Τα τοπικά εκπαιδευτικά σεμινάρια Apache Hadoop υπό την καθοδήγηση των τοπικών εκπαιδευτών επιδεικνύουν μέσω της διαδραστικής πρακτικής άσκησης τις βασικές συνιστώσες του οικοσυστήματος Hadoop και τον τρόπο με τον οποίο αυτές οι τεχνολογίες μπορούν να χρησιμοποιηθούν για την επίλυση μεγάλων προβλημάτων. Η εκπαίδευση Hadoop είναι διαθέσιμη ως "onsite live training" ή "remote live training". Η επιτόπια κατάρτιση σε απευθείας σύνδεση μπορεί να πραγματοποιηθεί σε τοπικό επίπεδο στις εγκαταστάσεις του πελάτη στο Ελλάδα ή σε εταιρικά κέντρα κατάρτισης NobleProg στο Ελλάδα . Η απομακρυσμένη ζωντανή προπόνηση πραγματοποιείται μέσω μιας διαδραστικής, απομακρυσμένης επιφάνειας εργασίας. NobleProg - Ο τοπικός παροχέας εκπαίδευσης
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Testimonials
Το γεγονός ότι όλα τα δεδομένα και το λογισμικό ήταν έτοιμα να χρησιμοποιηθούν σε ένα ήδη προετοιμασμένο VM, το οποίο παρείχε ο εκπαιδευτής σε εξωτερικούς δίσκους.
vyzVoice
Course: Hadoop for Developers and Administrators
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Εκπαιδευτής δίνουν πραγματικά παραδείγματα
Simon Hahn
Course: Administrator Training for Apache Hadoop
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Μεγάλες ικανότητες του εκπαιδευτή
Grzegorz Gorski
Course: Administrator Training for Apache Hadoop
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Πολλές πρακτικές συνεδρίες.
Jacek Pieczątka
Course: Administrator Training for Apache Hadoop
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Πολύ άρεσε ο διαδραστικός τρόπος εκμάθησης.
Luigi Loiacono
Course: Data Analysis with Hive/HiveQL
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Ήταν μια πολύ πρακτική εκπαίδευση, μου άρεσαν οι πρακτικές ασκήσεις.
Proximus
Course: Data Analysis with Hive/HiveQL
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καλή εικόνα, καλή ισορροπία μεταξύ θεωρίας και ασκήσεων
Proximus
Course: Data Analysis with Hive/HiveQL
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Δυναμική αλληλεπίδραση και "χέρια" στο θέμα, χάρη στην Virtual Machine, πολύ διεγερτικό!
Philippe Job
Course: Data Analysis with Hive/HiveQL
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Η ικανότητα και η γνώση του εκπαιδευτή
Jonathan Puvilland
Course: Data Analysis with Hive/HiveQL
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Ήταν πολύ hands-on, περάσαμε το ήμισυ του χρόνου πράγματι κάνουμε πράγματα στο Cloudera / Hadoop, τρέχοντας διαφορετικές εντολές, ελέγχοντας το σύστημα, και ούτω καθεξής. Τα επιπλέον υλικά (βιβλία, ιστοτόποι κ.λπ.) εκτιμήθηκαν πραγματικά, θα πρέπει να συνεχίσουμε να μαθαίνουμε. Οι εγκαταστάσεις ήταν αρκετά διασκεδαστικές και πολύ βολικό, το setup cluster από το μηδέν ήταν πραγματικά καλό.
Ericsson
Course: Administrator Training for Apache Hadoop
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Ο εκπαιδευτής ήταν φανταστικός και γνώριζε πραγματικά τα πράγματα του. Έμαθα πολλά σχετικά με το λογισμικό που δεν ήξερα προηγουμένως, το οποίο θα βοηθήσει πολύ στη δουλειά μου!
Steve McPhail - Alberta Health Services - Information Technology
Course: Data Analysis with Hive/HiveQL
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Οι αρχές υψηλού επιπέδου για την κυψέλη, το HDFS ...
