Helyi, oktatott élő Apache Spark tréningek demonstrálják a handson gyakorlatot arról, hogyan illeszkedik a Spark a Big Data ökoszisztémába, és hogyan használja a Sparkot az adatok elemzéséhez Az Apache Spark tréning "helyszíni élő képzés" vagy "távoli élő képzés" formájában érhető el A helyszíni élő képzés helyi szinten valósulhat meg az ügyfél telephelyén Magyarország vagy a NobleProg vállalati oktatóközpontjaiban Magyarország A távoli élőképzés interaktív, távoli asztal segítségével történik NobleProg Az Ön Helyi Képzési Szolgáltatója.
Machine Translated
Vélemények
Richard nagyon nyugodt és módszeres, analitikus betekintéssel - pontosan azokkal a tulajdonságokkal, amelyek ahhoz szükségesek, hogy bemutassák ezt a módszert.
Kieran Mac Kenna
Kurzus: Spark for Developers
Machine Translated
megoszthatja a koncepció diagramját és a mintákat a piszkos képpel M
Mark Yang - FMR
Kurzus: Spark for Developers
Machine Translated
Alkalmazható forgatókönyvek és esetek
zhaopeng liu - Fmr
Kurzus: Spark for Developers
Machine Translated
Esettanulmány
国栋 张
Kurzus: Spark for Developers
Machine Translated
az ülés minden részét
Eric Han - Fmr
Kurzus: Spark for Developers
Machine Translated
Sokkal többet tudunk az egész környezetről.
John Kidd
Kurzus: Spark for Developers
Machine Translated
A tréner érdekes és szórakoztató, ami egész nap egész napos edzést segíti.
Ryan Speelman
Kurzus: Spark for Developers
Machine Translated
Azt hiszem, a trénernek kiváló stílusa volt a humor és az élet történeteinek ötvözésével, hogy a tárgyakat könnyen elérhetővé tegye. Nagyon ajánlom ezt a professzort a jövőben.
Kurzus: Spark for Developers
Machine Translated
Ernesto nagyszerű munkát végzett a Spark és a különböző modulok magas szintű elképzeléseinek magyarázatával.
Michael Nemerouf
Kurzus: Spark for Developers
Machine Translated
Ez az egyik legjobb gyakorlati programozási gyakorlat, amit valaha vettem.
Laura Kahn
Kurzus: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
Ez az egyik legjobb minőségű online képzés, amit valaha is vettem a 13 éves karrierem során. Folytasd a nagyszerű munkát!.
Kurzus: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
Richard nagyon hajlandó volt kitérni, amikor félig kapcsolódó kérdéseket akartunk kérdezni olyan dolgokról, amelyek nem a tananyagon voltak. A magyarázatok világosak voltak, és a figyelmeztetésekkel kapcsolatban felvette a figyelmét minden olyan tanácsban, amit nekünk adtak.
ARM Limited
Kurzus: Spark for Developers
Machine Translated
A VM én szeretett nagyon sok A tanár nagyon tájékozott volt a témával kapcsolatban, valamint más témákban, nagyon kedves és barátságos volt. Tetszett a létesítmény Dubaiban.
Safar Alqahtani - Elm Information Security
Kurzus: Big Data Analytics in Health
Machine Translated
gyakorlófeladatokat
Pawel Kozikowski - GE Medical Systems Polska Sp. Zoo
Kurzus: Python and Spark for Big Data (PySpark)
Machine Translated
Ajay nagyon barátságos, segítőkész és ismeretes volt a tárgyalt témában is.
Biniam Guulay - ICE International Copyright Enterprise Germany GmbH
Kurzus: Spark for Developers
Machine Translated
A laboratóriumi gyakorlatok. Az elmélet alkalmazása a következő napok első napjától kezdve.
Dell
Kurzus: A Practical Introduction to Stream Processing
Machine Translated
A tanár a képzési programot a jelenlegi igényeinkhez igazította.
EduBroker Sp. z o.o.
Kurzus: Python and Spark for Big Data (PySpark)
Machine Translated
A hasonló gyakorlatok különböző módon történő végrehajtása segít megérteni, hogy az egyes összetevők ( Hadoop / Spark, önálló / klaszter) önmagukban és együtt képesek-e megtenni. Ötleteket adott nekem arról, hogyan kell tesztelnem az alkalmazásomat a helyi gépemen, amikor fejlesztem, amikor egy fürtbe telepítjük.
Thomas Carcaud - IT Frankfurt GmbH
Kurzus: Spark for Developers
Machine Translated
egyéni figyelmet.
