This course is designed to help IT professionals become Azure Data Engineers and prepare for the Microsoft DP-203 exam. It covers the power of Apache Spark and Azure Databricks, and how to use them to run large data engineering workloads in the cloud. You will learn how to work with large amounts of data from multiple sources, and how Azure Databricks supports day-to-day data-handling functions. This course is part of a Specialization intended for Data Engineers and developers, and includes a practice exam to help you prepare for the exam. With the help of experienced mentors, you will gain the knowledge and expertise to design and implement data solutions that use Microsoft Azure data services.
Tech Talk: Top Tuning Tips for Spark 30 and Delta Lake on Databricks
1.5
Youtube0 learners
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This Tech Talk provides an overview of the best tuning tips for Apache Spark 3.0 and Delta Lake on Databricks. Attendees will learn how to pick the best join strategy, use partition pruning and data skipping, optimize merges, and pick good instance types. They will also gain insight into the advantages of using Apache Spark 3.0 and AQE, as well as the benefits of Databricks Delta Lake and Stats.
Databricks Fundamentals & Apache Spark Core
4.4
Udemy20,440 learners
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This course provides an introduction to Databricks and Apache Spark 2.4 and 3.0.0. It covers the fundamentals of Apache Spark, how to write Spark applications using Scala and SQL, and how to use the DataFrame API and SQL to perform data manipulation tasks. It also explains how Apache Spark runs on a cluster with multiple nodes, and how to use UDFs with the DataFrame API or Spark SQL. Finally, it covers how to write DataFrames to external storage systems. With this course, you will gain the knowledge and skills to use Apache Spark and Databricks to process Big Data.
Databricks Essentials for Spark Developers (Azure and AWS)
4.1
Udemy9,528 learners
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Are you an experienced Spark Developer looking to understand the Databricks platform? This course will teach you the essentials of Databricks, including different editions such as Community, Databricks (AWS) and Azure Databricks, signing up for the community edition, uploading data to DBFS, developing using Databricks Notebook with Scala, Python and Spark SQL, and configuring jobs using Jar files. With this course, you can leverage the pay-as-you-go model of cloud computing to reduce the costs of infrastructure for Big Data Clusters. Don't miss out on this opportunity to learn the essentials of Databricks!
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