scale data sets is a core requirement for organizations that want to stay competitive. In this course, you will learn how to build a data pipeline using Apache Spark on Databricks' Lakehouse architecture. This will give you practical experience in working with Spark and Lakehouse concepts, as well as the skills needed to excel as a Data Engineer in a real-world environment.
Throughout the course, you will learn how to conduct analytics using Python and Scala with Spark, apply Spark SQL and Databricks SQL for analytics, develop a data pipeline with Apache Spark, quickly become proficient in Databricks' community edition, manage a Delta table by accessing version history, restore data, and utilize time travel features, optimize query performance using Delta Cache, work with Delta Tables and Databricks File System, and gain insights into real-world scenarios from our experienced instructor.
At the beginning of the course, you will start by becoming familiar with Databricks' community edition and creating a basic pipeline using Spark. This will assist you in setting up your environment and getting comfortable with the platform before progressing to more complex topics.
Once you are familiar with the basics, you will learn how to conduct analytics with Spark using Python and Scala. This will include topics such as Spark transformations, actions, joins Spark SQL and DataFrame APIs.
In the final section of the course, you will acquire the knowledge and skills to operate a Delta table . This will involve accessing its version history, restoring data, and utilizing time travel functionality using Spark and Databricks SQL. Additionally, you will learn how to use delta cache to optimize query performance.
This course is designed for Data Engineering beginners with no prior knowledge of Python and Scala required. However, some familiarity with databases and SQL is necessary to succeed in this course. Upon completion, you will have the skills and knowledge required to succeed in a real-world Data Engineer role.
Throughout the course, you will work with hands-on examples and real-world scenarios to apply the concepts you learn. By the end of the course, you will have the practical experience and skills required to understand Spark and Lakehouse concepts, and to build a scalable and reliable data pipeline using Apache Spark on Databricks' Lakehouse architecture.
Who this course is for:
- Data Engineering beginners
Data Engineering is a vital component of modern data-driven businesses. The ability to process, manage, and analyze large-scale data sets is a core requirement for organizations that want to stay competitive. In this course, you will learn how to build a data pipeline using Apache Spark on Databricks' Lakehouse architecture. This will give you practical experience in working with Spark and Lakehouse concepts, as well as the skills needed to excel as a Data Engineer in a real-world environment.
Throughout the course, you will learn how to conduct analytics using Python and Scala with Spark, apply Spark SQL and Databricks SQL for analytics, develop a data pipeline with Apache Spark, quickly become proficient in Databricks' community edition, manage a Delta table by accessing version history, restore data, and utilize time travel features, optimize query performance using Delta Cache, work with Delta Tables and Databricks File System, and gain insights into real-world scenarios from our experienced instructor.
At the beginning of the course, you will start by becoming familiar with Databricks' community edition and creating a basic pipeline using Spark. This will assist you in setting up your environment and getting comfortable with the platform before progressing to more complex topics.
Once you are familiar with the basics, you will learn how to conduct analytics with Spark using Python and Scala. This will include topics such as Spark transformations, actions, joins Spark SQL and DataFrame APIs.
In the final section of the course, you will acquire the knowledge and skills to operate a Delta table . This will involve accessing its version history, restoring data, and utilizing time travel functionality using Spark and Databricks SQL. Additionally, you will learn how to use delta cache to optimize query performance.
This course is designed for Data Engineering beginners with no prior knowledge of Python and Scala required. However, some familiarity with databases and SQL is necessary to succeed in this course. Upon completion, you will have the skills and knowledge required to succeed in a real-world Data Engineer role.
Throughout the course, you will work with hands-on examples and real-world scenarios to apply the concepts you learn. By the end of the course, you will have the practical experience and skills required to understand Spark and Lakehouse concepts, and to build a scalable and reliable data pipeline using Apache Spark on Databricks' Lakehouse architecture.
Who this course is for:
- Data Engineering beginners
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Udemy
View courses Udemy- language english
- Training sessions 23
- duration 2:07:54
- English subtitles has
- Release Date 2023/03/29
