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Big Data

Big Data

BIG DATA

KEY COMPETITIVE EDGE

The need to safely store and process information

With the rise of Big Data, successful businesses are seeing an ever-increasing need to safely store and process tremendous amounts of information – information that in turn becomes the key competitive edge allowing the company to become or maintain status as a leader in its industry.

Whether it’s Hadoop, Pig, Hive, Sqoop, Spark, Cassandra, Openstack, Kafka, HBase, Solr, R, Python, Scala or a unique topic, we’ll help you navigate the prolific list of technologies and tools in order to maximize your class room time.

Courses are available for every role including developer, executive, analyst and administrator, so get started today with a free consultation.

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BIG DATA COURSES

Become the key competitive edge.

Big Data

Apache

Hadoop

OpenStack

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Founded in 2006 on the core belief that there is a superior way to train technical teams

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BIG DATA

Click on the links below to view course outlines, pre-requisites, course length and course objectives.

Big Data Overview

Course Number:

N/A

Participants of this course will learn the theory and practice of Big Data. Upon completion they will be able to create data-driven applications that make modern enterprise tick. This course contains an ideal mix of theory and practice, with the theory portion illustrated by hands-on examples and accomplished with industry-leading tools.

Audience:

This course is intended for data scientists, IT managers, project leads and software developers.

Course Duration:

1 day

Prerequisites:

Attendees should possess basic computer literacy (Windows, Mac or Linux) and a desire to learn and ask questions. Command line skills are optional.

Course Objectives:

Course Outline:

Current state of Big Data

Big Data Tools
- Hadoop and Ecosystem
- Spark
- NoSQL
- Storm
- Search

Data
- Cleaning and Validation
- Security
- Privacy
- Messiness

Extracting Actionable Information
- Data Exploration
- Machine Learning
- Data Modeling and Prediction

Data Analytics with R

Course Number:

N/A

R is a popular open source environment for statistical computing, data analytics and graphics. This course introduces R programming language to students and covers language fundamentals, libraries and advanced concepts. Advanced data analytics and graphing with real world data are also included.

Audience:

This course is designed for developers and data analysts.

Course Duration:

3 days

Prerequisites:

A basic programming background is preferred.

Course Objectives:

Course Outline:

Language Basics (one day)
- Course Introduction
- About Data Science
  - Data Science Definition
  - Process of Doing Data Science
- Introducing R Language
- Variables and Types
- Control Structures (Loops / Conditionals)
- R Scalars, Vectors and Matrices
  - Defining R Vectors
  - Matrices
- String and Text Manipulation
  - Character Data Type
  - File IO
- Lists
- Functions
  - Introducing Functions
  - Closures
  - lapply/sapply Functions
- DataFrames
- Labs
Intermediate R Programming (one day)
- DataFrames and File I/O
- Reading Data from Files
- Data Preparation
- Built-In Datasets
- Visualization
  - Graphics Package
  - plot() / barplot() / hist() / boxplot() / scatter plot
  - Heat Map
  - ggplot2 package ( qplot(), ggplot())
- Exploration with Dplyr
- Labs
Advanced Programming with R (one day)
- Statistical Modeling with R
  - Statistical Functions
  - Dealing with NA
  - Distributions (Binomial, Poisson, Normal)
- Regression
  - Introducing Linear Regressions
- Recommendations
- Text Processing (tm package / wordcloud)
- Clustering
  - Introduction to Clustering
  - KMeans
- Classification
  - Introduction to Classification
  - Naive Bayes
  - Decision Trees
  - Training using Caret Package
  - Evaluating Algorithms
- R and Big Data
  - Hadoop
  - Big Data Ecosystem
  - RHadoop
- Labs

APACHE

Click on the links below to view course outlines, pre-requisites, course length and course objectives.

Apache Kafka for Software Developers

Course Number:

N/A

Apache Kafka is a publish-subscribe messaging rethought as a distributed commit log, capable of handling hundreds of megabytes of reads and writes per second from thousands of clients on each broker. A Kafka cluster can serve as the central data backbone for a large organization.

Audience:

This course is specifically designed for software developers.

Course Duration:

2 days

Prerequisites:

Participants should have basic computer literacy (Windows, Mac or Linux) and a desire to learn and use Kafka messaging system. Command line skills are optional.