ARCHANA ANILKUMAR - PPL
Kurzus: Python and Spark for Big Data (PySpark)
Machine Translated
Ismerje meg a Spark Streaming, az Adatriks és az AWS Redshift-t
Lim Meng Tee - Jobstreet.com Shared Services Sdn. Bhd.
Kurzus: Apache Spark in the Cloud
Machine Translated
A tartalom és a tudás.
Jobstreet.com Shared Services Sdn. Bhd.
Kurzus: Apache Spark in the Cloud
Machine Translated
Nagyon informatív volt. Nagyon kevés tapasztalatom volt a Spark-hoz, és eddig ez a tanfolyam nagyon jó bevezetést adott a témához.
Intelligent Medical Objects
Kurzus: Apache Spark in the Cloud
Machine Translated
Nagyszerű volt megérteni, hogy mi folyik a Spark motorháza alatt. Tudva, hogy mi folyik a motorháztető alatt segít jobban megérteni, hogy miért a kód, vagy nem csinál, amit elvár, hogy nem. Sok a képzés volt a kezét, amely mindig nagy, és a szakasz optimalizálás volt, rendkívül fontos, hogy az én jelenlegi munka, ami szép volt.
Intelligent Medical Objects
Kurzus: Apache Spark in the Cloud
Machine Translated
Ez egy nagyszerű osztály! Nagyon nagyra értékelem, hogy az András nagyon jól ismerte, hogy mi a szikra, ahol jött, és milyen problémák megoldhatók. Sokkal jobb, mint más bevezetések, amit láttam, hogy csak azért merülne fel, hogyan kell használni. András mély ismerete van a témáról, és jól ismerte a dolgokat.
Intelligent Medical Objects
Kurzus: Apache Spark in the Cloud
Machine Translated
Az élő példák, amelyeket megadtak és megmutatták a szikrák alapvető szempontjait.
Intelligent Medical Objects
Kurzus: Apache Spark in the Cloud
Machine Translated
1 . Jobb egyensúly a magas szintű koncepciók és a technikai adatok között. 2. Az András nagyon jól tudta a tanításáról. 3. Gyakorlat
Steven Wu - Intelligent Medical Objects
Kurzus: Apache Spark in the Cloud
Machine Translated
Kezében a munkamenet / megbízások
Poornima Chenthamarakshan - Intelligent Medical Objects
Kurzus: Apache Spark in the Cloud
Machine Translated
Edényt a közönség kérésére alapulva a képzést kissé kidobja, ezért néhány olyan fényt dob, hogy megköveteltem
Intelligent Medical Objects
Kurzus: Apache Spark in the Cloud
Machine Translated
A tempája nagyszerű volt. Szerettem azt a tényt, hogy elméletre is mentél, hogy megértem, miért csinálok, hogy megkérdezem.
Intelligent Medical Objects
Kurzus: Apache Spark in the Cloud
Machine Translated
Azt hiszem, a trénernek kiváló stílusa volt a humor és az élet történeteinek ötvözésével, hogy a tárgyakat könnyen elérhetővé tegye. Nagyon ajánlom ezt a professzort a jövőben.
Kurzus: Spark for Developers
Machine Translated
Ez az egyik legjobb minőségű online képzés, amit valaha is vettem a 13 éves karrierem során. Folytasd a nagyszerű munkát!.
Kurzus: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
Spark Subcategories
Apache Spark Course Outlines
Kurzusnév
Időtartalma
Összefoglaló
Kurzusnév
Időtartalma
Összefoglaló
21 hours
OBJECTIVE:
This course will introduce Apache Spark. The students will learn how Spark fits into the Big Data ecosystem, and how to use Spark for data analysis. The course covers Spark shell for interactive data analysis, Spark internals, Spark APIs, Spark SQL, Spark streaming, and machine learning and graphX.
AUDIENCE :
Developers / Data Analysts
This course will introduce Apache Spark. The students will learn how Spark fits into the Big Data ecosystem, and how to use Spark for data analysis. The course covers Spark shell for interactive data analysis, Spark internals, Spark APIs, Spark SQL, Spark streaming, and machine learning and graphX.
AUDIENCE :
Developers / Data Analysts
21 hours
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.
14 hours
Magellan is an open-source distributed execution engine for geospatial analytics on big data. Implemented on top of Apache Spark, it extends Spark SQL and provides a relational abstraction for geospatial analytics.
This instructor-led, live training introduces the concepts and approaches for implementing geospacial analytics and walks participants through the creation of a predictive analysis application using Magellan on Spark.
By the end of this training, participants will be able to:
- Efficiently query, parse and join geospatial datasets at scale
- Implement geospatial data in business intelligence and predictive analytics applications
- Use spatial context to extend the capabilities of mobile devices, sensors, logs, and wearables
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.
