Master Spark ecosystem and Scala. Enroll now to learn from a Spark and Scala expert.
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Corporate Served- Croma Campus' Apache Spark training course has been thoroughly reviewed by industry professionals to ensure that it meets industry standards. This article will show you how to use Apache Spark and the Spark Ecosystem, which includes Spark RDD, Spark SQL, and Spark MLlib.
- Things you will learn:
- The Apache Spark certification training course will teach you all you need to know. It provides a basic Spark vs. Hadoop comparison.
You'll gain a thorough understanding of key Apache Spark principles.
Interact with and learn from your instructor as well as your classmates.
How to build Spark apps using Scala programming.
how to increase application performance and enable high-speed processing using Spark RDDs
You'll learn how to customise Spark using Scala in this course.
Apache Spark and Scala Training
- Whether you're a beginner or a seasoned veteran, our Apache Spark training course will help you grasp all of the principles and put them into practise at work. Let's talk about the course goals:
Be familiar with Big Data, its components and frameworks, as well as Hadoop Cluster design and modes.
Know how to programme in Scala, how to implement it, and how to use the core Apache Spark constructs.
Gain an understanding of Apache Spark concepts and learn how to build Spark apps.
Understand the Apache Spark framework's concepts and deployment procedures.
Learn how to use the Spark Internals RDD, as well as the Spark API and Scala functions, to create and modify RDDs.
Be an expert in SparkSQL, Spark Context, Spark Streaming, MLlib, and GraphX, as well as RDD and other Combiners.
- The company where you work will also determine your income. Pay for businesses such as Cognizant, Accenture, and Infosys is normally good, but you may make a lot of money working for Amazon, Microsoft, or Yahoo.
A Spark developer at the beginning level can expect to earn between Rs 6,00,000 and Rs 10,00,000 per year.
A skilled developer might make anywhere between Rs 25,00,000 and Rs 40,00,000.
A Data Engineer with Apache Spark expertise may expect to earn more than Rs 10,00,000 per year on average.
You will receive a greater wage package after completing your Apache Spark certification course.
A Data Scientist with Apache Spark skills in Hyderabad, on the other hand, may earn more than Rs 8,00,000 per year on average.
- If you want to break into the big data industry and succeed, Apache Spark is the way to go, as it offers a wide range of options for big data analysis. It is the most popular Big Data technology because its different approaches are effective against a variety of data difficulties.
- That’s the reason, there’s a huge demand for Apache Spark training courses among students.
Because Spark can operate on Hadoop MapReduce, YARN, and HDFS, it outperforms Hadoop.
Because of its great Hadoop compatibility, companies are looking for a big number of Spark Developers.
Many companies are turning to Spark as a complementary big data platform since it processes data much quicker than Hadoop.
As technology progresses and new businesses turn to big data management to fulfil their needs, a plethora of new options emerge.
- Today, there are opportunities all around the world, including in India, resulting in a growth in professional opportunities for skilled persons.
Companies all across the world are using Spark as their primary big data processing platform.
You'll have the opportunity to work in a range of fields, including retail, software, media and entertainment, consulting, healthcare, and more.
To gain a competitive advantage, every industry is employing big data analytics and machine learning techniques.
- You will be required to undertake a variety of work duties and responsibilities after completing your Apache Spark training course.
Ability to define problems, collect data, establish facts, and draw valid conclusions using software code.
Using Spark, produce ready-to-use data by cleaning, processing, and analysing raw data from different mediation sources.
To guarantee that joins are executed quickly, refactoring code is utilised.
Assist with the Spark platform's technical architecture.
Use partitioning strategies to meet certain use situations.
Hold deep-dive working sessions to fix Spark platform issues fast.
- Top Hiring Industries in Apache Spark:
Google, Cognizant Technology Solutions, TCS, IBM, Accenture, and other well-known employers.
In addition, we have a team of experts who can assist you with resume writing and interview preparation.
You will have the opportunity to participate in interviews and be hired in a variety of industries.
There are several work opportunities available all over the world.
- Training Certificate:
With our certification, you may be able to work from anywhere.
In today's tech-driven environment, you'll be more valuable.
Our certification could help you outperform the competition.