Course Objectives:

Course Outline:

Introducing Kafka
- History
- Why Kafka?
- Kafka at LinkedIn
- Kafka Adoption in the Industry
- Homage to the Creator of Metamorphosis

Kafka Core Concepts
- Topics
- Partitions
- Replicas
- Producers
- Consumers
- Brokers

Operating Kafka
- Architecture
- Hardware Specs
- Deploying
- Monitoring
- Performance
- Scalability Tuning

Developing Kafka Apps
- Writing to Kafka
- Reading from Kafka
- Testing
- Serialization
- Compression
- Example Apps with kafka-storm-starter

Playing with Kafka Using Wirbelsturm

Machine Learning with Apache Spark

Course Number:

N/A

This course teaches machine learning at scale with the popular Apache Spark framework.

No machine learning knowledge is assumed. For each machine learning concept, we first discuss the foundations, its applicability and limitations. Then we explain the implementation and use, covering specific use cases. This is achieved through a combination of 50 percent lecture, 50 percent lab work.

Please note that this course does not include in-depth coverage of the maths/stats behind machine learning.

This course is taught using Spark and Python.

Audience:

This class is intended for data scientists and software engineers.

Course Duration:

3 Days

Prerequisites:

We assume no previous knowledge of Machine Learning. We teach popular Machine Learning algorithms from scratch.

Prior to taking this course, students should have:

A working knowledge of Apache Spark
- If students are new to Apache Spark, we can offer one day of Introduction to Spark training
A programming background
Familiarity with Python would be a plus, but not required

Course Objectives:

Learn popular machine learning algorithms, their applicability and limitations
Practice the application of these methods in the Spark machine learning environment
Learn practical use cases and limitations of algorithms

Course Outline:

Section One – Machine Learning (ML) Overview
- Machine Learning landscape
- Machine Learning applications
- Understanding ML algorithms and models (supervised and unsupervised)

Section Two – ML in Python and Spark
- Spark ML Overview
- Introduction to Jupyter notebooks
- Lab – Working with Jupyter + Python + Spark
- Lab – Spark ML utilities

Section Three – Machine Learning Concepts
- Statistics Primer
- Covariance, Correlation, Covariance Matrix
- Errors, Residuals
- Overfitting / Underfitting
- Cross validation, bootstrapping
- Confusion Matrix
- ROC curve, Area Under Curve (AUC)
- Lab – Basic stats

Section Four – Feature Engineering (FE)
- Preparing data for ML
- Extracting features, enhancing data
- Data cleanup
- Visualizing Data
- Lab – Data cleanup
- Lab – Visualizing data

Section Five – Linear regression
- Simple Linear Regression
- Multiple Linear Regression
- Running LR
- Evaluating LR model performance
- Lab
- Use case – House price estimates

Section Six – Logistic Regression
- Understanding Logistic Regression
- Calculating Logistic Regression
- Evaluating model performance
- Lab
- Use case –Credit card application, college admissions

Section Seven – Classification: SVM (Supervised Vector Machines)
- SVM concepts and theory
- SVM with kernel
- Lab
- Use case – Customer churn data

Section Eight – Classification: Decision Trees and Random Forests
- Theory behind trees
- Classification and Regression Trees (CART)
- Random Forest concepts
- Labs
- Use case – Predicting loan defaults, estimating election contributions

Section Nine – Classification: Naive Bayes
- Theory
- Lab
- Use case – Spam filtering

Section 10 – Clustering (K-Means)
- Theory behind K-Means
- Running K-Means algorithm
- Estimating the performance
- Lab
- Use case – Grouping cars data, grouping shopping data

Section 11 – Principal Component Analysis (PCA)
- Understanding PCA concepts
- PCA applications
- Running a PCA algorithm
- Evaluating results
- Lab
- Use case – Analyzing retail shopping data

Section 12 – Recommendation (Collaborative Filtering)
- Recommender systems overview
- Collaborative Filtering concepts
- Lab
- Use case – Movie recommendations, music recommendations

Section 13 – Final Workshop (time permitting)
- Students will analyze a couple of datasets and run ML algorithms
  - This is done as a group exercise. Each group will present findings to the class

Introduction to Apache Cassandra

Course Number:

N/A

This course will introduce Cassandra –a popular NoSQL database. It will cover Cassandra principles, architecture and data models. Students will learn data modeling in CQL (Cassandra Query Language) by participating in interactive, hands-on labs. This session also discusses Cassandra internals as well as some admin topics.

