big data hadoop training in trivandrum
Looking to enhance your data skills? Explore the world of Big Data with Hadoop training in Trivandrum. Learn Hadoop's fundamentals, including HDFS, MapReduce, and YARN, and gain hands-on experience in processing and analyzing massive datasets. Start your journey towards becoming a Big Data expert today!
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In today's data-driven world, organizations are constantly seeking professionals with expertise in Big Data technologies. Hadoop, a powerful open-source framework, has become a game-changer for managing and processing massive datasets. If you're looking to enhance your data skills and embark on a career in Big Data, Hadoop training in Trivandrum can be your gateway to success.
This article will guide you through the significance of Hadoop training, the key concepts you'll learn, and the benefits it offers in Trivandrum. Get ready to unlock the potential of Big Data and take your career to new heights.
Course Syllabus
Course Introduction
- Introduction
- Accessing Practice Lab
- Introduction to Big Data and Hadoop
- Introduction to Big Data
- Big Data Analytics
- What is Big Data
- Four Vs Of Big Data
- Case Study
- Challenges of Traditional System
- Distributed Systems
- Introduction to Hadoop
- Components of Hadoop Ecosystem: Part One
- Components of Hadoop Ecosystem: Part Two
- Components of Hadoop Ecosystem: Part Three
- Commercial Hadoop Distributions
Hadoop Architecture,Distributed Storage(HDFS) and YARN
- What Is HDFS
- Need for HDFS
- Regular File System vs HDFS
- Characteristics of HDFS
- HDFS Architecture and Components
- High Availability Cluster Implementations
- HDFS Component File System Namespace
- Data Block Split
- Data Replication Topology
- HDFS Command Line
- YARN Introduction
- YARN and Its Architecture
- Resource Manager
- How Resource Manager Operates
- Application Master
- How YARN Runs an Application
- Tools for YARN Developers
Data Ingestion into Big Data Systems and ETL
- Data Ingestion Overview Part One
- Data Ingestion
Apache Sqoop
- Sqoop and Its Uses
- Sqoop Processing
- Sqoop Import Process
- Sqoop Connectors
Apache Flume
- Flume Model
- Scalability in Flume
- Components in Flume’s Architecture
- Configuring Flume Components
Apache Kafka
- Aggregating User Activity Using Kafka
- Partitions
- Apache Kafka Architecture
- Producer Side API Example
- Consumer Side API
- Consumer Side API Example
- Kafka Connect
Distributed Processing MapReduce Framework and Pig
- Distributed Processing in MapReduce
- Word Count Example
- Map Execution Phases
- Map Execution Distributed Two Node Environment
- MapReduce Jobs
- Hadoop MapReduce Job Work Interaction
- Setting Up the Environment for MapReduce Development
- Set of Classes
- Advanced MapReduce
- Data Types in Hadoop
- OutputFormats in MapReduce
- Using Distributed Cache
- Joins in MapReduce
- Replicated Join
- Introduction to Pig
- Components of Pig
- Pig Data Model
- Pig Interactive Modes
- Pig Operations
- Relations Performed by Developers
- Apache Pig
Apache Hive
- Hive SQL over Hadoop MapReduce
- Hive Architecture
- Interfaces to Run Hive Queries
- Running Beeline from Command Line
- Hive Metastore
- Hive DDL and DML
- Creating New Table
- Data Types
- Validation of Data
- File Format Types
- Data Serialization
- Hive Table and Avro Schema
- Hive Optimization Partitioning Bucketing and Sampling
- Non Partitioned Table
- Data Insertion
- Dynamic Partitioning in Hive
- Bucketing
- What Do Buckets Do
- Hive Analytics UDF and UDAF
- Assisted Practice: Synchronization
- Other Functions of Hive
- NoSQL Databases HBase
- NoSQL Introduction
HBase Overview
- HBase Architecture
- Data Model
- Connecting to HBase
- HBase Shell
Basics of Functional Programming and Scala
- Introduction to Scala
- Scala Installation
- Functional Programming
- Programming with Scala
- Type Inference Classes Objects and Functions in Scala
- Collections
- Types of Collections
- Scala REPL
- History of Spark
- Limitations of MapReduce in Hadoop
- Introduction to Apache Spark
- Components of Spark
- Application of In-Memory Processing
- Hadoop Ecosystem vs Spark
- Advantages of Spark
- Spark Architecture
- Spark Cluster in Real World
- Spark Core Processing RDD
- Processing RDD
- Introduction to Spark RDD
- RDD in Spark
- Creating Spark RDD
- Pair RDD
- RDD Operations
- Caching and Persistence
- Storage Levels
- Lineage and DAG
- Need for DAG
- Debugging in Spark
- Partitioning in Spark
- Scheduling in Spark
- Shuffling in Spark
- Sort Shuffle
Aggregating Data with Pair RDD
- Spark SQL Processing DataFrames
- Spark SQL Introduction
- Spark SQL Architecture
- DataFrames
- Interoperating with RDDs
- RDD vs DataFrame vs Dataset
- Processing DataFrames
- Spark MLlib Modeling Big Data with Spark
Role of Data Scientist and Data Analyst in Big Data
- Analytics in Spark
- Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Semi-Supervised Learning
- Overview of MLlib
- MLlib Pipelines
- Stream Processing Frameworks and Spark Streaming
- Streaming Overview
- Real-Time Processing of Big Data
Data Processing Architectures
- Spark Streaming
- Introduction to DStreams
- Transformations on DStreams
- Design Patterns for Using ForeachRDD
- State Operations
- Windowing Operations
- Join Operations stream-dataset Join
- Streaming Sources
- Structured Spark Streaming
- Structured Streaming Architecture Model and Its Components
- Output Sinks
- Structured Streaming APIs
- Constructing Columns in Structured Streaming
- Windowed Operations on Event-Time
- Spark GraphX
- Introduction to Graph
- Graphx in Spark
- Graph Operators
- Join Operators
- Graph Parallel System
- Algorithms in Spark
- Pregel API
- Use Case of GraphX
Big data hadoop training in trivandrum Online mode
- RELSOFT SYSTEMS TRIVANDRUM
- Address: RG-85, Sreenagar Lane
- Phone: 0471 255 1755
- Techvarsity
- Address: Technopark Trivandrum
- Phone: 077361 28596
- Bigdata Devops Training
- Address: shieldhub Trinity Tower, TC 33/1402(11), ground floor, chackai bypass road, Pettah post office
- Phone: 095265 28596
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