Make a name for yourself as a sought-after expert.
Obtain a sizable remuneration package.
Why Should You Learn Apache Spark Training?
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Plenary for Apache Spark and Scala Training
| Track | Week Days | Weekends | Fast Track |
|---|---|---|---|
| Course Duration | 40-45 Days | 7 Weekends | 8 Days |
| Hours | 1 Hrs. Per Day | 2 Hrs. Per Day | 6+ Hrs. Per Day |
| Training Mode | Classroom/Online | Classroom/Online | Classroom/Online |
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CURRICULUM & PROJECTS
Apache Spark and Scala Training
- In this program you will learn:
Apache HBASE Ecosystem
Troubleshooting in Hbase
Troubleshooting in Hbase
Troubleshooting in Hbase
Administration in HBASE
No SQL Databases Hbase
Hadoop Overview
Advanced Concepts in PIG
Functions in PIG
Data Models, Operators, and Streaming in PIG
PIG Architecture
Introduction
Kafka Integration with Flume, Talend and Cassandra
Career Guidance and Roadmap
Kafka Integration with Hadoop, Storm, and Spark
Kafka Stream Processing
Kafka Monitoring & Kafka Connect
Cluster Architecture and Administering Kafka
Kafka Internals
Kafka Consumers
Kafka Producer
Overview
Graph X
Spark Streaming
Machine Learning with Spark ML
Spark SQL, Data Frames, and Data Sets
Running Spark on a cluster
Using Resilient Distributed Datasets
Scala
Introduction
- This Spark certification training helps you master the essential skills of the Apache Spark open-source framework and Scala programming language, including Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark.
- Introduction
Apache Spark Overview
What’s new in Apache Spark 3
Installing IntelliJ and Scala
Introduction to IntelliJ and Scala
Hadoop MapReduce Overview
Big Data Overview
Apache Hadoop Overview
Hadoop Distibution File System
- Scala
Flow control in Scala
Functions in Scala
Data Structures in Scala
- Using Resilient Distributed Datasets
Check Your Results and Implementation Against Min
Filtering RDD's and the Minimun Temperature by Location Example
Key/value RDD's and the Average Friends by Age example
The Resilient Distributed Dataset
Ratings Histogram Example
- Spark SQL, Data Frames, and Data Sets
Item-Based Collaborative Filtering in Spark, cache (), and persist ()
What are Data Sets
What are Data Frames
Introduction to Spark SQL
- Running Spark on a cluster
Partitioning Concepts
Troubleshooting and managing dependencies
Introducing Amazing Elastic MapReduce
Cluster management in Hadoop
What is a Cluster
- Machine Learning with Spark ML
Linear Regression with MLLib
Using MLLib
Introduction MLLib
- Spark Streaming
The DStream API for Spark Streaming
Spark Streaming
- Graph X
Using Pregel API with Spark API
Breadth-First-Search with Pregel
About Pregel
What is Graph X
- Overview
Configuring Single Node Single Broker Cluster
Type of Kafka Clusters
Kafka Cluster
Kafka Installation
Zookeeper
Where is Kafka Used
Kafka Components
Kafka Architecture
Kafka Concepts
Kafka Features
What is Kafka
Need for Kafka
Big Data Analytics
Introduction to Big Data
- Kafka Producer
Partitions
Serializing Using Apache Avro
Serializers
Configuring Producers
Sending a Message Synchronously & Asynchronously
Producing Keyed and Non-Keyed Messages
Sending a Message to Kafka
Configuring Single Node Multi Broker Cluster
- Kafka Consumers
De-serializers
Rebalance Listeners
Consuming Records With Specific Offsets
Commits and Offsets
Configuring Consumers
The Poll Loop
Subscribing to Topics
Creating a Kafka Consumer
Consumer Groups and Partition Rebalance
Standalone Consumer
Consumers and Consumer Groups
- Kafka Internals
Performance Tuning in Kafka
Validating System Reliability
Using Consumers in a Reliable System
Reliability
Broker Configuration
Using Producers in a Reliable System
Physical Storage
Request Processing
Replication
The Controller
Cluster Membership
- Cluster Architecture and Administering Kafka
Unsafe Operations