Audience:

This course is designed for developers.

Course Duration:

3 Days

Prerequisites:

Class participants should be comfortable with Java programming language and comfortable in the Linux environment (i.e., navigating command line, editing files with vi / nano).

Course Objectives:

Course Outline:

Introduction to Big Data / NoSQL
- NoSQL overview
- CAP Theorem
- When is NoSQL Appropriate?
- Columnar Storage
- NoSQL Ecosystem
Cassandra Basics
- Design and Architecture
- Cassandra Nodes, Clusters, Datacenters
- Keyspaces, Tables, Rows, Columns
- Partitioning, Replication, Tokens
- Quorum and Consistency Levels
- Labs
Data Modeling (Part One)
- Introduction to CQL
- CQL Datatypes
- Creating Keyspaces and Tables
- Choosing Columns and Types
- Choosing Primary Keys
- Data Layout for Rows and Columns
- Time to Live (TTL)
- Querying with CQL oCQL Updates
- Collections (list / map / set)
- Labs
Data Modeling (Part Two)
- Creating and Using Secondary Indexes
- Composite Keys – Partition and Clustering
- Time Series Data
- Best Practices for Time Series Data
- Counters
- Lightweight Transactions (LWT)
Data Modeling Labs – Group Design Session
- Multiple Use Cases from Various Domains Are Presented
- Students Work in Groups to Come Up with Designs and Models
- Discuss Various Designs, Analyze Decisions
- Lab: Implement One of the Scenarios
Cassandra Drivers
- Introduction to Java Driver
- CRUD (Create / Read / Update / Delete) Operations Using Java Client
- Asynchronous Queries
- Labs
Cassandra Internals
- Cassandra Design Under the Hood
- SSTables, Memtables, CommitLogoRead/Write Path
- Caching
- Vnodes
Administration
- Hardware Selection
- Cassandra Distributions
- Cassandra Best Practices (Compaction, Garbage Collection)
- Troubleshooting Tools and Tips
- Lab: Install Cassandra, Run Benchmarks
Bonus Lab (If Time Permits)
- Implement a Music Service like Pandora or Spotify on Cassandra

Introduction to Apache HBase

Course Number:

N/A

This engaging hands-on course introduces HBase, a NoSQL store on top of Hadoop. The course is intended for developers who will be using HBase to develop applications as well as administrators who will manage HBase clusters.

The instructor will walk participants through HBase architecture, data modeling and application development on HBase. Other subjects covered include using MapReduce with HBase and administration topics related to performance optimization. A significant portion of class time will be dedicated to interactive labs.

Audience:

This course is intended for developers and administrators.

Course Duration:

3 Days

Prerequisites:

Participants should be comfortable with the Java programming language (i.e., navigating Linux command line, editing files with vi / nano, etc.).

Course Objectives:

Course Outline:

Introduction to Big Data and NoSQL
- Big Data Ecosystem
- NoSQL Overview
- CAP Theorem
- When is NoSQL Appropriate?
- Columnar Storage
- HBase and NoSQL
HBase Intro
- Concepts and Design
- Architecture (HMaster and Region Server)
- Data integrity
- HBase Ecosystem
- Lab: Exploring HBase
HBase Data Model
- Namespaces, Tables and Regions
- Rows, Columns, Column Families, Versions
- HBase Shell and Admin commands
- Lab
Accessing HBase Using Java API
- Introduction to Java API
- Read / Write Path
- Time Series Data
- Scans
- Map Reduce
- Filters
- Counters
- Co-processors
- Labs (multiple)
HBase Schema Design (Group Session)
- Students Are Presented with Real World Use Cases
- Students Work in Groups to Produce Design Solutions
- Discuss, Critique and Learn from Multiple Designs
HBase Internals
- Understanding HBase Under the Hood
- Memfile / HFile / WAL
- HDFS Storage
- Compactions
- Splits
- Bloom Filters
- Caches
- Diagnostics
HBase Installation and Configuration
- Hardware Selection
- Install Methods
- Common Configurations
- Lab: Installing HBase
HBase Ecosystem
- Developing Applications Using HBase
- Interacting with Other Hadoop Stack (MapReduce, Pig, Hive)
- Frameworks Around HBase
- Advanced Concepts (Co-processors)
Monitoring and Best Practices
- Monitoring Tools and Practices
- Optimizing HBase
- HBase in the Cloud

Introduction to Apache Solr

Course Number:

N/A

This course introduces students to the Solr platform. Through a combination of lecture, discussion and labs, students will gain hands-on experience configuring effective search and indexing.

