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Hadoop Architecture & Administration Training for Big Data Solutions
Level: Intermediate
Rating:
4.72/5 Based on 36 Reviews
In this Hadoop Architecture and Administration big data training course, you gain the skills to install, configure, and manage the Apache Hadoop platform and its associated ecosystem, and build a Hadoop big data solution that satisfies your business and data science requirements. You will learn to install and build a Hadoop cluster capable of processing very large data sets, then configure and tune the Hadoop environment to ensure high throughput and availability.
Additionally, this course will teach attendees how to allocate, distribute and manage resources; monitor the Hadoop file system, job progress and overall cluster performance; as well as exchange information with relational databases.
Key Features of this Hadoop Administration for Big Data Training
- After-course instructor coaching benefit
- Learning Tree end-of-course exam included
- After-course computing sandbox included
You Will Learn How To
- Architect a Hadoop solution to satisfy your business requirements
- Install and build a Hadoop cluster capable of processing large data and executing data science jobs
- Configure and tune the Hadoop environment to ensure high throughput and availability
- Allocate, distribute, and manage resources
- Monitor the file system, job progress, and overall cluster performance
Certifications/Credits:
Choose the Training Solution That Best Fits Your Individual Needs or Organizational Goals
LIVE, INSTRUCTOR-LED
In Class & Live, Online Training
- 4-day instructor-led training course
- After-course instructor coaching benefit
- Learning Tree end-of-course exam included
- Earn 23 NASBA credits (live, in-class training only)
TRAINING AT YOUR SITE
Team Training
- Bring this or any training to your organization
- Full - scale program development
- Delivered when, where, and how you want it
- Blended learning models
- Tailored content
- Expert team coaching
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In Class & Live, Online Training
Time Zone Legend:
Note: This course runs for 4 Days *
*Events with the Partial Day Event clock icon run longer than normal but provide the convenience of half-day sessions.
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Jan 12 - 15 9:00 AM - 4:30 PM EST New York / Online (AnyWare)
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Mar 29 - Apr 1 9:00 AM - 4:30 PM EDT Ottawa / Online (AnyWare)
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Jul 20 - 23 9:00 AM - 4:30 PM EDT New York / Online (AnyWare)
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Guaranteed to Run
When you see the "Guaranteed to Run" icon next to a course event, you can rest assured that your course event — date, time — will run. Guaranteed.Partial Day Event
Learning Tree offers a flexible schedule program. If you cannot attend full day sessions, this option consists of four-hour sessions per day instead of the full-day session.Hadoop Administration Course Information
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Recommended Experience
- Knowledge of Linux at the level of:
- Course 144, Linux Administration & Support
- Knowledge of Java at the level of:
- Course 471, Java Programming Introduction
- Knowledge of Linux at the level of:
Hadoop Administration Course Outline
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Introduction to Data Storage and Processing
Installing the Hadoop Distributed File System (HDFS)
- Defining key design assumptions and architecture
- Configuring and setting up the file system
- Issuing commands from the console
- Reading and writing files
Setting the stage for MapReduce
- Reviewing the MapReduce approach
- Introducing the computing daemons
- Dissecting a MapReduce job
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Defining Hadoop Cluster Requirements
Planning the architecture
- Selecting appropriate hardware
- Designing a scalable cluster
Building the cluster
- Installing Hadoop daemons
- Optimizing the network architecture
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Configuring a Cluster
Preparing HDFS
- Setting basic configuration parameters
- Configuring block allocation, redundancy and replication
Deploying MapReduce
- Installing and setting up the MapReduce environment
- Delivering redundant load balancing via Rack Awareness
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Maximizing HDFS Robustness
Creating a fault–tolerant file system
- Isolating single points of failure
- Maintaining High Availability
- Triggering manual failover
- Automating failover with Zookeeper
Leveraging NameNode Federation
- Extending HDFS resources
- Managing the namespace volumes
Introducing YARN
- Critiquing the YARN architecture
- Identifying the new daemons
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Managing Resources and Cluster Health
Allocating resources
- Setting quotas to constrain HDFS utilization
- Prioritizing access to MapReduce using schedulers
Maintaining HDFS
- Starting and stopping Hadoop daemons
- Monitoring HDFS status
- Adding and removing data nodes
Administering MapReduce
- Managing MapReduce jobs
- Tracking progress with monitoring tools
- Commissioning and decommissioning compute nodes
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Maintaining a Cluster
Employing the standard built–in tools
- Managing and debugging processes using JVM metrics
- Performing Hadoop status checks
Tuning with supplementary tools
- Assessing performance with Ganglia
- Benchmarking to ensure continued performance
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Extending Hadoop
Simplifying information access
- Enabling SQL–like querying with Hive
- Installing Pig to create MapReduce jobs
Integrating additional elements of the ecosystem
- Imposing a tabular view on HDFS with HBase
- Leveraging memory with Spark
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Implementing Data Ingress and Egress
Facilitating generic input/output
- Moving bulk data into and out of Hadoop
- Transmitting HDFS data over HTTP with WebHDFS
Acquiring application–specific data
- Collecting multi–sourced log files with Flume
- Importing and exporting relational information with Sqoop
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Planning for Backup, Recovery and Security
- Coping with inevitable hardware failures
- Securing your Hadoop cluster
Team Training
Hadoop Administration Training FAQs
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Can I learn Hadoop Architecture and Administration online?
Yes! We know your busy work schedule may prevent you from getting to one of our classrooms which is why we offer convenient online training to meet your needs wherever you want, including online training.
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Where does MongoDB fit in my data science training?
A data science algorithm will ingest data from an appropriate storage technology like a relational database, MongoDB, Hadoop distributed file system into R or Python for data wrangling and model building. If the amount of data is large execution is performed in parallel using Spark. The results will often be visualized by the end user on dashboards.
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