Spark with Scala-training-in-bangalore-by-zekelabs

Spark with Scala Training

Spark with Scala Course: Manipulating big data distributed over a cluster using functional concepts is rampant in industry, and is arguably one of the first widespread industrial uses of functional ideas. This is evidenced by the popularity of MapReduce and Hadoop, and most recently Apache Spark, a fast, in-memory distributed collections framework written in Scala. In this course, we'll see how the data parallel paradigm can be extended to the distributed case, using Spark throughout. We'll cover Spark's programming model in detail, being careful to understand how and when it differs from familiar programming models, like shared-memory parallel collections or sequential Scala collections. Through hands-on examples in Spark and Scala, we'll learn when important issues related to distribution like latency and network communication should be considered and how they can be addressed effectively for improved performance.
Spark with Scala-training-in-bangalore-by-zekelabs
Spark with Scala-training-in-bangalore-by-zekelabs
Industry Level Projects
Spark with Scala-training-in-bangalore-by-zekelabs

Spark with Scala Course Curriculum

What Is Apache Spark?
Spark Core
Spark Streaming
Who Uses Spark, and for What?
Data Processing Applications
Spark Versions and Releases
Downloading Spark
Introduction to Core Spark Concepts
Initializing a SparkContext
RDD Basics
RDD Operations
Passing Functions to Spark
Basic RDDs
Persistence (Caching)
Transformations on Pair RDDs
Grouping Data
Sorting Data
Data Partitioning (Advanced)
Operations That Benefit from Partitioning
Example: PageRank
Text Files
Comma-Separated Values and Tab-Separated Values
Object Files
File Compression
Local/“Regular” FS
Apache Hive
Accumulators and Fault Tolerance
Broadcast Variables
Working on a Per-Partition Basis
Numeric RDD Operations
The Driver
Cluster Manager
Packaging Your Code and Dependencies
A Scala Spark Application Built with sbt
Scheduling Within and Between Spark Applications
Standalone Cluster Manager
Apache Mesos
Which Cluster Manager to Use?
Configuring Spark with SparkConf
Finding Information
Driver and Executor Logs
Level of Parallelism
Memory Management
Linking with Spark SQL
Initializing Spark SQL
Loading and Saving Data
From RDDs
Working with Beeline
User-Defined Functions
Hive UDFs
Performance Tuning Options
A Simple Example
Stateful Transformations
Input Sources
Additional Sources
Worker Fault Tolerance
Processing Guarantees
Performance Considerations
Level of Parallelism
Machine Learning Basics
Data Types
Dimensionality Reduction
Tips and Performance Considerations
Configuring Algorithms
Caching RDDs to Reuse
Level of Parallelism

Frequently Asked Questions

We have options for classroom-based as well as instructor led live online training. The online training is live and the instructors screen will be visible and voice will be audible. Your screen will also be visible and you can ask queries during the live session.

The training on "Spark with Scala" course is a hands-on training. All the code and exercises will be done in the live sessions. Our batch sizes are generally small so that personalized attention can be given to each and every learner.

We will provide course-specific study material as the course progresses. You will have lifetime access to all the code and basic settings needed for this "Spark with Scala" through our GitHub account and the study material that we share with you. You can use that for quick reference

Feel free to drop a mail to us at [email protected] and we will get back to you at the earliest for your queries on "Spark with Scala" course.

We have tie-ups with a number of hiring partners and and placement assistance companies to whom we connect our learners. Each "Spark with Scala" course ends with career consulting and guidance on interview preparation.

Minimum 2-3 projects of industry standards on "Spark with Scala" will be provided.

Yes, we provide course completion certificate to all students. Each "Spark with Scala" training ends with training and project completion certificate.

You can pay by card (debit/credit), cash, cheque and net-banking. You can also pay in easy installments. You can reach out to us for more information.

We take pride in providing post-training career consulting for "Spark with Scala".

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