Databricks Certified Associate Developer for Apache Spark
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If you are looking for a certification-oriented, hands-on and comprehensive course to prepare for the Databricks Certified Associate Developer for Apache Spark certification, you have come to the right place.
This course is designed to prepare you to learn everything related to the Databricks Certified Associate Developer for Apache Spark certification.
In today’s data-driven world, Apache Spark has become the standard big-data cluster processing framework. And Databricks have become the reference tool for Big Data. For this reason, Databricks is one of the most valuable skills today. Having the Databricks Certified Associate Developer for Apache Spark certification will allow you to position yourself in the Big Data job market. Get certified and advance your Big Data career.
With theoretical training, downloadable study guides, hands-on exercises, and mock exams, this is the only course you’ll need to learn Apache Spark in Databricks and get certified. The exam consists of 60 multiple-choice questions. Candidates will have 120 minutes to complete the exam.
Topics covered in the course:
Certification preparation.
This course teaches you how to prepare for the Databricks exam. Including tips, proved preparation methodology, hands-on lectures in every section and tips and strategies using Mock Test.
Spark Architecture — Conceptual
· Cluster architecture: nodes, drivers, workers, executors, slots, etc.
· Spark execution hierarchy: applications, jobs, stages, tasks, etc.
· Shuffling
· Partitioning
· Lazy evaluation
· Transformations vs Actions
· Narrow vs Wide transformations
Spark Architecture — Applied
· Execution deployment modes
· Stability
· Storage levels
· Repartitioning
· Coalescing
· Broadcasting
· DataFrames
Spark DataFrame API
· Subsetting DataFrames (select, filter, etc.)
· Column manipulation (casting, creating columns, manipulating existing columns, complex column types)
· String manipulation (Splitting strings, regex)
· Performance-based operations (repartitioning, shuffle partitions, caching)
· Combining DataFrames (joins, broadcasting, unions, etc)
· Reading/writing DataFrames (schemas, overwriting)
· Working with dates (extraction, formatting, etc)
· Aggregations
· Miscellaneous (sorting, missing values, typed UDFs, value extraction, sampling)
Finally, we will conclude with a complete, comprehensive set of realistic, high-quality questions to practice for the Databricks Certified Developer for Apache Spark 3.0 exam in Python. These up-to-date practice exams provide you with the knowledge and confidence you need to pass the exam with excellence. Questions are based on the actual distribution of topics in the real exam. We include also real exam questions.The questions cover all themes being tested for in the exam, including specifics to Python and Apache Spark 3.0.
If you’re ready to sharpen your skills, increase your career opportunities, and become a Big Data expert, join today and get immediate and lifetime access to:
• Complete guide to Databricks Certified Associate Developer for Apache Spark guide (PDF e-book)
• Downloadable Spark project files
• Practical exercises
•Quizzes and mock exams
• Spark resources like Cheatsheets and Summaries
• 1 to 1 expert support
• Course question and answer forum
See you there!
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3Why Apache Spark Certification?Video lesson
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4Certification topicsVideo lesson
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5Certification General informationVideo lesson
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6Preparation processVideo lesson
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7Tips for passing exam in the first attemptVideo lesson
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8Registration and Certification processVideo lesson
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9Certification questions typesVideo lesson
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10How to obtain Databricks certification for freeVideo lesson
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15Fundamentals and advantages of DataFramesVideo lesson
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16Characteristics of DataFrames and data sourcesVideo lesson
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17Creating DataFrames in PySparkVideo lesson
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18Operations with PySpark DataFramesVideo lesson
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19Different types of joins in DataFramesVideo lesson
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20SQL queries in PySparkVideo lesson
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21Advanced features for loading and exporting data in PySparkVideo lesson
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22Practical Exercise: Spark DataFrames and Apache Spark SQLText lesson
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28Basics of advanced analytics with SparkVideo lesson
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29Data loading and data schema modificationVideo lesson
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30Inspect data in PySparkVideo lesson
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31Column transformation in PySparkVideo lesson
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32Advanced missing data imputation in PySparkVideo lesson
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33Data selection with PySpark and PySpark SQLVideo lesson
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34Data visualization and graph generation in PySparkVideo lesson
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35Persist data with PySparkVideo lesson
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36Practical Exercise: Advanced Analytics with Apache SparkText lesson
