Lucidtechsystems is a brand and providing quality Online, Classroom, Corporate, Weekends and Fast track training on demand to the students in worldwide on Hadoop Training In Hyderabad. Lucidtechsystems is one best Hadoop Training institute in Hyderabad and providing IT End-to-End training with real-time hands-on exposure through the IT Experts to the students. We are giving best on Hadoop online Training and offline training in Hyderabad. Lucidtechsystems provides flexibility to the students while choosing online classes, classroom training, corporate training, the summary of courses and their scope.
Faculty : Real-time experience
Lucidtechsystems Manage risk and reduce compliance costs with end-to-end processes for compliance, governance, and risk. Class discussion, class assignments, A/V presentation, real-life experience, classroom exercises, and self-study.
Highlights In Our Big Data and Hadoop Training In Hyderabad Service:
In Lucidtechsystems all faculties or trainers have experienced the pleasure of training and trained Resources is accessible all over the world. Lucidtechsystems Hadoop Training in Hyderabad leads to excel understanding, IT Industry latest skills requirement and proficiency with consulting service LucidTechSystems real-time skilled instructors accomplish your ideas/thoughts and create competently /expertly driven environment. We will develop the association with DEVELOPMENT, TESTING, QA, UAT, STAGE and PRODUCTION Environments.
Lucidtechsystems help out in certification programs , Resume preparation, Boost Interview skills, Live Project life cycle, evolution, clarifying Doubts through Questions and Answers Method, Providing the Material with Video Sessions, troubleshooting bugs or issues in different target environment such as DEVELOPMENT, TESTING, QA, UAT, STAGE and PRODUCTION, support the novel thoughts.
Lucidtechsystems is one best on “Big Data And Hadoop Training“ through online and offline or classroom training center in Hyderabad , We also provide on “Hadoop Training in Hyderabad online“ in India, Chennai, Bangalore, Mumbai, Calcutta, Pune, Delhi, Noida, gorgon, NCR region, USA, UK, Canada, Japan, Malaysia, Singapore, UAE, Kuwait , Abu Dhabi, Dubai, Australia, South Africa etc.
See other related courses Cloudera training also.
During this course, you will learn:
- Introduction to Big Data and Analytics
- Introduction to Hadoop
- Hadoop ecosystem – Concepts
- Hadoop Map-reduce concepts and features
- Developing the map-reduce Applications
- Pig concepts
- Hive concepts
- Sqoop concepts
- Flume Concepts
- Oozie workflow concepts
- Impala Concepts
- Hue Concepts
- HBASE Concepts
- ZooKeeper Concepts
- Real Life Use Cases
- Why Hadoop?
- Distributed Framework
- Hadoop v/s RDBMS
- Brief history of Hadoop
- Pseudo mode
- Cluster mode
- Installation of java, Hadoop
- Configurations of Hadoop
- Hadoop Processes ( NN, SNN, JT, DN, TT)
- Temporary directory
- Common errors when running Hadoop cluster, solutions
5.HDFS- Hadoop distributed File System
- HDFS Design and Architecture
- HDFS Concepts
- Interacting HDFS using command line
- Interacting HDFS using Java APIs
- Name node
- Secondary name node
- Job tracker
- Task tracker
- Data node
- Developing Map Reduce Application
- Phases in Map-Reduce Framework
- Map Reduce Input and Output Formats
- Advanced Concepts
- Sample Applications
8.Joining datasets in MapReduce jobs
- Map-side join
- Reduce-Side join
9.Map-reduce – customization
- Custom Input format class
- Hash Partitioner
- Custom Partitioner
- Sorting techniques
- Custom Output format class
- Hadoop Programming Languages :-
- Installation and Configuration
- Interacting HDFS using HIVE
- Map Reduce Programs through HIVE
- HIVE Commands
- Loading, Filtering, Grouping….
- Data types, Operators…..
- Joins, Groups….
- Sample programs in HIVE
- Installation and Configurations
- OVERVIEW HADOOP DEVELOPER
- The Motivation for Hadoop
- Problems with traditional large-scale systems
- Requirements for a new approach
- Hadoop: Basic Concepts
- An Overview of Hadoop
- The Hadoop Distributed File System
- Hands-On Exercise
- How MapReduce Works
- Hands-On Exercise
- Anatomy of a Hadoop Cluster
- Other Hadoop Ecosystem Components
14.Writing a MapReduce Program
- The MapReduce Flow
- Examining a Sample MapReduce Program
- Basic MapReduce API Concepts
- The Driver Code
- The Mapper
- The Reducer
- Hadoop’s Streaming API
- Using Eclipse for Rapid Development
- Hands-on exercise
- The New MapReduce API
15.Common MapReduce Algorithms
- Sorting and Searching
- Machine Learning With Mahout
- Term Frequency – Inverse Document Frequency
- Word Co-Occurrence
- Hands-On Exercise.
- Data loading in PIG.
- Data Extraction in PIG.
- Data Transformation in PIG.
- Hands-on exercise on PIG.
- Hive Concepts.
- Hive Query Language.
- Alter and Delete in Hive.
- Partition in Hive.
- Joins in Hive.Unions in the hive.
- Industry specific configuration of HIVE parameters.
- Authentication & Authorization.
- Statistics with Hive.
- Archiving in Hive.
- Hands-on exercise
- Working with Sqoop
- Import Data.
- Export Data.
- Sqoop Syntaxes.
- Databases connection.
- Hands-on exercise
- Working with Flume
- Configuration and Setup.
- Flume Sink with the example.
- Flume Source with the example.
- Complex flume architecture.
This course is designed for the beginner to intermediate-level Tableau user. It is for anyone who works with data – regardless of technical or analytical background. This course is designed to help you understand the important concepts and techniques used in Tableau to move from simple to complex visualizations and learn how to combine them in interactive dashboards.
- What is visual analysis?
- strengths/weakness of the visual system.
Laying the Groundwork for Visual Analysis
- Analytical Process
- Preparing for analysis
Getting, Cleaning and Classifying Your Data
- Cleaning, formatting and reshaping.
- Using additional data to support your analysis.
- Data classification
Visual Mapping Techniques
- Visual Variables : Basic Units of Data Visualization
- Working with Color
- Marks in action: Common chart types
Solving Real-World Problems with Visual Analysis
- Getting a Feel for the Data- Exploratory Analysis.
- Making comparisons
- Looking at (co-)Relationships.
- Checking progress.
- Spatial Relationships.
- Try, try again.
Communicating Your Findings
- Fine-tuning for more effective visualization
- Storytelling and guided analytics
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