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mdlmrah |
Model MapReduce and Apache Hadoop |
14 hours |
The course is intended for IT specialist that works with the distributed processing of large data sets across clusters of computers.
Data Mining and Business Intelligence
- Introduction
- Area of application
- Capabilities
- Basics of data exploration
Big data
- What does Big data stand for?
- Big data and Data mining
MapReduce
- Model basics
- Example application
- Stats
- Cluster model
Hadoop
- What is Hadoop
- Installation
- Configuration
- Cluster settings
- Architecture and configuration of Hadoop Distributed File System
- Console tools
- DistCp tool
- MapReduce and Hadoop
- Streaming
- Administration and configuration of Hadoop On Demand
- Alternatives
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hadoopforprojectmgrs |
Hadoop for Project Managers |
14 hours |
As more and more software and IT projects migrate from local processing and data management to distributed processing and big data storage, Project Managers are finding the need to upgrade their knowledge and skills to grasp the concepts and practices relevant to Big Data projects and opportunities.
This course introduces Project Managers to the most popular Big Data processing framework: Hadoop.
In this instructor-led training, participants will learn the core components of the Hadoop ecosystem and how these technologies can used to solve large-scale problems. In learning these foundations, participants will also improve their ability to communicate with the developers and implementers of these systems as well as the data scientists and analysts that many IT projects involve.
Audience
- Project Managers wishing to implement Hadoop into their existing development or IT infrastructure
- Project Managers needing to communicate with cross-functional teams that include big data engineers, data scientists and business analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Introduction
Why and how project teams adopt Hadoop.
How it all started
The Project Manager's role in Hadoop projects
Understanding Hadoop's architecture and key concepts
HDFS
MapReduce
Other pieces of the Hadoop ecosystem
What constitutes Big Data?
Different approaches to storing Big Data
HDFS (Hadoop Distributed File System) as the foundation
How Big Data is processed
The power of distributed processing
Processing data with Map Reduce
How data is picked apart step by step
The role of clustering in large-scale distributed processing
Architectural overview
Clustering approaches
Clustering your data and processes with YARN
The role of non-relational database in Big Data storage
Working with Hadoop's non-relational database: HBase
Data warehousing architectural overview
Managing your data warehouse with Hive
Running Hadoop from shell-scripts
Working with Hadoop Streaming
Other Hadoop tools and utilities
Getting started on a Hadoop project
Demystifying complexity
Migrating an existing project to Hadoop
Infrastructure considerations
Scaling beyond your allocated resources
Hadoop project stakeholders and their toolkits
Developers, data scientists, business analysts and project managers
Hadoop as a foundation for new technologies and approaches
Closing remarks
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apacheh |
Administrator Training for Apache Hadoop |
35 hours |
Audience:
The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment
Goal:
Deep knowledge on Hadoop cluster administration.
1: HDFS (17%)
- Describe the function of HDFS Daemons
- Describe the normal operation of an Apache Hadoop cluster, both in data storage and in data processing.
- Identify current features of computing systems that motivate a system like Apache Hadoop.
- Classify major goals of HDFS Design
- Given a scenario, identify appropriate use case for HDFS Federation
- Identify components and daemon of an HDFS HA-Quorum cluster
- Analyze the role of HDFS security (Kerberos)
- Determine the best data serialization choice for a given scenario
- Describe file read and write paths
- Identify the commands to manipulate files in the Hadoop File System Shell
2: YARN and MapReduce version 2 (MRv2) (17%)
- Understand how upgrading a cluster from Hadoop 1 to Hadoop 2 affects cluster settings
- Understand how to deploy MapReduce v2 (MRv2 / YARN), including all YARN daemons
- Understand basic design strategy for MapReduce v2 (MRv2)
- Determine how YARN handles resource allocations
- Identify the workflow of MapReduce job running on YARN
- Determine which files you must change and how in order to migrate a cluster from MapReduce version 1 (MRv1) to MapReduce version 2 (MRv2) running on YARN.
3: Hadoop Cluster Planning (16%)
- Principal points to consider in choosing the hardware and operating systems to host an Apache Hadoop cluster.