The class begins with basic Solr installation and configuration then teaches the attendees the search features of Solr. Students will gain experience with faceting, indexing and search relevance among other features central to the Solr platform. The course concludes with a number of advanced topics, including spell checking, suggestions, Multicore and SolrCloud.Introduction to Apache Solr

Audience:

This course is appropriate for developers, business users and administrators.

Course Duration:

3 Days

Prerequisites:

All attendees should be experienced technical staff with a background in web application operations and preferably development as well.

Course Objectives:

To provide experienced web developers and technical staff with a comprehensive introduction to the Solr search platform
To teach software developers skills for creating search solutions

Course Outline:

Fundamentals
- Solr Overview
- Installing and Running Solr
- Adding Content to Solr
- Reading a Solr XML Response
- Changing Parameters in the URL
- Using the Browse Interface
Searching
- Sorting Results
- Query Parsers
- More Queries
- Hardwiring Request Parameters
- Adding Fields to Default Search
- Faceting
- Result Grouping
Indexing
- Adding Your Own Content to Solr
- Deleting data from Solr
- Building a Bookstore Search
- Adding Book Data
- Exploring the Book Data
- Dedupe Update Processor
Schema Updating
- Adding Fields to the Schema
- Analyzing Text
Relevance
- Field Weighting
- Phrase Queries
- Function Queries
- Fuzzier Search
- Sounds-Like
Extended Features
- More-Like-This
- Geospatial
- Spell Checking
- Suggestions
- Highlighting
- Pseudo-Fields
- Pseudo-Joins
- Multilanguage
Multicore
- Adding More Kinds of Data
SolrCloud
- Introduction
- How SolrCloud Works
- Commit Strategies
- ZooKeeper
- Managing Solr Config Files
Developing with Solr API
- Talking to Solr through REST
- Configuration
- Indexing and Searching
- Solr and Spring
Developing with Lucene API
- Building a Lucene Index
- Searching, Viewing, Debugging
- Extracting Text with Tika
- Scaling Lucene Indices On Clusters
- Lucene Performance Tuning
Conclusion
- Other Approaches to Search
  - ElasticSearch
  - DataStax Enterprise: Solr+Cassandra
  - Cloudera Solr Integration
  - Blur
- Future directions

Introduction to Apache Spark

Course Number:

N/A

This course will familiarize participants with Apache Spark, explaining how Spark fits into the big data ecosystem and how to use Spark for data analysis. Topics covered will include Spark shell for interactive data analysis, Spark internals, Spark APIs, Spark SQL, Spark streaming, machine learning and GraphX.

Audience:

This course is appropriate for developers and data analysts.

Course Duration:

3 Days

Prerequisites:

Familiarity with either Java, Scala or Python language is recommended (labs are in Scala and Python)
A basic understanding of the Linux development environment (i.e., command line navigation / editing files using VI or nano) is also required

Course Objectives:

Course Outline:

Scala Primer
- A Quick Introduction to Scala
Spark Basics
- Background and History
- Spark and Hadoop
- Spark Concepts and Architecture
- Spark Ecosystem (Core, Spark SQL, MLlib, Streaming)
RDDs
- Running Spark in Local Mode
- Spark Web UI
- Spark Shell
- Analyzing Dataset (Part One)
- Inspecting RDDs
RDDs In-Depth
- Partitions
- RDD Operations /Transformations
- RDD Types
- Key-Value Pair RDDs
- MapReduce on RDD
- Caching and persistence
Spark and Hadoop
- Hadoop Introduction (HDFS / YARN)
- Hadoop + Spark Architecture
- Running Spark on Hadoop YARN
- Processing HDFS files using Spark
Spark API Programming
- Introduction to Spark API / RDD API
- Submitting the First Program to Spark
- Debugging / Logging
- Configuration Properties
Spark SQL
- SQL Context
- Defining Tables and Importing Datasets
- Querying
Spark Streaming
- Streaming Overview
- Streaming Operations
- Sliding Window Operations
- Writing Spark Streaming Applications
Spark MLlib
- MLlib Introduction
- MLlib Algorithms
- Writing MLlib Applications
Spark GraphX
- GraphX Library Overview
- GraphX APIs
- Processing Graph Data Using Spark
Spark Performance and Tuning
- Broadcast Variables
- Accumulators
- Memory Management
Bonus Lab: Running Spark in Cluster Mode
- Inspecting Masters and Workers in UIs
- Configurations
- Distributed Processing of Large Data Sets