This instructor-led, live training introduces the concepts and approaches for implementing geospacial analytics and walks participants through the creation of a predictive analysis application using Magellan on Spark.
By the end of this training, participants will be able to:
- Efficiently query, parse and join geospatial datasets at scale
- Implement geospatial data in business intelligence and predictive analytics applications
- Use spatial context to extend the capabilities of mobile devices, sensors, logs, and wearables
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.
7 hours
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
7 hours
Spark SQL is Apache Spark's module for working with structured and unstructured data. Spark SQL provides information about the structure of the data as well as the computation being performed. This information can be used to perform optimizations. Two common uses for Spark SQL are:
- to execute SQL queries.
- to read data from an existing Hive installation.
In this instructor-led, live training (onsite or remote), participants will learn how to analyze various types of data sets using Spark SQL.
By the end of this training, participants will be able to:
- Install and configure Spark SQL.
- Perform data analysis using Spark SQL.
- Query data sets in different formats.
- Visualize data and query results.
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.
- to execute SQL queries.
- to read data from an existing Hive installation.
In this instructor-led, live training (onsite or remote), participants will learn how to analyze various types of data sets using Spark SQL.
By the end of this training, participants will be able to:
- Install and configure Spark SQL.
- Perform data analysis using Spark SQL.
- Query data sets in different formats.
- Visualize data and query results.
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.
21 hours
In this instructor-led, live training in Magyarország (onsite or remote), participants will learn how to set up and integrate different Stream Processing frameworks with existing big data storage systems and related software applications and microservices.
By the end of this training, participants will be able to:
- Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming.
- Understand and select the most appropriate framework for the job.
- Process of data continuously, concurrently, and in a record-by-record fashion.
- Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc.
- Integrate the most appropriate stream processing library with enterprise applications and microservices.
By the end of this training, participants will be able to:
- Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming.
- Understand and select the most appropriate framework for the job.
- Process of data continuously, concurrently, and in a record-by-record fashion.
- Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc.
- Integrate the most appropriate stream processing library with enterprise applications and microservices.
21 hours
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.
21 hours
Apache Spark's learning curve is slowly increasing at the begining, it needs a lot of effort to get the first return. This course aims to jump through the first tough part. After taking this course the participants will understand the basics of Apache Spark , they will clearly differentiate RDD from DataFrame, they will learn Python and Scala API, they will understand executors and tasks, etc. Also following the best practices, this course strongly focuses on cloud deployment, Databricks and AWS. The students will also understand the differences between AWS EMR and AWS Glue, one of the lastest Spark service of AWS.
AUDIENCE:
Data Engineer, DevOps, Data Scientist
AUDIENCE:
Data Engineer, DevOps, Data Scientist
21 hours
This instructor-led, live training in Magyarország (online or onsite) is aimed at software engineers who wish to stream big data with Spark Streaming and Scala.
By the end of this training, participants will be able to:
- Create Spark applications with the Scala programming language.
- Use Spark Streaming to process continuous streams of data.
- Process streams of real-time data with Spark Streaming.
By the end of this training, participants will be able to:
- Create Spark applications with the Scala programming language.
- Use Spark Streaming to process continuous streams of data.
- Process streams of real-time data with Spark Streaming.
14 hours
This instructor-led, live training in Magyarország (online or onsite) is aimed at data scientists who wish to use the SMACK stack to build data processing platforms for big data solutions.
By the end of this training, participants will be able to:
- Implement a data pipeline architecture for processing big data.
- Develop a cluster infrastructure with Apache Mesos and Docker.
- Analyze data with Spark and Scala.
- Manage unstructured data with Apache Cassandra.
By the end of this training, participants will be able to:
- Implement a data pipeline architecture for processing big data.
- Develop a cluster infrastructure with Apache Mesos and Docker.
- Analyze data with Spark and Scala.
- Manage unstructured data with Apache Cassandra.
21 hours
This instructor-led, live training in Magyarország (online or onsite) is aimed at engineers who wish to set up and deploy Apache Spark system for processing very large amounts of data.
By the end of this training, participants will be able to:
- Install and configure Apache Spark.
- Quickly process and analyze very large data sets.
- Understand the difference between Apache Spark and Hadoop MapReduce and when to use which.
- Integrate Apache Spark with other machine learning tools.
By the end of this training, participants will be able to:
- Install and configure Apache Spark.
- Quickly process and analyze very large data sets.
- Understand the difference between Apache Spark and Hadoop MapReduce and when to use which.
- Integrate Apache Spark with other machine learning tools.
