Hadoop Počítačové Kurzy

Python je měřitelný, flexibilní a široce používaný programovací jazyk pro datovou vědu a strojové učení. Spark je datový procesor používaný při vyhledávání, analýze a transformaci velkých dat, zatímco Hadoop je rámec knihovny softwaru pro ukládání a zpracování velkých dat. Tento výcvik vedený instruktorem (online nebo on-site) je zaměřen na vývojáře, kteří chtějí používat a integrovat Spark, Hadoop, a Python k zpracování, analýze a transformaci velkých a složitých datových souborů. Po ukončení tohoto tréninku budou účastníci schopni:

Vytvořte nezbytné prostředí k zahájení zpracování velkých dat pomocí Spark, Hadoop, a Python. Pochopte vlastnosti, klíčové složky a architekturu Spark a Hadoop. Naučte se, jak integrovat Spark, Hadoop, a Python pro zpracování velkých dat. Prozkoumejte nástroje v ekosystému Spark (Spark MlLib, Spark Streaming, Kafka, Sqoop, Kafka a Flume). Vytvořte společné systémy doporučení pro filtrování podobné Netflix, YouTube, Amazon, Spotify a Google. Použijte Apache Mahout pro skalování algoritmů strojového učení.

Formát kurzu

Interaktivní přednáška a diskuse. Mnoho cvičení a praxe. Hands-on implementace v živém laboratoři prostředí.

Možnosti personalizace kurzu

Chcete-li požádat o přizpůsobené školení pro tento kurz, kontaktujte nás, abyste uspořádali.

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

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

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.

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.

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.

Hadoop is the most popular Big Data processing framework.

Audience

• Developers

Format of the Course

• Lectures, hands-on practice, small tests along the way to gauge understanding

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.

In this instructor-led training in České republice, 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

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

Alluxio je open-source virtuální distribuovaný systém ukládání, který spojuje rozdílné systémy ukládání a umožňuje aplikacím interagovat s daty v rychlosti paměti. Používá se společnostmi jako Intel, Baidu a Alibaba. V tomto instruktorově vedeném, živém tréninku se účastníci naučí, jak používat Alluxio pro propojení různých počítačových rámců se systémy ukládání a efektivně spravovat multi-petabyte data, jak postupují prostřednictvím vytvoření aplikace s Alluxio. Po ukončení tohoto tréninku budou účastníci schopni:

Rozvíjet aplikaci s Alluxio Připojení velkých datových systémů a aplikací při zachování jednoho názvového prostoru Efektivně extrahuje hodnotu z velkých dat v jakémkoli formátu ukládání Zlepšení výkonu pracovní zátěže Rozložte a spravujte Alluxio samostatně nebo klastrovaně

publikum

Data vědci Vývojář Systémový administrátor

Formát kurzu

Částečná přednáška, částečná diskuse, cvičení a těžká praxe

Tigon is an open-source, real-time, low-latency, high-throughput, native YARN, stream processing framework that sits on top of HDFS and HBase for persistence. Tigon applications address use cases such as network intrusion detection and analytics, social media market analysis, location analytics, and real-time recommendations to users. This instructor-led, live training introduces Tigon's approach to blending real-time and batch processing as it walks participants through the creation a sample application. By the end of this training, participants will be able to:

• Create powerful, stream processing applications for handling large volumes of data
• Process stream sources such as Twitter and Webserver Logs
• Use Tigon for rapid joining, filtering, and aggregating of streams

Audience

• Developers

Format of the course

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

In this instructor-led, live training in České republice (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 České republice, 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.

Hadoop je populární Big Data zpracovatelský rámec. Python je programovací jazyk vysoké úrovně známý pro jeho jasný syntax a čitelnost kódu. V tomto instruktorově vedeném, živém tréninku se účastníci dozví, jak pracovat s Hadoop, MapReduce, Pig, a Spark pomocí Python, jak postupují prostřednictvím několika příkladů a používají případy. Po ukončení tohoto tréninku budou účastníci schopni:

Pochopte základní pojmy za Hadoop, MapReduce, Pig a Spark Používejte Python s Hadoop Distributed File System (HDFS), MapReduce, Pig a Spark Použijte Snakebite pro programový přístup k HDFS v rámci Python Použijte mrjob psát MapReduce práce v Python Napsat Spark programy s Python Rozšiřte funkčnost prasat pomocí Python UDF Řízení MapReduce pracovních míst a Pig scripts pomocí Luigi

publikum

Vývojáři IT profesionálové

Formát kurzu

Částečná přednáška, částečná diskuse, cvičení a těžká praxe

Sqoop is an open source software tool for transfering data between Hadoop and relational databases or mainframes. It can be used to import data from a relational database management system (RDBMS) such as MySQL or Oracle or a mainframe into the Hadoop Distributed File System (HDFS). Thereafter, the data can be transformed in Hadoop MapReduce, and then re-exported back into an RDBMS. In this instructor-led, live training, participants will learn how to use Sqoop to import data from a traditional relational database to Hadoop storage such HDFS or Hive and vice versa. By the end of this training, participants will be able to:

• Install and configure Sqoop
• Import data from MySQL to HDFS and Hive
• Import data from HDFS and Hive to MySQL

Audience

• System administrators
• Data engineers

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.

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.

Apache Hadoop je populární rámec pro zpracování dat pro zpracování velkých datových souborů na mnoha počítačích. Tento výcvik vedený instruktorem (online nebo on-site) je zaměřen na správce systémů, kteří se chtějí dozvědět, jak vytvořit, rozvíjet a spravovat Hadoop klustery v rámci své organizace. Po ukončení tohoto tréninku budou účastníci schopni:

Instalace a nastavení Apache Hadoop. Pochopte čtyři hlavní složky Hadoop ekosystému: HDFS, MapReduce, YARN a Hadoop Common. Použijte Hadoop Distributed File System (HDFS) pro skalování klastru na stovky nebo tisíce uzlin. •   Nainstalujte HDFS, aby fungoval jako skladovací motor pro on-premise Spark deploymenty. Nastavení Spark pro přístup k alternativním úložným řešením, jako je Amazon S3 a NoSQL databázových systémů, jako jsou Redis, Elasticsearch, Couchbase, Aerospike, atd. Provádět administrativní úkoly, jako je poskytování, řízení, monitorování a zabezpečení Apache Hadoop klastru.

Formát kurzu

Interaktivní přednáška a diskuse. Mnoho cvičení a praxe. Hands-on implementace v živém laboratoři prostředí.

Možnosti personalizace kurzu

Chcete-li požádat o přizpůsobené školení pro tento kurz, kontaktujte nás, abyste uspořádali.

Cloudera Impala je open source masivně paralelní zpracování (MPP) SQL dotazní motor pro Apache Hadoop klastry. Impala umožňuje uživatelům vydávat nízkou frekvenci SQL požadavky na data uložená v Hadoop Distributed File System a Apache Hbase bez nutnosti pohybu nebo transformace dat. publikum Tento kurz je zaměřen na analytiky a vědce údajů, kteří provádějí analýzu údajů uložených v Hadoop prostřednictvím nástrojů a nástrojů SQL. Po tomto kurzu budou delegáti schopni

Využijte významné informace z Hadoop klastrů s Impala. Napište konkrétní programy k usnadnění Business Intelligence v Impala SQL Dialect. Problémy se vyskytují Impala.

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

This instructor-led, live training in České republice (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.