HADOOP

Click on the links below to view course outlines, pre-requisites, course length and course objectives.

Advanced Hadoop for Developers

Course Number:

N/A

Apache Hadoop is one of the most popular frameworks for processing Big Data on clusters of servers. This course delves into data management in HDFS, advanced Pig, Hive and HBase. These advanced programming techniques will be beneficial to experienced Hadoop developers.

Audience:

This class is designed specifically for developers.

Course Duration:

Prerequisites:

Students should be familiar with the Java programming language (most programming exercises are in Java) and comfortable in Linux environment (i.e., be able to navigate Linux command line, edit files using vi / nano). We recommend participating in the Hadoop for Developers course first or having equivalent knowledge and experience.

Course Objectives:

Course Outline:

Data Management in HDFS
- Various Data Formats (JSON / Avro / Parquet)
- Compression Schemes
- Data Masking
- Labs

Advanced Pig
- User-defined Functions
- Introduction to Pig Libraries (ElephantBird / Data-Fu)
- Loading Complex Structured Data using Pig
- Pig Tuning
- Labs

Advanced Hive
- User-defined Functions
- Compressed Tables
- Hive Performance Tuning
- Labs

Advanced HBase
- Advanced Schema Modelling
- Compression
- Bulk Data Ingest
- Wide-table / Tall-table comparison
- HBase and Pig
- HBase and Hive
- HBase Performance Tuning
- Labs

Hadoop for Executives and Managers

Course Number:

N/A

This interactive seminar with Q&A explores the brave new world of Big Data for executives, IT managers and investors who want to learn about Hadoop and Big Data. High-level topics covered include benefits, business opportunities, challenges, technologies and implementation stages.

Audience:

This seminar is designed especially for executives, IT managers and investors.

Course Duration:

4 days

Prerequisites:

No prerequisites are required for this seminar.

Course Objectives:

Review Big Data challenges, potential and benefits
Understand Hadoop’s role in Big Data and the roles of related technologies
Survey typical use cases in various industries
Recognize the phases of Big Data technologies adoption in the enterprise
Preview of the future of Hadoop and Big Data

Course Outline:

Big Data Potential and Benefits
- What is Big Data?
- Sources
- Quantities
- Challenges of Processing
- Technologies
- Strategies
- New Ways of Data Thinking
- Uses and Benefits
- Big Data Players, Their Approaches
- Untapped Potential

Hadoop and Big Data Technologies
- History of Hadoop Tools
- Architectural Concepts
- Who Is Using Hadoop?
- Hadoop Skills and Job Market
- Distributions and Support
- Eight Use Cases: How Hadoop Can Help You

Typical Use Cases in Various Industries/Sectors
- Note: This section may be tailored to fit a specific sector(s)
- Social Networking
- Marketing and Advertising
- Financial Risk / Insurance
- Healthcare
- Law Enforcement
- Sales and Market Analytics
- Fraud Detection
- Litigation
- Government and Politics
- Language, Meaning, Feeling
- Human Resources

Hadoop and Big Data Adoption
- Hadoop Benefits: Storage and Processing
- Hadoop Ecosystem
- Integration with Hadoop (BI Tools, Databases, Visualization, etc.)
- What is Possible: Batch Analysis, Real Time Analysis, Analytics
- Competing and Complementing Technologies (Oracle, IBM, Microsoft, etc.)
- Planning Adoption: Use Cases, Pilot Projects, Implementations
- Key Challenges of Adopting Hadoop and Big Data
- Production and Beyond
- The Role of Iterative Design and Development

The Future of Big Data and Hadoop
- Adoption Trends
- Hadoop Developments and Alternatives
- What’s Next for Big Data?

