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
- 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.
- In this program you will learn:
Introduction
Scala
Using Resilient Distributed Datasets
Spark SQL, Data Frames, and Data Sets
Running Spark on a cluster
Machine Learning with Spark ML
Spark Streaming
Graph X
Overview
Kafka Producer
Kafka Consumers
Kafka Internals
Cluster Architecture and Administering Kafka
Kafka Monitoring & Kafka Connect
Kafka Stream Processing
Kafka Integration with Hadoop, Storm, and Spark
Kafka Integration with Flume, Talend and Cassandra
Career Guidance and Roadmap
Introduction
PIG Architecture
Data Models, Operators, and Streaming in PIG
Functions in PIG
Advanced Concepts in PIG
Hadoop Overview
No SQL Databases Hbase
Administration in HBASE
Troubleshooting in Hbase
Troubleshooting in Hbase
Troubleshooting in Hbase
Apache HBASE Ecosystem
- Introduction
Big Data Overview
Apache Hadoop Overview
Hadoop Distibution File System
Hadoop MapReduce Overview
Introduction to IntelliJ and Scala
Installing IntelliJ and Scala
Apache Spark Overview
What’s new in Apache Spark 3
- Scala
Flow control in Scala
Functions in Scala
Data Structures in Scala
- Using Resilient Distributed Datasets
The Resilient Distributed Dataset
Ratings Histogram Example
Key/value RDD's and the Average Friends by Age example
Filtering RDD's and the Minimun Temperature by Location Example
Check Your Results and Implementation Against Min
- Spark SQL, Data Frames, and Data Sets
Introduction to Spark SQL
What are Data Frames
What are Data Sets
Item-Based Collaborative Filtering in Spark, cache (), and persist ()
- Running Spark on a cluster
What is a Cluster
Cluster management in Hadoop
Introducing Amazing Elastic MapReduce
Partitioning Concepts
Troubleshooting and managing dependencies
- Machine Learning with Spark ML
Introduction MLLib
Using MLLib
Linear Regression with MLLib
- Spark Streaming
Spark Streaming
The DStream API for Spark Streaming
- Graph X
What is Graph X
About Pregel
Breadth-First-Search with Pregel
Using Pregel API with Spark API
- Overview
Introduction to Big Data
Big Data Analytics
Need for Kafka
What is Kafka
Kafka Features
Kafka Concepts
Kafka Architecture
Kafka Components
Zookeeper
Where is Kafka Used
Kafka Installation
Kafka Cluster
Type of Kafka Clusters
Configuring Single Node Single Broker Cluster
- Kafka Producer
Configuring Single Node Multi Broker Cluster
Sending a Message to Kafka
Producing Keyed and Non-Keyed Messages
Sending a Message Synchronously & Asynchronously
Configuring Producers
Serializers
Serializing Using Apache Avro
Partitions
- Kafka Consumers
Consumers and Consumer Groups
Standalone Consumer
Consumer Groups and Partition Rebalance
Creating a Kafka Consumer
Subscribing to Topics
The Poll Loop
Configuring Consumers
Commits and Offsets
Rebalance Listeners
Consuming Records With Specific Offsets
De-serializers
- Kafka Internals
Cluster Membership
The Controller
Replication
Request Processing
Physical Storage
Reliability
Broker Configuration
Using Producers in a Reliable System
Using Consumers in a Reliable System
Validating System Reliability
Performance Tuning in Kafka
- Cluster Architecture and Administering Kafka
Use Cases - Cross-Cluster Mirroring
Multi-Cluster Architectures
Apache Kafka’s Mirror Maker
Other Cross-Cluster Mirroring Solutions
Topic Operations
Consumer Groups
Dynamic Configuration Changes
Partition Management
Consuming and Producing
Unsafe Operations
- Kafka Monitoring & Kafka Connect
Considerations When Building Data Pipelines
Metric Basics
Kafka Broker Metrics
Client Monitoring
Lag Monitoring
End-to-End Monitoring
Kafka Connect
When to Use Kafka Connect
Kafka Connect Properties
- Kafka Stream Processing
Stream Processing
Stream-Processing Concepts
Stream-Processing Design Patterns
Kafka Streams by Example
Kafka Streams: Architecture Overview
- Kafka Integration with Hadoop, Storm, and Spark
Apache Hadoop Basics
Hadoop Configuration
Kafka Integration with Hadoop
Apache Storm Basics
Configuration of Storm
Integration of Kafka with Storm
Apache Spark Basics
Spark Configuration
Kafka Integration with Spark
- Kafka Integration with Flume, Talend and Cassandra
Flume Basics
Integration of Kafka with Flume
Cassandra Basics Such as Key Space and Table Creation
Integration of Kafka with Cassandra
Talend Basics
Integration of Kafka with Talend
- Career Guidance and Roadmap
Apache Hadoop Overview
Hadoop Distribution File System
Hadoop MapReduce Overview
Introduction to PIG
Prerequisites for Apache PIG
Exploring use cases for PIG
History of Apache PIG
Why you need PIG
Significance od PIG
PIG over MapReduce
When PIG suits the most
When to avoid PIG
- PIG Architecture
PIG Latin Language
Running PIG in Different Modes
PIG Architecture
GRUNT Shell
PIG Latin Statements
Running Pig Scripts
Utility Commands
- Data Models, Operators, and Streaming in PIG
PIG Data Model - Scarlar Data Type
PIG Data Model - Complex Data Type
Arithmetic Operators
Comparison Operators
Cast Operators
Type Construction Operators
Relation Operators
Loading and Stroing Operators
Filtering Operators
Filtering Operators- Pig Streaming with Python
Grouping and Joining Operators-
Sorting Operator
Combining and Splitting Operators
Diagnostic Operators
- Functions in PIG
Eval Functions
Load and Store Funtions
Tuple and Bag Functions
String Functions
Math Function
- Advanced Concepts in PIG
File compression in PIG
Intermediate Compression
Pig Unit Testing
Embedded PIG in JAVA
Pig Macros
Import Macros
Parameter Substitutions
- Hadoop Overview
Course overview
Big Data Overview
Hadoop Overview
HDFS
Hadoop Ecosystem
What is a Hadoop Framework
Type of Hadoop Frameworks
- No SQL Databases Hbase
NoSQL Databases Hbase
NoSQL Introduction
HBase Overview
HBase Architecture
Data Model
Connecting to HBase
HBase Shell
- Administration in HBASE
Introduction
Learn and Understand Hbase Fault Tolerance
Hardware Recommendations
Software Recommendations
Hbase Deployment at scale
Installation with Cloudera Manager
Basic Static Configuration
Rolling Restarts and Upgrades
Interacting with HBase
- Troubleshooting in Hbase
Introduction
Troubleshooting Distributed Clusters
Learn How To Use the Hbase UI
Learn How To Use the Metrics
Learn How To Use the Logs
- Troubleshooting in Hbase
Introduction
Generating Load & Load Test Tool
Generating With YCSB
Region Tuning
Table Storage Tuning
Memory Tuning
Tuning with Failures
Tuning for Modern Hardware
- Apache HBase Operations Continuity
Introduction
Corruption: hbck
Corruption: Other Tools
Security
Security Demo
Snapshots
Import Export and copy Paste
Cluster Replication
- Apache HBASE Ecosystem
Introduction
Hue
HBase With Apache Phoenix’
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Projects
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