Geert Suys - Proximus Group
Course: Data Analysis with Hive/HiveQL
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Ο χειροτέχνης. Η πρακτική mix / theroy
Proximus Group
Course: Data Analysis with Hive/HiveQL
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Ο Fulvio μπόρεσε να καταλάβει την επιχειρηματική περίπτωση των εταιρειών μας και ήταν σε θέση να συσχετιστεί με το υλικό του μαθήματος, σχεδόν αμέσως.
Samuel Peeters - Proximus Group
Course: Data Analysis with Hive/HiveQL
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Πολλές πρακτικές ασκήσεις.
Ericsson
Course: Administrator Training for Apache Hadoop
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Εργαλείο διαχείρισης Ambari. Δυνατότητα συζήτησης πρακτικών εμπειριών Hadoop από άλλες επιχειρηματικές περιπτώσεις παρά από τηλεπικοινωνίες.
Ericsson
Course: Administrator Training for Apache Hadoop
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Νόμιζα ότι έκανε καλή δουλειά στην προσαρμογή της εμπειρίας στο κοινό. Αυτή η κλάση σχεδιάστηκε κυρίως για να καλύψει την ανάλυση δεδομένων με την ΚΥΨΈΛΗ, αλλά εγώ και ο συμεργάτης μου κάνουμε διαχείριση ΚΥΨΈΛΗς χωρίς πραγματικές ευθύνες ανάλυσης δεδομένων.
ian reif - Franchise Tax Board
Course: Data Analysis with Hive/HiveQL
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Το VM που μου άρεσε πάρα πολύ Ο δάσκαλος ήταν πολύ γνώστης σχετικά με το θέμα, καθώς και άλλα θέματα, ήταν πολύ ωραίο και φιλικό Μου άρεσε η εγκατάσταση στο Ντουμπάι.
Safar Alqahtani - Elm Information Security
Course: Big Data Analytics in Health
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Εκπαιδευτικά θέματα και συμμετοχή του εκπαιδευτή
Izba Administracji Skarbowej w Lublinie
Course: Apache NiFi for Administrators
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Επικοινωνία με άτομα που παρακολουθούν εκπαίδευση.
Andrzej Szewczuk - Izba Administracji Skarbowej w Lublinie
Course: Apache NiFi for Administrators
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πρακτικά πράγματα για να γίνει, η θεωρία εξυπηρετήθηκε καλά από τον Ajay
Dominik Mazur - Capgemini Polska Sp. z o.o.
Course: Hadoop Administration on MapR
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Γυμνάσια
Capgemini Polska Sp. z o.o.
Course: Hadoop Administration on MapR
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χρησιμότητα των ασκήσεων
Algomine sp.z.o.o sp.k.
Course: Apache NiFi for Administrators
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Βρήκα την κατάρτιση καλή, πολύ ενημερωτική .... αλλά θα μπορούσε να έχει διανεμηθεί σε 4 ή 5 ημέρες, επιτρέποντάς μας να πάμε σε περισσότερες λεπτομέρειες για διαφορετικές πτυχές.
Veterans Affairs Canada
Course: Hadoop Administration
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Μου άρεσε πραγματικά η προπόνηση. Ο Αντόν έχει πολλές γνώσεις και έχει αναπτύξει την απαραίτητη θεωρία με πολύ προσιτό τρόπο. Είναι υπέροχο ότι η προπόνηση ήταν πολλές ενδιαφέρουσες ασκήσεις, έτσι έχουμε έρθει σε επαφή με την τεχνολογία που γνωρίζουμε από την αρχή.
Szymon Dybczak - Algomine sp.z.o.o sp.k.
Course: Apache NiFi for Administrators
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Βρήκα αυτό το μάθημα έδωσε μια μεγάλη εικόνα και άγγιξε γρήγορα μερικές περιοχές που δεν σκέφτηκα καν.
Veterans Affairs Canada
Course: Hadoop Administration
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Μου άρεσε πραγματικά οι ασκήσεις εργασίας με το σύμπλεγμα για να δούμε την απόδοση των κόμβων σε όλο το σύμπλεγμα και την εκτεταμένη λειτουργικότητα.