Combined Hadoop for Administrators and Developers

Course Number:

N/A

Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. During this five-day course, attendees will learn about the business benefits and use cases for Hadoop and its ecosystem; how to plan cluster deployment and growth; and how to install, maintain, monitor, troubleshoot and optimize Hadoop. They will also practice cluster bulk data load, become familiar with various Hadoop distribution and practice installing and managing Hadoop ecosystem tools. The section will conclude with discussion of securing cluster with Kerberos.

Audience:

This class is designed for Hadoop administrators and developers.

Course Duration:

5 days

Prerequisites:

Participants should be comfortable with basic Linux system administration and scripting skills. Knowledge of Hadoop and distributed computing is not required but will be introduced and explained in the course.

Course Objectives:

Course Outline:

Section One – Administration

Introduction
- Hadoop History, Concepts
- Ecosystem
- Distributions
- High-Level Architecture
- Hadoop Myths
- Hadoop Challenges (Hardware / Software)
- Planning and Installation
- Selecting Software, Hadoop Distributions
- Sizing The Cluster, Planning for Growth
- Selecting Hardware and Network
- Rack Topology
- Installation
- Multi-Tenancy
- Directory Structure, Logs
- Benchmarking

HDFS Operations
- Concepts (Horizontal Scaling, Replication, Data Locality, Rack Awareness)
- Nodes and Daemons (NameNode, Secondary NameNode, Ha Standby NameNode, Datanode)
- Health Monitoring
- Command-Line and Browser-Based AdministrationAdding Storage, Replacing Defective Drives

Data Ingestion
- Note: This Section is Also Good for Developers
- Flume for Logs and Other Data Ingestion into HDFS
- Sqoop for Importing from SQL Databases to HDFS and Exporting Back to SQL
- Hadoop Data Warehousing with Hive
- Copying Data Between Clusters (DistCp)
- Using S3 as Complementary to HDFS
- Data Ingestion Best Practices and Architectures
- MapReduce Operations and Administration
- Parallel Computing Before MapReduce
  - Compare HPC versus Hadoop Administration
- MapReduce Cluster Loads
- Nodes and Daemons (JobTracker, TaskTracker)
- MapReduce UI Walk Through
- MapReduce Configuration
- Job Config
- Optimizing MapReduce
- Fool-Proofing MR: What to Tell Your Programmers

Yarn: New Architecture and New Capabilities
- Note: This Section is Also Good for Developers
- Yarn Design Goals and Implementation Architecture
- New Actors: ResourceManager, NodeManager, Application Master
- Installing Yarn
- Job Scheduling Under Yarn

Advanced Topics
- Hardware Monitoring
- Cluster Monitoring
- Adding and Removing Servers, Upgrading Hadoop
- Backup, Recovery and Business Continuity Planning
- Oozie Job Workflows
- Hadoop High Availability (HA)
- Hadoop Federation
- Securing Your Cluster with Kerberos

Cloudera Distribution (CDH5) Track
- Note: This Section is Optional
- Cloudera Manager for Cluster Administration, Monitoring and Routine Tasks; Installation; Use
  - In This Track, All Exercises and Labs Are Performed Within the Cloudera Distribution Environment (CDH5)

Section Two – Development

Introduction to Hadoop
- Note: This Section is a Brief Overview of the Administration Section; Can Be Omitted if Needed
- Hadoop History, Concepts
- Ecosystem
- Distributions
- High-Level Architecture
- Hadoop Myths
- Hadoop Challenges
- Hardware / Software

HDFS
- Concepts (Horizontal Scaling, Replication, Data Locality, Rack Awareness)
- Architecture
- NameNode
- Secondary NameNode
- Data Node
- Communications / Heart-Beats
- Block Manager / Balancer
- Health Check / Safe Mode
- Read / Write Path
- File Systems Abstractions
- Data Integrity
- Future of HDFS: NameNode HA, Federation
- Lab Exercises

