Big Data Hadoop Administration Training - Plan Szkolenia

Hortonworks Data Platform (HDP) is an open-source Apache Hadoop support platform that provides a stable foundation for developing big data solutions on the Apache Hadoop ecosystem.

This instructor-led, live training (online or onsite) introduces Hortonworks Data Platform (HDP) and walks participants through the deployment of Spark + Hadoop solution.

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

• Use Hortonworks to reliably run Hadoop at a large scale.
• Unify Hadoop's security, governance, and operations capabilities with Spark's agile analytic workflows.
• Use Hortonworks to investigate, validate, certify and support each of the components in a Spark project.
• Process different types of data, including structured, unstructured, in-motion, and at-rest.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

Apache Ambari is an open-source management platform for provisioning, managing, monitoring and securing Apache Hadoop clusters.

In this instructor-led live training participants will learn the management tools and practices provided by Ambari to successfully manage Hadoop clusters.

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

• Set up a live Big Data cluster using Ambari
• Apply Ambari's advanced features and functionalities to various use cases
• Seamlessly add and remove nodes as needed
• Improve a Hadoop cluster's performance through tuning and tweaking

Audience

• DevOps
• System Administrators
• DBAs
• Hadoop testing professionals

Format of the course

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

Cloudera Impala is an open source massively parallel processing (MPP) SQL query engine for Apache Hadoop clusters.

Impala enables users to issue low-latency SQL queries to data stored in Hadoop Distributed File System and Apache Hbase without requiring data movement or transformation.

Audience

This course is aimed at analysts and data scientists performing analysis on data stored in Hadoop via Business Intelligence or SQL tools.

After this course delegates will be able to

• Extract meaningful information from Hadoop clusters with Impala.
• Write specific programs to facilitate Business Intelligence in Impala SQL Dialect.
• Troubleshoot Impala.

This course covers how to use Hive SQL language (AKA: Hive HQL, SQL on Hive, HiveQL) for people who extract data from Hive

Głównym celem szkolenia jest zdobycie wiedzy z administracji systemem Apache Hadoop w środowiskach MapReduce oraz YARN na poziomie zaawansowanym. Tematyka szkolenia dotyczy w głównej mierze architektury systemu Hadoop, a w szczególności systemu plików HDFS oraz modeli programistycznych MapReduce i YARN oraz zagadnień związanych z planowaniem, instalacją, konfiguracją, administracją, zarządzaniem i monitorowaniem klastra systemu Hadoop. Pozostałe zagadnienia związane z tematyką BigData takie jak HBase, Cassandra, Impala, Pig, Hiver oraz Sqoop są również omówione, choć pobieżnie. Kurs przeznaczony jest w głównej mierze do specjalistów z branży IT, którzy chcą przygotować się i zdać egzamin CCAH (Cloudera Certified administrator for Apache Hadoop).

Audience:

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

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%).

 

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

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.

Big Data Hadoop to otwarta, rozproszona i skalowalna platforma, stworzona do przechowywania, przetwarzania i analizy dużych zbiorów danych. Hadoop umożliwia przechowywanie dużych ilości danych na klastrze komputerów, dzięki czemu można obsługiwać petabajty danych. Framework Hadoop zawiera system plików Hadoop Distributed File System (HDFS) oraz framework do przetwarzania rozproszonego MapReduce. Pozwala to na równoległe przetwarzanie danych na wielu maszynach w klastrze. Platforma Hadoop jest łatwa w skalowaniu. Można ją rozbudować poprzez dodanie nowych węzłów do klastra, co zwiększa przepustowość i przetwarzanie danych. Składa się z różnych modułów, takich jak HDFS, MapReduce, YARN (Yet Another Resource Negotiator) oraz dodatkowych narzędzi i bibliotek, które mogą być używane w zależności od potrzeb projektu. Hadoop umożliwia pracę z różnorodnymi typami danych, w tym strukturyzowanymi, półstrukturyzowanymi i nieustrukturyzowanymi. Jest używany do analizy dużych zbiorów danych, tworzenia raportów, analizy trendów, przewidywania oraz uczenia maszynowego. Hadoop posiada bogaty ekosystem narzędzi i frameworków, takich jak Apache Hive, Apache Pig, Apache Spark, Apache HBase, które wspierają różne aspekty przetwarzania i analizy danych. Zapewnia mechanizmy bezpieczeństwa, kontroli dostępu i zarządzania użytkownikami, aby chronić dane przechowywane i przetwarzane w klastrze Hadoop. Big Data Hadoop jest popularnym narzędziem do przetwarzania i analizy dużych zbiorów danych, stosowanym w różnych branżach, w tym w analizie rynkowej, medycynie, finansach, przemyśle i wielu innych, gdzie konieczne jest zarządzanie i analiza dużej ilości danych.

Hadoop is the most popular Big Data processing framework.

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 in, 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

Format of the course

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

Apache Samza is an open-source near-realtime, asynchronous computational framework for stream processing.  It uses Apache Kafka for messaging, and Apache Hadoop YARN for fault tolerance, processor isolation, security, and resource management.

This instructor-led, live training introduces the principles behind messaging systems and distributed stream processing, while walking participants through the creation of a sample Samza-based project and job execution.

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

• Use Samza to simplify the code needed to produce and consume messages.
• Decouple the handling of messages from an application.
• Use Samza to implement near-realtime asynchronous computation.
• Use stream processing to provide a higher level of abstraction over messaging systems.

Audience

• Developers

Format of the course

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

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