- Analyze the choices in selecting an OS
- Understand kernel tuning and disk swapping
- Given a scenario and workload pattern, identify a hardware configuration appropriate to the scenario
- Given a scenario, determine the ecosystem components your cluster needs to run in order to fulfill the SLA
- Cluster sizing: given a scenario and frequency of execution, identify the specifics for the workload, including CPU, memory, storage, disk I/O
- Disk Sizing and Configuration, including JBOD versus RAID, SANs, virtualization, and disk sizing requirements in a cluster
- Network Topologies: understand network usage in Hadoop (for both HDFS and MapReduce) and propose or identify key network design components for a given scenario
4: Hadoop Cluster Installation and Administration (25%)
- Given a scenario, identify how the cluster will handle disk and machine failures
- Analyze a logging configuration and logging configuration file format
- Understand the basics of Hadoop metrics and cluster health monitoring
- Identify the function and purpose of available tools for cluster monitoring
- Be able to install all the ecosystem components in CDH 5, including (but not limited to): Impala, Flume, Oozie, Hue, Manager, Sqoop, Hive, and Pig
- Identify the function and purpose of available tools for managing the Apache Hadoop file system
5: Resource Management (10%)
- Understand the overall design goals of each of Hadoop schedulers
- Given a scenario, determine how the FIFO Scheduler allocates cluster resources
- Given a scenario, determine how the Fair Scheduler allocates cluster resources under YARN
- Given a scenario, determine how the Capacity Scheduler allocates cluster resources
6: Monitoring and Logging (15%)
- Understand the functions and features of Hadoop’s metric collection abilities
- Analyze the NameNode and JobTracker Web UIs
- Understand how to monitor cluster Daemons
- Identify and monitor CPU usage on master nodes
- Describe how to monitor swap and memory allocation on all nodes
- Identify how to view and manage Hadoop’s log files
- Interpret a log file
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hadoopadm |
Hadoop Administration |
21 hours |
The course is dedicated to IT specialists that are looking for a solution to store and process large data sets in distributed system environment
Course goal:
Getting knowledge regarding Hadoop cluster administration
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Introduction to Cloud Computing and Big Data solutions
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Apache Hadoop evolution: HDFS, MapReduce, YARN
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Installation and configuration of Hadoop in Pseudo-distributed mode
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Running MapReduce jobs on Hadoop cluster
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Hadoop cluster planning, installation and configuration
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Hadoop ecosystem: Pig, Hive, Sqoop, HBase
- Big Data future: Impala, Cassandra
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68736 |
Hadoop for Developers (2 days) |
14 hours |
Introduction
- What is Hadoop?
- What does it do?
- How does it do it?
The Motivation for Hadoop
- Problems with Traditional Large-Scale Systems
- Introducing Hadoop
- Hadoopable Problems
Hadoop: Basic Concepts and HDFS
- The Hadoop Project and Hadoop Components
- The Hadoop Distributed File System
Introduction to MapReduce
- MapReduce Overview
- Example: WordCount
- Mappers
- Reducers
Hadoop Clusters and the Hadoop Ecosystem
- Hadoop Cluster Overview
- Hadoop Jobs and Tasks
- Other Hadoop Ecosystem Components
Writing a MapReduce Program in Java
- Basic MapReduce API Concepts
- Writing MapReduce Drivers, Mappers, and Reducers in Java
- Speeding Up Hadoop Development by Using Eclipse
- Differences Between the Old and New MapReduce APIs
Writing a MapReduce Program Using Streaming
- Writing Mappers and Reducers with the Streaming API
Unit Testing MapReduce Programs
- Unit Testing
- The JUnit and MRUnit Testing Frameworks
- Writing Unit Tests with MRUnit
- Running Unit Tests
Delving Deeper into the Hadoop API
- Using the ToolRunner Class
- Setting Up and Tearing Down Mappers and Reducers
- Decreasing the Amount of Intermediate Data with Combiners
- Accessing HDFS Programmatically
