Course Outline
Introduction
Overview of Data Access Approaches (Hive, databases, etc.)
Overview of Spark Features and Architecture
Installing and Configuring Spark
Understanding Dataframes in Spark
Defining Tables and Importing Datasets
Querying Data Frames using SQL
Carrying out Aggregations, JOINs and Nested Queries
Uploading and Accessing Data
Querying Different Types of Data
- JSON, Parquet, etc.
Querying Data Lakes with SQL
Troubleshooting
Summary and Conclusion
Requirements
- Experience with SQL queries
- Programming experience in any language
Audience
- Data analysts
- Data scientists
- Data engineers
Testimonials (3)
A lot of practical examples, different ways to approach the same problem, and sometimes not so obvious tricks how to improve the current solution
Rafał - Nordea
Course - Apache Spark MLlib
Commitment and willingness to explain secondary topics.
Marek - Krajowy Rejestr Długów Biuro Informacji Gospodarczej S.A.
Course - Apache Spark Fundamentals
Machine Translated
The trainer's practical experience, not coloring the discussed solution, but also not introducing a negative connotation. I feel that the trainer is preparing me for real and practical use of the tool - these valuable details are usually not found in books.
Krzysztof Miodek - Krajowy Rejestr Długów Biuro Informacji Gospodarczej S.A.
Course - Apache Spark Fundamentals
Machine Translated