MapReduce
- MapReduce Concepts
- Daemons: JobTracker / TaskTracker
- Phases: Driver, Mapper, Shuffle/Sort, Reducer
- Counters
- Distributed Cache
- Combiners
- MapReduce Configuration
- MR Types and Formats
- Sorting
- Joins (Map Side and Reduce Side)
- Job Schedulers
- Unit Testing
- Thinking in MapReduce
- Future of MapReduce (Yarn)
- Lab Exercises

Pig
- Pig versus Java MapReduce
- Pig Job Flow
- Pig Latin Language
- Lab Exercises

Hive
- Hive Concepts
- Architecture
- Data Types
- Hive Versus SQL
- Lab Exercises

HBase
- Introduction
- Concepts
- Architecture
- HBase versus RDBMS
- Read Path / Write Path
- Schema Design
- Lab Exercises

Data Science with Hadoop

Course Number:

N/A

The course teaches data science principles and applications through lecture and hands-on labs. Students will learn how to select the right tool for the job and the strengths of each tool while gaining practical experience in creating working systems.

Audience:

This course is intended for architects, software developers, analysts and data scientists who need to understand how to apply data science to large datasets with Hadoop.

Course Duration:

3 days

Prerequisites:

Students must have basic computer skills, basic knowledge in statistics and a basic understanding of programming or scripting. Prior experience with Hadoop, Mahout, R or Python is helpful but not required.

Course Objectives:

Understand the foundations of data science
Understand the principles of machine learning
Learn about Hadoop and its interaction with data science
Learn to program in R and to use it for statistical analysis
Analyze texts with Python NLTK
Understand recommender systems
Compare implementing a recommender with R and Mahout

Course Outline:

Lab Content
- Set Up Development Environment
- Defining the Problem
- Programming in R
- Analyzing Data with R
- Creating the User/Item Matrix
- Recommender Lab with R
- Recommender Lab with Mahout

Hadoop for Administrators

Course Number:

N/A

Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. In this three-days (optionally four-day) course, attendees will learn about the business benefits and use cases for Hadoop and its ecosystem; how to plan cluster deployment and growth; and how to install, maintain, monitor, troubleshoot and optimize Hadoop. They will also practice cluster bulk data load, gain familiarity with various Hadoop distributions and practice installing and managing Hadoop ecosystem tools. The course finishes off with discussion of securing cluster with Kerberos.

This class can be extended to four days with the optional addition of Cloudera/Hortonworks specialization.

Audience:

This class is designed especially for Hadoop administrators.

Course Duration:

3 to 4 days

Prerequisites:

Participants should be comfortable with basic Linux system administration and possess basic scripting skills. Knowledge of Hadoop and distributed computing is not required as it will be introduced and explained in the course.

Course Objectives:

Course Outline:

Bulleted Introduction
- Hadoop History, Concepts
- Ecosystem
- Distributions
- High-Level Architecture
- Hadoop Myths
- Hadoop Challenges (Hardware / Software)

Planning and Installation
- Selecting Software, Hadoop Distributions
- Sizing The Cluster, Planning for Growth
- Selecting Hardware and Network
- Rack Topology
- Installation
- Multi-Tenancy
- Directory Structure, Logs
- Benchmarking

HDFS Operations
- Concepts (Horizontal Scaling, Replication, Data Locality, Rack Awareness)
- Nodes and Daemons (NameNode, Secondary NameNode, HA Standby NameNode, DataNode)
- Health Monitoring
- Command-Line and Browser-Based Administration
- Adding Storage, Replacing Defective Drives
- Data Ingestion
- Flume for Logs and Other Data Ingestion into HDFS
- Sqoop for Importing from SQL Databases to HDFS and Exporting Back to SQL
- Hadoop Data Warehousing with Hive
- Copying Data Between Clusters (distcp)
- Using S3 as Complementary to HDFS
- Data Ingestion Best Practices and Architectures

MapReduce Operations and Administration
- Parallel Computing Before MapReduce: Compare HPC vs Hadoop administration
- MapReduce Cluster Loads
- Nodes and Daemons (JobTracker, TaskTracker)
- MapReduce UI Walk Through
- MapReduce Configuration
- Job Config
- Optimizing MapReduce
- Fool-Proofing MR: What to Tell Your Programmers

YARN: New Architecture and New Capabilities
- YARN Design Goals and Implementation Architecture
- New Actors: ResourceManager, NodeManager, Application Master
- Installing YARN
- Job Scheduling Under YARN