- Using The Distributed Cache
- Using the Hadoop API’s Library of Mappers, Reducers, and Partitioners
Practical Development Tips and Techniques
- Strategies for Debugging MapReduce Code
- Testing MapReduce Code Locally by Using LocalJobRunner
- Writing and Viewing Log Files
- Retrieving Job Information with Counters
- Reusing Objects
- Creating Map-Only MapReduce Jobs
Partitioners and Reducers
- How Partitioners and Reducers Work Together
- Determining the Optimal Number of Reducers for a Job
- Writing Customer Partitioners
Data Input and Output
- Creating Custom Writable and Writable-Comparable Implementations
- Saving Binary Data Using SequenceFile and Avro Data Files
- Issues to Consider When Using File Compression
- Implementing Custom InputFormats and OutputFormats
- Common MapReduce Algorithms
- Sorting and Searching Large Data Sets
- Indexing Data
- Computing Term Frequency — Inverse Document Frequency
- Calculating Word Co-Occurrence
- Performing Secondary Sort
- Joining Data Sets in MapReduce Jobs
- Writing a Map-Side Join
- Writing a Reduce-Side Join
- Integrating Hadoop into the Enterprise Workflow
- Integrating Hadoop into an Existing Enterprise
- Loading Data from an RDBMS into HDFS by Using Sqoop
- Managing Real-Time Data Using Flume
- Accessing HDFS from Legacy Systems with FuseDFS and HttpFS
- An Introduction to Hive, Imapala, and Pig
- The Motivation for Hive, Impala, and Pig
- Hive Overview
- Impala Overview
- Pig Overview
- Choosing Between Hive, Impala, and Pig
- An Introduction to Oozie
- Introduction to Oozie
- Creating Oozie Workflows
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hadoopmapr |
Hadoop Administration on MapR |
28 hours |
Audience:
This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand.
Big Data Overview:
- What is Big Data
- Why Big Data is gaining popularity
- Big Data Case Studies
- Big Data Characteristics
- Solutions to work on Big Data.
Hadoop & Its components:
- What is Hadoop and what are its components.
- Hadoop Architecture and its characteristics of Data it can handle /Process.
- Brief on Hadoop History, companies using it and why they have started using it.
- Hadoop Frame work & its components- explained in detail.
- What is HDFS and Reads -Writes to Hadoop Distributed File System.
- How to Setup Hadoop Cluster in different modes- Stand- alone/Pseudo/Multi Node cluster.
(This includes setting up a Hadoop cluster in VirtualBox/KVM/VMware, Network configurations that need to be carefully looked into, running Hadoop Daemons and testing the cluster).
- What is Map Reduce frame work and how it works.
- Running Map Reduce jobs on Hadoop cluster.
- Understanding Replication , Mirroring and Rack awareness in context of Hadoop clusters.
Hadoop Cluster Planning:
- How to plan your hadoop cluster.
- Understanding hardware-software to plan your hadoop cluster.
- Understanding workloads and planning cluster to avoid failures and perform optimum.
What is MapR and why MapR :
- Overview of MapR and its architecture.
- Understanding & working of MapR Control System, MapR Volumes , snapshots & Mirrors.
- Planning a cluster in context of MapR.
- Comparison of MapR with other distributions and Apache Hadoop.
- MapR installation and cluster deployment.
Cluster Setup & Administration:
- Managing services, nodes ,snapshots, mirror volumes and remote clusters.
- Understanding and managing Nodes.
- Understanding of Hadoop components, Installing Hadoop components alongside MapR Services.
- Accessing Data on cluster including via NFS Managing services & nodes.
- Managing data by using volumes, managing users and groups, managing & assigning roles to nodes, commissioning decommissioning of nodes, cluster administration and performance monitoring, configuring/ analyzing and monitoring metrics to monitor performance, configuring and administering MapR security.
- Understanding and working with M7- Native storage for MapR tables.
- Cluster configuration and tuning for optimum performance.
Cluster upgrade and integration with other setups:
- Upgrading software version of MapR and types of upgrade.
- Configuring Mapr cluster to access HDFS cluster.
- Setting up MapR cluster on Amazon Elastic Mapreduce.
All the above topics include Demonstrations and practice sessions for learners to have hands on experience of the technology.
