Apache Spark Scala Course Description
Pincorps Apache Spark Scala training module teaches you to create applications in Spark with the implementation of Scala programming. It provides a clear comparison between Spark and Hadoop and covers techniques to increasing your application performance and enabling high-speed processing.
Learners will master Scala programming and will get trained on different APIs which Spark offers such as Spark Streaming, Spark SQL, Spark RDD, Spark MLlib and Spark GraphX.
Apache Spark, a fast, in-memory distributed collections framework written in Scala. Employers including Amazon, EBay, NASA JPL, and Yahoo all use Spark to quickly extract meaning from massive data sets across a fault-tolerant Hadoop cluster.
Apache Spark & Scala Learning Outcomes;
- Understand Scala and its implementation.
- Apply Lazy values, Control Structures, Loops, Collection, etc.
- Learn the concepts of Traits and OOPS in scala.
- Understand Functional programming in scala.
- Get an insight into the BigData challenges.
- How spark acts as a solution to these challenges.
- Install spark and implement spark operations on spark shell.
- Understand what are RDDs in spark.
- Implement spark application on YARN (Hadoop).
- Analyze Hive and Spark SQL Architecture.
Apache Spark & Scala Training – Suggested Audience
This Apache Spark & Scala training is aimed at professionals with little bit knowledge on functional programming language and object-oriented programming. Suggested attendees based on our past programs are:
- Big Data enthusiasts
- Software Architects
- Software Engineers
- Software Developers
- Data Scientists
- Data Engineers
- ETL Developers
Apache Spark Scala Training Prerequisites
- Basic familiarity with Linux or Unix
- A basic understanding of functional programming and object-oriented programming.
- Intermediate-level of knowledge on Hadoop.
- Knowledge of Scala will definitely be a plus but is not mandatory.
Apache Spark & Scala In-house/Corporate Training
If you have a group of 5-6 participants, apply for in-house training. For commercials please send us an email with group size to firstname.lastname@example.org
|Module-1: Introduction to Spark and Analysis|
|Why second generation frameworks? Details||00:00:00|
|Introduction to Spark Details||00:00:00|
|Scala shell Details||00:00:00|
|Spark Architecture Details||00:00:00|
|Spark on Cluster Details||00:00:00|
|Spark Core Details||00:00:00|
|Spark Streaming Details||00:00:00|
|Cluster Managers Details||00:00:00|
|Spark Users Details||00:00:00|
|What is use of Spark Details||00:00:00|
|Spark Versions Details||00:00:00|
|Spark Storage Layers Details||00:00:00|
|Download Spark Details||00:00:00|
|1. Spark API on a Cluster Details||00:00:00|
|2. Cluster Manager Details||00:00:00|
|Module-2: DATALOADING (HDFS, Amazon s3)|
|7 Different file formats: Details||00:00:00|
|What is RDD Details||00:00:00|
|Why RDD? Details||00:00:00|
|RDD operations Details||00:00:00|
|Lazy Evaluation Details||00:00:00|
|Basic RDD’s Details||00:00:00|
|Converting between RDD types Details||00:00:00|
|Spark Api supports Python, Java, Scala Details||00:00:00|
|Working with Key, value pairs Details||00:00:00|
|Create key, value pair RDD’s Details||00:00:00|
|Transformations on pair RDD’s Details||00:00:00|
|Actions on pair RDD’s Details||00:00:00|
|Advanced Spark operation Details||00:00:00|
|Module-4: SPARK SQL|
|Spark sql in applications Details||00:00:00|
|Spark sql initialization Details||00:00:00|
|Spark sql basic query Details||00:00:00|
|Schema RDD’s Details||00:00:00|
|Load data from hive Details||00:00:00|
|Load data from json Details||00:00:00|
|Load data from RDD’s Details||00:00:00|
|Long-lived tables and queries Details||00:00:00|
|Query hands-on Details||00:00:00|
|Spark sql UDF’s Details||00:00:00|
|Module-5: SPARK STREAMING|
|Streaming Architecture Details||00:00:00|
|Two types of Transformations Details||00:00:00|
|Streaming UI Details||00:00:00|
|Sources: Input Details||00:00:00|
|Core Sources Details||00:00:00|
|Additional Sources Details||00:00:00|
|Multiple Sources Details||00:00:00|
|Cluster Sizing Details||00:00:00|
|Fault Tolerance Details||00:00:00|
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