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Top Skills You Will Gain
- Big Data and the need for Spark
- Diving deep into Apache Spark
- Deploying Spark in different modes
- Deploying Spark in local mode
- Spark Streaming
- Demystifying Apache Spark
- Spark MLlib and Spark GraphX, Spark SQL
- Diving into Scala
Apache Spark with Scala Course Key Features 100% Money Back Guarantee
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Apache Spark with Scala Course Curriculam
Trainers Profile
Trainers are certified professionals with 12+ years of experience in their respective domains as well as they are currently working with Top MNCs. As all Trainers from Apache Spark with Scala are respective domain working professionals so they are having many live projects, trainers will use these projects during training sessions.
Pre-requisites
Those wishing to take the Apache Spark certification training course should have a fundamental knowledge of any programming language and a basic understanding of any database, SQL, and query language for databases. Working knowledge of Linux- or Unix-based systems is also beneficial.
Syllabus of Apache Spark with Scala Online Course Download syllabus
- 1. Overview of Hadoop
- 2. Architecture of HDFS & YARN
- 3. Overview of Spark version 2.2.0
- 4. Spark Architecture
- 5. Spark Components
- 6. Comparison of Spark & Hadoop
- 7. Installation of Spark v 2.2.0 on Linux 64 bit
- 1. Exploring the Spark shell
- 2. Creating Spark Context
- 3. Operations on Resilient Distributed Dataset – RDD
- 4. Transformations & Actions
- 5. Loading Data and Saving Data
- 1. Introduction to SQL Operations
- 2. SQL Context
- 3. Data Frame
- 4. Working with Hive
- 5. Loading Partitioned Tables
- 6. Processing CSV, Json ,Parquet files
- 1. Introduction to Scala
- 2. Feature of Scala
- 3. Scala vs Java Comparison
- 4. Data types
- 5. Data Structure
- 6. Arrays
- 7. Literals
- 8. Logical Operators
- 9. Mutable & Immutable variables
- 10. Type interface
- 1. Oops vs Functions
- 2. Anonymous
- 3. Recursive
- 4. Call-by-name
- 5. Currying
- 6. Conditional statement
- 1. List
- 2. Map
- 3. Sets
- 4. Options
- 5. Tuples
- 6. Mutable collection
- 7. Immutable collection
- 8. Iterating
- 9. Filtering and counting
- 10. Group By
- 11. Flat Map
- 12. Word count
- 13. File Access
- 1. Classes ,Objects & Properties
- 2. Inheritance
- 1. Maven build tool implementation
- 2. Build Libraries
- 3. Create Jar files
- 4. Spark-Submit
- 1. Overview of Spark Streaming
- 2. Architecture of Spark Streaming
- 3. File streaming
- 4. Twitter Streaming
- 1. Overview of Kafka Streaming
- 2. Architecture of Kafka Streaming
- 3. Kafka Installation
- 4. Topic
- 5. Producer
- 6. Consumer
- 7. File streaming
- 8. Twitter Streaming
- 1. Overview of Machine Learning Algorithm
- 2. Linear Regression
- 3. Logistic Regression
- 1. GraphX overview
- 2. Vertices
- 3. Edges
- 4. Triplets
- 5. Page Rank
- 6. Pregel
- 1. On-Off-heap memory tuning
- 2. Kryo Serialization
- 3. Broadcast Variable
- 4. Accumulator Variable
- 5. DAG Scheduler
- 6. Data Locality
- 7. Check Pointing
- 8. Speculative Execution
- 9. Garbage Collection
- 1. Master – Driver Node capacity
- 2. Slave – Worker Node capacity
- 3. Executor capacity
- 4. Executor core capacity
- 5. Project scenario and execution
- 6. Out-of-memory error handling
- 7. Master logs, Worker logs, Driver logs
- 8. Monitoring Web UI
- 9. Heap memory dump
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Apache Spark with Scala Training Objectives
- Apache Spark is a data processing framework that can quickly perform processing tasks on very large data sets, and can also distribute data processing tasks across multiple computers, either on its own or in tandem with other distributed computing tools.
- Spark is written in Scala
- When you want to get the most out of a framework, you need to master its original language. Scala is not only Spark's programming language, but it's also scalable on JVM.
- Apache Spark is written in Scala and because of its scalability on JVM - Scala programming is most prominently used programming language, by big data developers for working on Spark projects.
- The performance achieved using Scala is better than many other traditional data analysis tools like R or Python.
- Apache Spark is a Big Data framework which is in high demand. Spark provides streaming as well as batch capabilities, making it one of the biggest revolutionary changes in the environment of Big Data processing. Hence, it is an ideal framework for people and organizations that are looking for speed data analysis.
- Learning this framework will help you climb up the ladder of your career as nowadays more and more companies are eager to adopt Spark in their system.
- Learn by examples
- Scala and Functional Programming for Beginners
- Scala and Spark for Big Data and Machine Learning
- Scalable programming with Scala and Spark
- Scala Advanced
- Basics of Hadoop file system
- Understanding of SQL concepts
- Basics of any Distributed Database
- Performing ETL or SQL batch jobs with large data sets
- Processing streaming, real-time data from sensors, IoT, or financial systems especially in combination with static data.
- Using streaming data to trigger a response
- Performing complex session analysis
- Machine Learning tasks
- Learning Spark is not difficult if you have a basic understanding of Python or any programming language, as Spark provides APIs in Java, Python, and Scala.
- You can take up this Spark Training to learn Spark from industry experts.
- Scala ecosystem is constantly growing. The number of great libraries and frameworks is amazing. Scala also supports new technologies like Data Science or Blockchain with its amazing tools.
- If you have your one favourite Java library you can easily adapt it to Scala's environment because Scala is running on JVM.
- Spark is kind of like every other language or framework.
- You can probably get something running on day 1 (or week 1 if it's very unfamiliar), you can express yourself in a naive manner in a few weeks, and you can start writing quality code that you would expect from an experienced developer in a month or two.
Exam & Certification
- Participate and Complete One batch of Apache Spark with Scala Training Course
- Successful completion and evaluation of any one of the given projects
- Complete 85% of the Apache Spark with Scala Certification course
- Successful completion and evaluation of any one of the given projects
- Oracle Certified Associate (OCA)
- Oracle Certified Professional (OCP)
- Oracle Certified Expert (OCE)
- Oracle Certified Master (OCM)
- Learn About the Certification Paths.
- Write Code Daily This will help you develop Coding Reading and Writing ability.
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- Join LearnoVita Online Training Course That Gives you a High Chance to interact with your Subject Expert Instructors and fellow Aspirants Preparing for Certifications.
- Solve Sample Tests that would help you to Increase the Speed needed for attempting the exam and also helps for Agile Thinking.
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Software Testing, CapgeminiApache Spark with Scala Course FAQ's
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