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hadoopdeva |
Advanced Hadoop for Developers |
21 hours |
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: developers
Duration: three days
Format: lectures (50%) and hands-on labs (50%).
Section 1: Data Management in HDFS
- Various Data Formats (JSON / Avro / Parquet)
- Compression Schemes
- Data Masking
- Labs : Analyzing different data formats; enabling compression
Section 2: Advanced Pig
- User-defined Functions
- Introduction to Pig Libraries (ElephantBird / Data-Fu)
- Loading Complex Structured Data using Pig
- Pig Tuning
- Labs : advanced pig scripting, parsing complex data types
Section 3 : Advanced Hive
- User-defined Functions
- Compressed Tables
- Hive Performance Tuning
- Labs : creating compressed tables, evaluating table formats and configuration
Section 4 : Advanced HBase
- Advanced Schema Modelling
- Compression
- Bulk Data Ingest
- Wide-table / Tall-table comparison
- HBase and Pig
- HBase and Hive
- HBase Performance Tuning
- Labs : tuning HBase; accessing HBase data from Pig & Hive; Using Phoenix for data modeling
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hadoopdev |
Hadoop for Developers (4 days) |
28 hours |
Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. This course will introduce a developer to various components (HDFS, MapReduce, Pig, Hive and HBase) Hadoop ecosystem.
Section 1: Introduction to Hadoop
- hadoop history, concepts
- eco system
- distributions
- high level architecture
- hadoop myths
- hadoop challenges
- hardware / software
- lab : first look at Hadoop
Section 2: HDFS
- Design and architecture
- concepts (horizontal scaling, replication, data locality, rack awareness)
- Daemons : Namenode, Secondary namenode, Data node
- communications / heart-beats
- data integrity
- read / write path
- Namenode High Availability (HA), Federation
- labs : Interacting with HDFS
Section 3 : Map Reduce
- concepts and architecture
- daemons (MRV1) : jobtracker / tasktracker
- phases : driver, mapper, shuffle/sort, reducer
- Map Reduce Version 1 and Version 2 (YARN)
- Internals of Map Reduce
- Introduction to Java Map Reduce program
- labs : Running a sample MapReduce program
Section 4 : Pig
- pig vs java map reduce
- pig job flow
- pig latin language
- ETL with Pig
- Transformations & Joins
- User defined functions (UDF)
- labs : writing Pig scripts to analyze data
Section 5: Hive
- architecture and design
- data types
- SQL support in Hive
- Creating Hive tables and querying
- partitions
- joins
- text processing
- labs : various labs on processing data with Hive
Section 6: HBase
- concepts and architecture
- hbase vs RDBMS vs cassandra
- HBase Java API
- Time series data on HBase
- schema design
- labs : Interacting with HBase using shell; programming in HBase Java API ; Schema design exercise
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hadoopba |
Hadoop for Business Analysts |
21 hours |
Apache Hadoop is the most popular framework for processing Big Data. Hadoop provides rich and deep analytics capability, and it is making in-roads in to tradional BI analytics world. This course will introduce an analyst to the core components of Hadoop eco system and its analytics
Audience
Business Analysts
Duration
three days
Format
Lectures and hands on labs.
- Section 1: Introduction to Hadoop
- hadoop history, concepts
- eco system
- distributions
- high level architecture
- hadoop myths
- hadoop challenges
- hardware / software
- Labs : first look at Hadoop
- Section 2: HDFS Overview
- concepts (horizontal scaling, replication, data locality, rack awareness)
- architecture (Namenode, Secondary namenode, Data node)
- data integrity
- future of HDFS : Namenode HA, Federation
- labs : Interacting with HDFS
- Section 3 : Map Reduce Overview
- mapreduce concepts
- daemons : jobtracker / tasktracker
- phases : driver, mapper, shuffle/sort, reducer
- Thinking in map reduce
- Future of mapreduce (yarn)
- labs : Running a Map Reduce program
- Section 4 : Pig
- pig vs java map reduce
- 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 concepts
- lab : writing pig scripts to analyze / transform data
- Section 5: Hive
- hive concepts
- architecture
- SQL support in Hive
- data types
- table creation and queries
- Hive data management
- partitions & joins
- text analytics
- labs (multiple) : creating Hive tables and running queries, joins , using partitions, using text analytics functions
- Section 6: BI Tools for Hadoop
- BI tools and Hadoop
- Overview of current BI tools landscape
- Choosing the best tool for the job
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hadoopadm1 |
Hadoop For Administrators |
21 hours |
Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. In this three (optionally, four) days course, attendees will learn about the business benefits and use cases for Hadoop and its ecosystem, how to plan cluster deployment and growth, how to install, maintain, monitor, troubleshoot and optimize Hadoop. They will also practice cluster bulk data load, get familiar with various Hadoop distributions, and practice installing and managing Hadoop ecosystem tools. The course finishes off with discussion of securing cluster with Kerberos.
