Review key points of working with SQL from Scala.
- [Instructor] So to summarize our work with SQL and JDBC,…some things I'd like to point out is that first of all,…it requires the installation of a JDBC driver,…so we have to have Java installed,…but we also have to install a specific JDBC driver…for the particular kind of database…that you're working with.…So if you're using MySQL, that would be one driver.…If using Postgres, that would be another, and so on.…Another thing to keep in mind is that when we work…with queries they return a structure called a cursor,…so you have to move through those using the next operator,…and SQL statements can be constructed as string and executed…but prepared statements are more efficient…if we're repeatedly using those statements.…
Dan also focuses on using Scala with Spark, a distributed processing platform. He first describes how to work with Resilient Distributed Datasets (RDDs)—a fundamental Spark data structure—and then explains how to use Scala with Spark DataFrames, a new class of data structure specially designed for analytic processing. He wraps up the course by providing a summary of advantages of using Scala for data science.
- The advantages of Scala for data science
- Scala data types
- Scala arrays, vectors, and ranges
- Parallel processing in Scala
- Mapping functions over parallel collections
- When and when not to use parallel collections
- Using SQL in Scala
- Scala and Spark RDDs
- Scala and Spark DataFrames
- Creating DataFrames
Skill Level Intermediate
Java for Data Scientists Essential Trainingwith Charles Kelly2h 43m Intermediate
1. Introduction to Scala
2. Parallel Processing in Scala
3. Using SQL in Scala
4. Scala and Spark RDDs
5. Scala and Spark DataFrames
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