21 hours
This instructor-led, live training in Magyarország (online or onsite) is aimed at developers who wish to carry out big data analysis using Apache Spark in their .NET applications.
By the end of this training, participants will be able to:
- Install and configure Apache Spark.
- Understand how .NET implements Spark APIs so that they can be accessed from a .NET application.
- Develop data processing applications using C# or F#, capable of handling data sets whose size is measured in terabytes and pedabytes.
- Develop machine learning features for a .NET application using Apache Spark capabilities.
- Carry out exploratory analysis using SQL queries on big data sets.
By the end of this training, participants will be able to:
- Install and configure Apache Spark.
- Understand how .NET implements Spark APIs so that they can be accessed from a .NET application.
- Develop data processing applications using C# or F#, capable of handling data sets whose size is measured in terabytes and pedabytes.
- Develop machine learning features for a .NET application using Apache Spark capabilities.
- Carry out exploratory analysis using SQL queries on big data sets.
35 hours
This instructor-led, live training in Magyarország (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.
14 hours
This instructor-led, live training in Magyarország (online or onsite) is aimed at data scientists and developers who wish to use Spark NLP, built on top of Apache Spark, to develop, implement, and scale natural language text processing models and pipelines.
By the end of this training, participants will be able to:
- Set up the necessary development environment to start building NLP pipelines with Spark NLP.
- Understand the features, architecture, and benefits of using Spark NLP.
- Use the pre-trained models available in Spark NLP to implement text processing.
- Learn how to build, train, and scale Spark NLP models for production-grade projects.
- Apply classification, inference, and sentiment analysis on real-world use cases (clinical data, customer behavior insights, etc.).
By the end of this training, participants will be able to:
- Set up the necessary development environment to start building NLP pipelines with Spark NLP.
- Understand the features, architecture, and benefits of using Spark NLP.
- Use the pre-trained models available in Spark NLP to implement text processing.
- Learn how to build, train, and scale Spark NLP models for production-grade projects.
- Apply classification, inference, and sentiment analysis on real-world use cases (clinical data, customer behavior insights, etc.).
35 hours
MLlib is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.
It divides into two packages:
-
spark.mllib contains the original API built on top of RDDs.
-
spark.ml provides higher-level API built on top of DataFrames for constructing ML pipelines.
Audience
This course is directed at engineers and developers seeking to utilize a built in Machine Library for Apache Spark
It divides into two packages:
-
spark.mllib contains the original API built on top of RDDs.
-
spark.ml provides higher-level API built on top of DataFrames for constructing ML pipelines.
Audience
This course is directed at engineers and developers seeking to utilize a built in Machine Library for Apache Spark
21 hours
This course is aimed at developers and data scientists who wish to understand and implement AI within their applications. Special focus is given to Data Analysis, Distributed AI and NLP.
28 hours
In this instructor-led, live training in Magyarország, participants will learn about the technology offerings and implementation approaches for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using a Graph Computing (also known as Graph Analytics) approach. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments.
By the end of this training, participants will be able to:
- Understand how graph data is persisted and traversed.
- Select the best framework for a given task (from graph databases to batch processing frameworks.)
- Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel.
- View real-world big data problems in terms of graphs, processes and traversals.
By the end of this training, participants will be able to:
- Understand how graph data is persisted and traversed.
- Select the best framework for a given task (from graph databases to batch processing frameworks.)
- Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel.
- View real-world big data problems in terms of graphs, processes and traversals.
21 hours
In this instructor-led, live training in Magyarország, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.
By the end of this training, participants will be able to:
- Learn how to use Spark with Python to analyze Big Data.
- Work on exercises that mimic real world cases.
- Use different tools and techniques for big data analysis using PySpark.
By the end of this training, participants will be able to:
- Learn how to use Spark with Python to analyze Big Data.
- Work on exercises that mimic real world cases.
- Use different tools and techniques for big data analysis using PySpark.
Last Updated:
Upcoming Spark Courses
12-01-2022 09:30
7 hours
14-01-2022 09:30
7 hours
17-01-2022 09:30
21 hours
26-01-2022 09:30
14 hours
08-02-2022 09:30
21 hours
Other countries
Consulting
Online Spark courses, Weekend Apache Spark courses, Evening Apache Spark training, Spark boot camp, Spark instructor-led, Weekend Spark training, Evening Spark courses, Apache Spark coaching, Spark instructor, Apache Spark trainer, Spark training courses, Apache Spark classes, Apache Spark on-site, Apache Spark private courses, Apache Spark one on one training
.png "european space agency (esa)")
.png "aeronautical radio, incorporated (arinc)"){kind=small}
.png "new york stock exchange (nyse)")