“…The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organized”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising
Audience
Hadoop administrators
Format
Lectures and hands-on labs, approximate balance 60% lectures, 40% labs.
- Introduction
- Hadoop history, concepts
- Ecosystem
- Distributions
- High level architecture
- Hadoop myths
- Hadoop challenges (hardware / software)
- Labs: discuss your Big Data projects and problems
- 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
- Labs: cluster install, run performance benchmarks
- 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
- Labs: getting familiar with HDFS command lines
- Data ingestion
- Flume for logs and other data ingestion into HDFS
- Sqoop for importing from SQL databases to HDFS, as well as 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
- Labs: setting up and using Flume, the same for Sqoop
- 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
- Labs: running MapReduce examples
- YARN: new architecture and new capabilities
- YARN design goals and implementation architecture
- New actors: ResourceManager, NodeManager, Application Master
- Installing YARN
- Job scheduling under YARN
- Labs: investigate job scheduling
- 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
- Labs: set up monitoring
- Optional tracks
- 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)
- Ambari for cluster administration, monitoring, and routine tasks; installation, use. In this track, all exercises and labs are performed within the Ambari cluster manager and Hortonworks Data Platform (HDP 2.0)
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hbasedev |
HBase for Developers |
21 hours |
This course introduces HBase – a NoSQL store on top of Hadoop. The course is intended for developers who will be using HBase to develop applications, and administrators who will manage HBase clusters.
We will walk a developer through HBase architecture and data modelling and application development on HBase. It will also discuss using MapReduce with HBase, and some administration topics, related to performance optimization. The course is very hands-on with lots of lab exercises.
Duration : 3 days
Audience : Developers & Administrators
- Section 1: Introduction to Big Data & NoSQL
- Big Data ecosystem
- NoSQL overview
- CAP theorem
- When is NoSQL appropriate
- Columnar storage
- HBase and NoSQL
- Section 2 : HBase Intro
- Concepts and Design
- Architecture (HMaster and Region Server)
- Data integrity
- HBase ecosystem
- Lab : Exploring HBase
- Section 3 : HBase Data model
- Namespaces, Tables and Regions
- Rows, columns, column families, versions
- HBase Shell and Admin commands
- Lab : HBase Shell
- Section 3 : Accessing HBase using Java API
- Introduction to Java API
- Read / Write path
- Time Series data
- Scans
- Map Reduce
- Filters
- Counters
- Co-processors
- Labs (multiple) : Using HBase Java API to implement time series , Map Reduce, Filters and counters.
