Apache Hive
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Course Overview
Master core concepts on hadoop distributed file system and Understand apache pig and advanced apache hive programming concepts as you learn with our certified experts. Learn how to use Hcatalog, joining datasets in apache hive and HDFS Commands.Gain practical experience to import and export RDBMS data into HDFS, analyze clickstream data. Data using quantiles. With our cloudlabs get hands-on experience to run a YARN application, apache hive programming, analyzing big data with apache hive, join datasets with apache pig and starting an HDP cluster.
At the end of the training, participants will be able to:
- Explain Hadoop and the Hadoop Distributed File System (HDFS)
- Interpret Common HDFS Commands Types
- Export Table
- Distinguish between Relational Databases and Hadoop
- Explain Purpose of NameNodes, DataNode, MapReduce and Reduce Phases
- Differentiate Pig Latin Relation Names and Field Names
- Explain programming concepts using PIG and HIVE.
- Perform Inner, Outer and Replicated Join
- Demonstrate the Use of HCatLoader and HCatStorer with Apache Pig
- Explain Lifecycle of YARN Applications
- Common use cases of Spark
- Load Data and Perform a Word Count
- Perform SQL Queries
- Perform DataFrame Operations
- Submit an Apache Oozie Workflow
Pre-requisite
- Should be familiar with programming principles and have experience in software development.
- SQL knowledge is also helpful.
- No prior Hadoop knowledge is required.
Duration
2 days
Course Outline
- List the Three “V”s of Big Data
- List the Six Key Hadoop Data Types
- Describe Hadoop, YARN and Use Cases for Hadoop
- Describe Hadoop Ecosystem Tools and Frameworks
- Describe the Differences Between Relational Databases and Hadoop
- Describe What is New in Hadoop 2.x
- Describe the Hadoop Distributed File System (HDFS)
- Describe the Differences Between HDFS and an RDBMS
- Describe the Purpose of NameNodes and DataNodes
- List Common HDFS Commands
- Describe HDFS File Permissions
- List Options for Data Input
- Describe WebHDFS
- Describe the Purpose of Sqoop and Flume
- Describe How to Export to a Table
- Describe the Purpose of MapReduce
- Define Key/Value Pairs in MapReduce
- Describe the Map and Reduce Phases
- Describe Hadoop Streaming
- Starting an HDP Cluster
- Demonstration: Understanding Block Storage (Lab)
- Using HDFS Commands (Lab)
- Importing RDBMS Data into HDFS (Lab)
- Exporting HDFS Data to an RDBMS (Lab)
- Importing Log Data into HDFS Using Flume (Lab)
- Demonstration: Understanding MapReduce (Lab)
- Running a MapReduce Job (Lab)
- Describe the Purpose of Apache Pig
- Describe the Purpose of Pig Latin
- Demonstrate the Use of the Grunt Shell
- List Pig Latin Relation Names and Field Names
- List Pig Data Types
- Define a Schema
- Describe the Purpose of the GROUP Operator
- Describe Common Pig Operators ( ORDER BY, CASE, DISTINCT, PARALLEL, FLATTEN, FOREACH)
- Perform an Inner, Outer and Replicated Join
- Describe the Purpose of the DataFu Library
- Demonstration: Understanding Apache Pig (Lab)
- Getting Starting with Apache Pig (Lab)
- Exploring Data with Apache Pig (Lab)
- Splitting a Dataset (Lab)
- Joining Datasets with Apache Pig (Lab)
- Preparing Data for Apache Hive (Lab)
- Demonstration: Computing Page Rank (Lab)
- Analyzing Clickstream Data (Lab)
- Analyzing Stock Market Data Using Quantiles (Lab)
- Describe the Purpose of Apache Hive
- Describe the Differences Between Apache Hive and SQL
- Describe the Apache Hive Architecture
- Demonstrate How to Submit Hive Queries
- Describe How to Define Tables
- Describe How to Load Date Into Hive
- Define Hive Partitions, Buckets and Skew
- Describe How to Sort Data
- List Hive Join Strategies
- Describe the Purpose of HCatalog
- Describe the HCatalog Ecosystem
- Define a New Schema
- Demonstrate the Use of HCatLoader and HCatStorer with Apache Pig
- Perform a Multi-table/File Insert
- Describe the Purpose of Views
- Describe the Purpose of the OVER Clause
- Describe the Purpose of Windows
- List Hive Analytics Functions
- List Hive File Formats
- Describe the Purpose of Hive SerDe
- Understanding Hive Tables (Lab)
- Understanding Partition and Skew (Lab)
- Analyzing Big Data with Apache Hive (Lab)
- Demonstration: Computing NGrams (Lab)
- Joining Datasets in Apache Hive (Lab)
- Computing NGrams of Emails in Avro Format (Lab)
- Using HCatalog with Apache Pig (Lab)
- Describe the Purpose HDFS Federation
- Describe the Purpose of HDFS High Availability (HA)
- Describe the Purpose of the Quorum Journal Manager
- Demonstrate How to Configure Automatic Failover
- Describe the Purpose of YARN
- List the Components of YARN
- Describe the Lifecycle of a YARN Application
- Describe the Purpose of a Cluster View
- Describe the Purpose of Apache Slider
- Describe the Origin and Purpose of Apache Spark
- List Common Spark Use Cases
- Describe the Differences Between Apache Spark and MapReduce
- Demonstrate the Use of the Spark Shell
- Describe the Purpose of an Resilient Distributed Dateset (RDD)
- Demonstrate How to Load Data and Perform a Word Count
- Define Lazy Evaluation
- Describe How to Load Multiple Types of Data
- Demonstrate How to Perform SQL Queries
- Demonstrate How to Perform DataFrame Operations
- Describe the Purpose of the Optimization Engine
- Describe the Purpose of Apache Oozie
- Describe Apache Pig Actions
- Describe Apache Hive Actions
- Describe MapReduce Actions
- Describe How to Submit an Apache Oozie Workflow
- Define an Oozie Coordinator Job
- Advanced Apache Hive Programming (Lab)
- Running a YARN Application (Lab)
- Getting Started with Apache Spark (Lab)
- Exploring Apache Spark SQL (Lab)
- Defining an Apache Oozie Workflow (Lab)
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