Advanced Topics
- Hardware Monitoring
- Cluster Monitoring
- Adding and Removing Servers, Upgrading Hadoop
- Backup, Recovery and Business Continuity Planning
- Oozie Job Workflows
- Hadoop High Availability (HA)
- Hadoop Federation
- Securing Your Cluster with Kerberos

Optional Tracks
- Cloudera Manager for Cluster Administration, Monitoring and Routine Tasks; Installation; and Use
  - In This Track, All Exercises and Labs Are Performed Within the Cloudera Distribution Environment (CDH5)
- Ambari for Cluster Administration, Monitoring and Routine Tasks; Installation; and Use
  - In This Track, All Exercises And Labs Are Performed Within the Ambari Cluster Manager and Hortonworks Data Platform (HDP 2.0)

Hadoop for Business Analysts

Course Number:

N/A

Apache Hadoop is the most popular framework for processing Big Data. Hadoop provides rich and deep analytics capability, and it is making in-roads into the traditional business intelligence and analytics world. This course will introduce an analyst to core components of the Hadoop ecosystem and its analytics.

Audience:

This course is designed especially for business analysts.

Course Duration:

3 days

Prerequisites:

Participants should have a programming background with databases / SQL and a basic knowledge of Linux (e.g., be able to navigate Linux command line, editing files with vi / nano).

Course Objectives:

Course Outline:

Introduction to Hadoop
- Hadoop History, Concepts
- Ecosystem
- Distributions
- High-Level Architecture
- Hadoop Myths
- Hadoop Challenges
- Hardware / Software

HDFS Overview
- Concepts (Horizontal Scaling, Replication, Data Locality, Rack Awareness)
- Architecture (NameNode, Secondary NameNode, Data Node)
- Data Integrity
- Future of HDFS (NameNode HA, Federation)
- Lab Exercises

Map Reduce Overview
- MapReduce Concepts
- Daemons: JobTracker / TaskTracker
- Phases: Driver, Mapper, Shuffle/Sort, Reducer
- Thinking in MapReduce
- Future of MapReduce (YARN)
- Lab Exercises

Pig
- Pig Versus Java MapReduce
- Pig Latin Language
- User-Defined Functions
- Understanding Pig Job Flow
- Basic Data Analysis with Pig
- Complex Data Analysis with Pig
- Multi Datasets with Pig
- Advanced Concept
- Lab Exercises

Hive
- Hive Concepts
- Architecture
- Data Types
- Hive Data Management
- Hive Versus SQL
- Lab Exercises

BI Tools for Hadoop
- BI Tools and Hadoop
- Overview of Current BI Tools Landscape

Conclusion
- Choosing the Best Tool for the Job

OPENSTACK

Click on the links below to view course outlines, pre-requisites, course length and course objectives.

Introduction to OpenStack Administration

Course Number:

N/A

This hands-on introductory course will introduce OpenStack and teach participants to administer a private cloud. Other topics covered include installation, configuration and automation.

Audience:

This course is designed specifically for administrators responsible for a private cloud.

Course Duration:

5 days

Prerequisites:

Course Objectives:

Course Outline:

Introduction to OpenStack (One-Day)

OpenStack Introduction (Half-Day)
- OpenStack and Private Cloud Overview
- OpenStack Project History
- OpenStack Features

OpenStack Private Cloud (Half-Day)
- Driving IT Agility
- OpenStack Use Cases
- OpenStack Architecture
- OpenStack Demo

Installation and Configuration (One-Day)
- OpenStack Installation
- OpenStack Storage Setup
- OpenStack Compute Setup
- OpenStack Networking Setup

OpenStack Setup (Half-Day)
- OpenStack Security Model and Setup
- OpenStack Multi-Tenant Architecture
- Troubleshooting OpenStack

OpenStack Private Cloud Operations and Integration (One-Day)
- Monitoring OpenStack
- OpenStack Disaster Recovery
- Upgrading OpenStack Installation
- Growing and Scaling OpenStack Private Cloud

OpenStack Automation and Integration (Half-Day)
- OpenStack Automation
- OpenStack Orchestration
- OpenStack as a Private Cloud: Metering and Chargeback
- OpenStack in a Hybrid IT Data Center: Legacy Environment Integration

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