Praveen L

Tutor for Bigdata, HBase, Hadoop, Hive, Impala and Spark with 2 years of teaching experience

English,Telugu

Sholinganallur, Chennai, Tamil Nadu, India

Available for : Online Teaching | Tuition at my place | Tuition at your place

BigdataHBaseHadoopHiveImpalaSpark

About

Having 6 years of IT and 2 years of teaching experience in Bigdata, HBase, Hadoop, Hive, Impala and Spark. Good knowledge in Hdfs, Sqoop, Pig and Map reduce. Currently working in TCS as Hadoop and Spark developer. Completed Bachelor of Technology in Computer Science.

Expertise

Bigdata - 2 years experience 330 INR / Hour

What is Big Data? What is Hadoop? History of Hadoop Hadoop Eco-Systems Hadoop Framework Hadoop Distribution

Expert in using tools like Hive, Pig, Imapala, Map reduce, Sqoop, HBase and Oozie.

HBase - 1 years experience 330 INR / Hour

Introduction Architecture Creating tables and loading data Column families RDBMS vs no sql Latency

I can teach all topics in HBase.

Hadoop - 2 years experience 330 INR / Hour

Full course

HDFS:

  • Hdfs design
  • Hadoop file systems
  • HDFS Daemons
  • Master and Slave node architecture
  • How to load the data into HDFS
Sqoop:
  • How to get the data from Mysql tables to Hadoop Hdfs
  • How to perform import and export between the data stores
  • Sqoop installations
  • Sqoop1 vs Sqoop2
  • Sqoop connectors for oracle
  • Mysql
  • Teradata and other RDBMS
  • Sqoop commands
  • Exporting the data from HDFS to RDBMS
Pig:
  • Pig Introduction
  • Pig installation
  • Pig Latin commands
  • Built-in functions
  • Data processing in pig
  • Load and store
  • Filtering the data
  • Grouping the data
  • Joining the data
  • Sorting the data
  • Loading Hive data
  • Word count in Pig
Map reduce:
  • How Map-Reduce framework works
  • How to create and run a map reduce programs
  • MapReduce Overview
  • MapReduce Architecture
  • MapReduce Data Types
  • Developing MapReduce Jobs
  • Steps to write MapReduce Programs
  • Performance and Tuning of MapReduce Jobs
  • Distributed Cache
  • Writing MapReduce jobs in Eclipse
  • Word count program in MapReduce
  • Yarn Architecture
  • Yarn Configurations
  • Yarn and MapReduce 2.0 Daemons

Hive - 2 years experience 330 INR / Hour

Metastore configurations Built-in functions Hive Tables Creating tables Partitions and buckets Browsing tables and partitions Storage formats Loading data Joins Aggregations and sorting Insert into local files Altering Dropping tables Importing data

I can teach all topics in Hive.

Impala - 2 years experience 330 INR / Hour

Metastore configurations Built-in functions Impala Tables Creating tables Partitions and buckets Browsing tables and partitions Storage formats Loading data Joins Aggregations and sorting Insert into local files Altering Dropping tables Importing data

I can teach all topics in Impala.

Spark - 1 years experience 330 INR / Hour

Full course

  • Introduction
  • Need of New Generation Distributed Systems
  • Limitations of MapReduce in Hadoop
  • Batch vs Real-Time Processing
  • Application of Stream Processing Preview
  • Application of In-Memory Processing
Apache Spark
  • Introduction to Apache Spark
  • Spark Execution Architecture
  • Automatic Parallelization of Complex Flows
  • Running Spark in Different Modes
  • Installing Spark as a Standalone Cluster-Configurations
  • Spark-core
RDD:
  • Introduction
  • RDDs API
  • Features of RDDs
  • Creating RDDs
  • Creating RDDs—Referencing an External text files or parallelize
  • RDD Operations
  • RDD Operations—Transformations
  • Features of RDD Persistence
  • Storage Levels Of RDD Persistence
  • Choosing The Correct RDD Persistence Storage Level
  • Invoking the Spark Shell
  • Importing Spark Classes
  • Creating the SparkContext
  • Create a Spark Project
  • Running a Spark Project With Eclipse
  • —Broadcast Variables
  • —Accumulators
  • Demo-Run a Scala Application
  • Run a Scala Application
  • Demo-Write a Scala Application Reading the Hadoop Data Transformations in RDD, Actions in RDD
  • Key-Value Pair RDD
Spark-SQL
  • Introduction
  • Importance of Spark SQL
  • DataFrames
  • SQLContext
  • Creating a DataFrame
  • DataFrame Operations
  • Demo-Run SparkSQL with a Dataframe
  • Data Sources
  • Save Modes
  • Saving to Persistent Tables
  • Parquet Files
  • Supported Hive Features
  • Case Classes
Spark-Streaming
  • Introduction to Spark Streaming
  • Working of Spark Streaming
  • Features of Spark Streaming
  • Streaming Word Count
  • Micro Batch
  • DStreams
  • Input DStreams and Receivers
  • Sources
  • Transformations on DStreams
  • DataFrame and SQL Operations
  • Checkpointing
  • Socket Stream
  • File Stream
  • Stateful Operations
  • Window Operations
  • Types of Window Operations,Performance Tuning

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