CACI Ltd
Course: Apache NiFi for Developers
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Ο Ajay ήταν ένας πολύ έμπειρος σύμβουλος και ήταν σε θέση να απαντήσει σε όλες μας τις ερωτήσεις και μάλιστα έκανε προτάσεις για τις βέλτιστες πρακτικές για το έργο στο οποίο ασχολούμαστε.
CACI Ltd
Course: Apache NiFi for Administrators
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Πρακτικό κομμάτι. Τα καθήκοντα ήταν καλά επιλεγμένα για τα θέματα που συζητήθηκαν.
Rossmann SDP
Course: HBase for Developers
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Apache Hadoop Subcategories
Hadoop Course Outlines
Course Name
Duration
Overview
Course Name
Duration
Overview
21 hours
Overview
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
Course goal:
Getting knowledge regarding Hadoop cluster administration
35 hours
Overview
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.
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.
28 hours
Overview
Audience:
This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand.
This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand.
28 hours
Overview
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.
21 hours
Overview
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%).
Audience: developers
Duration: three days
Format: lectures (50%) and hands-on labs (50%).
21 hours
Overview
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
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
21 hours
Overview
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.
“…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.
21 hours
Overview
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.
Audience
Business Analysts
Duration
three days
Format
Lectures and hands on labs.
21 hours
Overview
Hadoop is the most popular Big Data processing framework.
14 hours
Overview
Audience
- Developers
Format of the Course
- Lectures, hands-on practice, small tests along the way to gauge understanding
- Developers
Format of the Course
- Lectures, hands-on practice, small tests along the way to gauge understanding
21 hours
Overview
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.
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.
14 hours
Overview
In this instructor-led training in Ελλάδα, 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
- 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
14 hours
Overview
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
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
7 hours
Overview
Alluxio is an open-source virtual distributed storage system that unifies disparate storage systems and enables applications to interact with data at memory speed. It is used by companies such as Intel, Baidu and Alibaba.
In this instructor-led, live training, participants will learn how to use Alluxio to bridge different computation frameworks with storage systems and efficiently manage multi-petabyte scale data as they step through the creation of an application with Alluxio.
By the end of this training, participants will be able to:
- Develop an application with Alluxio
- Connect big data systems and applications while preserving one namespace
- Efficiently extract value from big data in any storage format
- Improve workload performance
- Deploy and manage Alluxio standalone or clustered
Audience
- Data scientist
- Developer
- System administrator
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use Alluxio to bridge different computation frameworks with storage systems and efficiently manage multi-petabyte scale data as they step through the creation of an application with Alluxio.
By the end of this training, participants will be able to:
- Develop an application with Alluxio
- Connect big data systems and applications while preserving one namespace
- Efficiently extract value from big data in any storage format
- Improve workload performance
- Deploy and manage Alluxio standalone or clustered
Audience
- Data scientist
- Developer
- System administrator
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
Overview
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
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
14 hours
Overview
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
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
21 hours
Overview
In this instructor-led, live training in Ελλάδα (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.
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.
7 hours
Overview
In this instructor-led, live training in Ελλάδα, 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.
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.
28 hours
Overview
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
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
14 hours
Overview
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.
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.
21 hours
Overview
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.
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.
35 hours
Overview
This instructor-led, live training in Ελλάδα (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.
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.
7 hours
Overview
This course covers how to use Hive SQL language (AKA: Hive HQL, SQL on Hive, HiveQL) for people who extract data from Hive
21 hours
Overview
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.
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.
21 hours
Overview
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
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
21 hours
Overview
This instructor-led, live training in (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.
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.
Upcoming Apache Hadoop Courses
13-10-2021 09:30
21 hours
26-10-2021 09:30
7 hours
16-11-2021 09:30
21 hours
22-11-2021 09:30
35 hours
22-11-2021 09:30
28 hours
Other countries
Consulting
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