- Section 4 : HBase schema Design : Group session
- students are presented with real world use cases
- students work in groups to come up with design solutions
- discuss / critique and learn from multiple designs
- Labs : implement a scenario in HBase
- Section 5 : HBase Internals
- Understanding HBase under the hood
- Memfile / HFile / WAL
- HDFS storage
- Compactions
- Splits
- Bloom Filters
- Caches
- Diagnostics
- Section 6 : HBase installation and configuration
- hardware selection
- install methods
- common configurations
- Lab : installing HBase
- Section 7 : HBase eco-system
- developing applications using HBase
- interacting with other Hadoop stack (MapReduce, Pig, Hive)
- frameworks around HBase
- advanced concepts (co-processors)
- Labs : writing HBase applications
- Section 8 : Monitoring And Best Practices
- monitoring tools and practices
- optimizing HBase
- HBase in the cloud
- real world use cases of HBase
- Labs : checking HBase vitals
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hivehiveql |
Data Analysis with Hive/HiveQL |
7 hours |
This course covers how to use Hive SQL language (AKA: Hive HQL, SQL on Hive, HiveQL) for people who extract data from Hive
Hive Overview
- Architecture and design
- Aata types
- SQL support in Hive
- Creating Hive tables and querying
- Partitions
- Joins
- Text processing
- labs : various labs on processing data with Hive
DQL (Data Query Language) in Detail
- SELECT clause
- Column aliases
- Table aliases
- Date types and Date functions
- Group function
- Table joins
- JOIN clause
- UNION operator
- Nested queries
- Correlated subqueries
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IntroToAvro |
Apache Avro: Data serialization for distributed applications |
14 hours |
This course is intended for
Format of the course
- Lectures, hands-on practice, small tests along the way to gauge understanding
Principles of distributed computing
Principles of data serialization
- How data object is passed over the network
- Serialization of objects
- Serialization approaches
- Thrift
- Protocol Buffers
- Apache Avro
- data structure
- size, speed, format characteristics
- persistent data storage
- integration with dynamic languages
- dynamic typing
- schemas
- untagged data
- change management
Data serialization and distributed computing
- Avro as a subproject of Hadoop
- Java serialization
- Hadoop serialization
- Avro serialization
Using Avro with
- Hive (AvroSerDe)
- Pig (AvroStorage)
Porting Existing RPC Frameworks
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druid |
Druid: Build a fast, real-time data analysis system |
21 hours |
Druid is an open-source, column-oriented, distributed data store written in Java. It was designed to quickly ingest massive quantities of event data and execute low-latency OLAP queries on that data. Druid is commonly used in business intelligence applications to analyze high volumes of real-time and historical data. It is also well suited for powering fast, interactive, analytic dashboards for end-users. Druid is used by companies such as Alibaba, Airbnb, Cisco, eBay, Netflix, Paypal, and Yahoo.
In this course we explore some of the limitations of data warehouse solutions and discuss how Druid can compliment those technologies to form a flexible and scalable streaming analytics stack. We walk through many examples, offering participants the chance to implement and test Druid-based solutions in a lab environment.
Audience
Application developers
Software engineers
Technical consultants
DevOps professionals
Architecture engineers
Format of the course
Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
Introduction
Installing and starting Druid
Druid architecture and design
Real-time ingestion of event data
Sharding and indexing
Loading data
Querying data
Visualizing data
Running a distributed cluster
Druid + Apache Hive
Druid + Apache Kafka
Druid + others
Troubleshooting
Administrative tasks
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voldemort |
Voldemort: Setting up a key-value distributed data store |
14 hours |
Voldemort is an open-source distributed data store that is designed as a key-value store. It is used at LinkedIn by numerous critical services powering a large portion of the site.
This course will introduce the architecture and capabilities of Voldomort and walk participants through the setup and application of a key-value distributed data store.
Audience
Software developers
System administrators
DevOps engineers
Format of the course
Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
Introduction
Understanding distributed key-value storage systems
Voldomort data model and architecture
Downloading and configuration
Command line operations
Clients and servers
Working with Hadoop
Configuring build and push jobs
Rebalancing a Voldemort instance
Serving Large-scale Batch Computed Data
Using the Admin Tool
Performance tuning
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BigData_ |
A practical introduction to Data Analysis and Big Data |
28 hours |
Participants who complete this training will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools.
Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.
The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.
Audience
- Developers / programmers
- IT consultants
Format of the course
Part lecture, part discussion, heavy hands-on practice and implementation, occasional quizing to measure progress.
Introduction to Data Analysis and Big Data
- What makes Big Data "big"?
- Velocity, Volume, Variety, Veracity (VVVV)
- Limits to traditional Data Processing
- Distributed Processing
- Statistical Analysis
- Types of Machine Learning Analysis
- Data Visualization
Languages used for Data Analysis
- R language (crash course)
- Why R for Data Analysis?
