Apache Ambari: Efficiently Manage Hadoop Clusters Training Course

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.

Big data analytics involves the process of examining large amounts of varied data sets in order to uncover correlations, hidden patterns, and other useful insights.

The health industry has massive amounts of complex heterogeneous medical and clinical data. Applying big data analytics on health data presents huge potential in deriving insights for improving delivery of healthcare. However, the enormity of these datasets poses great challenges in analyses and practical applications to a clinical environment.

In this instructor-led, live training (remote), participants will learn how to perform big data analytics in health as they step through a series of hands-on live-lab exercises.

By the end of this training, participants will be able to:

• Install and configure big data analytics tools such as Hadoop MapReduce and Spark
• Understand the characteristics of medical data
• Apply big data techniques to deal with medical data
• Study big data systems and algorithms in the context of health applications

Audience

• Developers
• Data Scientists

Format of the Course

• Part lecture, part discussion, exercises and heavy hands-on practice.

Note

• To request a customized training for this course, please contact us to arrange.

Datameer is a business intelligence and analytics platform built on Hadoop. It allows end-users to access, explore and correlate large-scale, structured, semi-structured and unstructured data in an easy-to-use fashion.

In this instructor-led, live training, participants will learn how to use Datameer to overcome Hadoop's steep learning curve as they step through the setup and analysis of a series of big data sources.

By the end of this training, participants will be able to:

• Create, curate, and interactively explore an enterprise data lake
• Access business intelligence data warehouses, transactional databases and other analytic stores
• Use a spreadsheet user-interface to design end-to-end data processing pipelines
• Access pre-built functions to explore complex data relationships
• Use drag-and-drop wizards to visualize data and create dashboards
• Use tables, charts, graphs, and maps to analyze query results

Audience

• Data analysts

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

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

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.

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.

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%).

Hadoop is the most popular Big Data processing framework.

In this instructor-led training in Taiwan, participants will learn the core components of the Hadoop ecosystem and how these technologies can be used to solve large-scale problems. By learning these foundations, participants will  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

Audience:

This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand.

Hadoop is a popular Big Data processing framework. Python is a high-level programming language famous for its clear syntax and code readibility.

In this instructor-led, live training, participants will learn how to work with Hadoop, MapReduce, Pig, and Spark using Python as they step through multiple examples and use cases.

By the end of this training, participants will be able to:

• Understand the basic concepts behind Hadoop, MapReduce, Pig, and Spark
• Use Python with Hadoop Distributed File System (HDFS), MapReduce, Pig, and Spark
• Use Snakebite to programmatically access HDFS within Python
• Use mrjob to write MapReduce jobs in Python
• Write Spark programs with Python
• Extend the functionality of pig using Python UDFs
• Manage MapReduce jobs and Pig scripts using Luigi

Audience

• Developers
• IT Professionals

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This instructor-led, live training in Taiwan (online or onsite) is aimed at system administrators who wish to learn how to set up, deploy and manage Hadoop clusters within their organization.

By the end of this training, participants will be able to:

• Install and configure Apache Hadoop.
• Understand the four major components in the Hadoop ecoystem: HDFS, MapReduce, YARN, and Hadoop Common.
• Use Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.  
• Set up HDFS to operate as storage engine for on-premise Spark deployments.
• Set up Spark to access alternative storage solutions such as Amazon S3 and NoSQL database systems such as Redis, Elasticsearch, Couchbase, Aerospike, etc.
• Carry out administrative tasks such as provisioning, management, monitoring and securing an Apache Hadoop cluster.

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

In this instructor-led, live training in Taiwan (onsite or remote), participants will learn how to deploy and manage Apache NiFi in a live lab environment.

By the end of this training, participants will be able to:

• Install and configure Apachi NiFi.
• Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes.
• Automate dataflows.
• Enable streaming analytics.
• Apply various approaches for data ingestion.
• Transform Big Data and into business insights.

In this instructor-led, live training in Taiwan, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi.

By the end of this training, participants will be able to:

• Understand NiFi's architecture and dataflow concepts.
• Develop extensions using NiFi and third-party APIs.
• Custom develop their own Apache Nifi processor.
• Ingest and process real-time data from disparate and uncommon file formats and data sources.