Consuming and Producing
Partition Management
Topic Operations
Consumer Groups
Dynamic Configuration Changes
Other Cross-Cluster Mirroring Solutions
Apache Kafka’s Mirror Maker
Multi-Cluster Architectures
Use Cases - Cross-Cluster Mirroring
- Kafka Monitoring & Kafka Connect
When to Use Kafka Connect
Kafka Connect Properties
Kafka Connect
Kafka Broker Metrics
Client Monitoring
Lag Monitoring
End-to-End Monitoring
Metric Basics
Considerations When Building Data Pipelines
- Kafka Stream Processing
Kafka Streams: Architecture Overview
Kafka Streams by Example
Stream Processing
Stream-Processing Concepts
Stream-Processing Design Patterns
- Kafka Integration with Hadoop, Storm, and Spark
Kafka Integration with Spark
Spark Configuration
Integration of Kafka with Storm
Apache Spark Basics
Configuration of Storm
Apache Storm Basics
Kafka Integration with Hadoop
Hadoop Configuration
Apache Hadoop Basics
- Kafka Integration with Flume, Talend and Cassandra
Talend Basics
Integration of Kafka with Talend
Integration of Kafka with Cassandra
Cassandra Basics Such as Key Space and Table Creation
Integration of Kafka with Flume
Flume Basics
- Career Guidance and Roadmap
When to avoid PIG
When PIG suits the most
Why you need PIG
Significance od PIG
PIG over MapReduce
History of Apache PIG
Exploring use cases for PIG
Introduction to PIG
Prerequisites for Apache PIG
Hadoop MapReduce Overview
Hadoop Distribution File System
Apache Hadoop Overview
- PIG Architecture
Utility Commands
Running Pig Scripts
PIG Latin Statements
GRUNT Shell
PIG Architecture
Running PIG in Different Modes
PIG Latin Language
- Data Models, Operators, and Streaming in PIG
Diagnostic Operators
Combining and Splitting Operators
Sorting Operator
Grouping and Joining Operators-
Filtering Operators- Pig Streaming with Python
Filtering Operators
Relation Operators
Loading and Stroing Operators
Type Construction Operators
Cast Operators
Comparison Operators
Arithmetic Operators
PIG Data Model - Complex Data Type
PIG Data Model - Scarlar Data Type
- Functions in PIG
Math Function
String Functions
Tuple and Bag Functions
Eval Functions
Load and Store Funtions
- Advanced Concepts in PIG
Parameter Substitutions
Import Macros
Pig Macros
Embedded PIG in JAVA
Pig Unit Testing
Intermediate Compression
File compression in PIG
- Hadoop Overview
What is a Hadoop Framework
Type of Hadoop Frameworks
Hadoop Ecosystem
HDFS
Big Data Overview
Hadoop Overview
Course overview
- No SQL Databases Hbase
HBase Shell
Connecting to HBase
Data Model
HBase Overview
HBase Architecture
NoSQL Introduction
NoSQL Databases Hbase
- Administration in HBASE
Interacting with HBase
Basic Static Configuration
Rolling Restarts and Upgrades
Installation with Cloudera Manager
Hbase Deployment at scale
Software Recommendations
Hardware Recommendations
Introduction
Learn and Understand Hbase Fault Tolerance
- Troubleshooting in Hbase
Learn How To Use the Metrics
Learn How To Use the Logs
Learn How To Use the Hbase UI
Troubleshooting Distributed Clusters
Introduction
- Troubleshooting in Hbase
Tuning for Modern Hardware
Tuning with Failures
Memory Tuning
Table Storage Tuning
Generating With YCSB
Region Tuning
Generating Load & Load Test Tool
Introduction
- Apache HBase Operations Continuity
Cluster Replication
Snapshots
Import Export and copy Paste
Security Demo
Security
Corruption: Other Tools
Corruption: hbck
Introduction
- Apache HBASE Ecosystem
Hue
HBase With Apache Phoenix’
Introduction
+ More Lessons
Mock Interviews
Projects
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FAQ's
Pre-course reading will be provided so that you are familiar with the content before the class begins.
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You will be able to register for our next training session, as we only offer two to three each year.
Yes, there are a variety of payment options.
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