- Data manipulation, calculation and graphical display
- Python (crash course)
- Why Python for Data Analysis?
- Manipulating, processing, cleaning, and crunching data
Approaches to Data Analysis
- Statistical Analysis
- Time Series analysis
- Forecasting with Correlation and Regression models
- Inferential Statistics (estimating)
- Descriptive Statistics in Big Data sets (e.g. calculating mean)
- Machine Learning
- Supervised vs unsupervised learning
- Classification and clustering
- Estimating cost of specific methods
- Filtering
- Natural Language Processing
- Processing text
- Understaing meaning of the text
- Automatic text generation
- Sentiment/Topic Analysis
- Computer Vision
- Acquiring, processing, analyzing, and understanding images
- Reconstructing, interpreting and understanding 3D scenes
- Using image data to make decisions
Big Data infrastructure
- Data Storage
- Relational databases (SQL)
- Non-relational databases (NoSQL)
- Understanding the nuances
- Hierarchical databases
- Object-oriented databases
- Document-oriented databases
- Graph-oriented databases
- Other
- Distributed Processing
- Hadoop
- HDFS as a distributed filesystem
- MapReduce for distributed processing
- Spark
- All-in-one in-memory cluster computing framework for large-scale data processing
- Structured streaming
- Spark SQL
- Machine Learning libraries: MLlib
- Graph processing with GraphX
- Search Engines
- Scalability
- Public cloud
- AWS, Google, Aliyun, etc.
- Private cloud
- OpenStack, Cloud Foundry, etc.
- Auto-scalability
- Choosing right solution for the problem
- The future of Big Data
- Closing remarks
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ApHadm1 |
Apache Hadoop: Manipulation and Transformation of Data Performance |
21 hours |
This course is intended for developers, architects, data scientists or any profile that requires access to data either intensively or on a regular basis.
The major focus of the course is data manipulation and transformation.
Among the tools in the Hadoop ecosystem this course includes the use of Pig and Hive both of which are heavily used for data transformation and manipulation.
This training also addresses performance metrics and performance optimisation.
The course is entirely hands on and is punctuated by presentations of the theoretical aspects.
1.1Hadoop Concepts
1.1.1HDFS
- The Design of HDFS
- Command line interface
- Hadoop File System
1.1.2Clusters
- Anatomy of a cluster
- Mater Node / Slave node
- Name Node / Data Node
1.2Data Manipulation
1.2.1MapReduce detailed
- Map phase
- Reduce phase
- Shuffle
1.2.2Analytics with Map Reduce
- Group-By with MapReduce
- Frequency distributions and sorting with MapReduce
- Plotting results (GNU Plot)
- Histograms with MapReduce
- Scatter plots with MapReduce
- Parsing complex datasets
- Counting with MapReduce and Combiners
- Build reports
1.2.3Data Cleansing
- Document Cleaning
- Fuzzy string search
- Record linkage / data deduplication
- Transform and sort event dates
- Validate source reliability
- Trim Outliers
1.2.4Extracting and Transforming Data
- Transforming logs
- Using Apache Pig to filter
- Using Apache Pig to sort
- Using Apache Pig to sessionize
1.2.5Advanced Joins
- Joining data in the Mapper using MapReduce
- Joining data using Apache Pig replicated join
- Joining sorted data using Apache Pig merge join
- Joining skewed data using Apache Pig skewed join
- Using a map-side join in Apache Hive
- Using optimized full outer joins in Apache Hive
- Joining data using an external key value store
1.3Performance Diagnosis and Optimization Techniques
- Map
- Investigating spikes in input data
- Identifying map-side data skew problems
- Map task throughput
- Small files
- Unsplittable files
- Reduce
- Too few or too many reducers
- Reduce-side data skew problems
- Reduce tasks throughput
- Slow shuffle and sort
- Competing jobs and scheduler throttling
- Stack dumps & unoptimized code
- Hardware failures
- CPU contention
- Tasks
- Extracting and visualizing task execution times
- Profiling your map and reduce tasks
- Avoid the reducer
- Filter and project
- Using the combiner
- Fast sorting with comparators
- Collecting skewed data
- Reduce skew mitigation
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