Introduction to Scala and Spark
Data Scientists tend to favor one of three programming languages, Python, R, or Scala. Which to choose? Learn Scala if you are an aspiring or a seasoned Data Scientist (or Data Engineer) who is planning to work with Apache Spark to tackle Big Data with ease.
On completion of this course and the final project based on Scala and Spark candidate will be awarded with a certificate.
- Over 15 lectures and 24 hours of content!
- LIVE PROJECT based on real time data using Scala and Spark Included.
- Learn Spark and Scala basics to advance from a professional trainer.
- Information packed practical training starting from basics to advanced techniques.
- Best suitable for beginners and working professionals and a Data Science enthusiast.
- Course content designed by considering latest Spark & Scala version and the job market.
- Practical assignments at the end of every session.
- Practical learning experience with live project work and examples.
- Lectures 59
- Quizzes 0
- Duration 24 hours
- Skill level Beginner
- Language English
- Students 52
- Assessments Yes
Introduction to Scala
OOPs and Functional programming in Scala
- Class in Scala
- Getters and Setters, Custom Getters and Setters, Properties with only Getters,
- Auxiliary Constructor, Primary Constructor
- Singletons, Companion Objects
- Extending a Class, Overriding Methods, Traits as Interfaces, Layered Traits, Functional Programming, Higher Order Functions, Anonymous Functions, and more.
Introduction to Bigdata and Apache Spark
- Introduction to big data
- Challenges with big data
- Batch Vs. Real Time big data analytics
- Batch Analytics – Hadoop Ecosystem Overview
- Real-time Analytics Options
- Streaming Data – Spark, In-memory data – Spark
- What is Spark? Spark Ecosystem, modes of Spark
- Spark installation demo
- Overview of Spark on a cluster
- Spark Standalone cluster
- Spark Web UI.
Spark Common operations
Working with RDDs
Spark Streaming and Mlib
GraphX ,SparkSql and performance tuning in Spark
- Analyze Hive and Spark SQL architecture
- SQLContext in Spark SQL
- Working with DataFrames
- Implementing an example for Spark SQL
- Integrating hive and Spark SQL
- Support for JSON and Parquet File Formats
- Implement data visualization in Spark
- Loading of data
- Hive queries through Spark
- Testing tips in Scala
- Performance tuning tips in Spark
- Shared variables: Broadcast Variables, Shared Variables: Accumulators.