Τα τοπικά εκπαιδευτικά σεμινάρια Apache Spark, καθοδηγούμενα από εκπαιδευτές, αποδεικνύουν μέσω πρακτικής άσκησης πώς ο Spark εντάσσεται στο οικοσύστημα Big Data και πώς να χρησιμοποιήσει το Spark για ανάλυση δεδομένων. Η εκπαίδευση Apache Spark είναι διαθέσιμη ως "onsite live training" ή "remote live training". Η επιτόπια κατάρτιση σε πραγματικό χρόνο μπορεί να πραγματοποιηθεί σε τοπικό επίπεδο στις εγκαταστάσεις του πελάτη 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
Testimonials
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Ο Richard είναι πολύ ήρεμος και μεθοδικός, με μια αναλυτική γνώση - ακριβώς τις ιδιότητες που απαιτούνται για να παρουσιάσουν αυτό το είδος πορείας
Kieran Mac Kenna
Course: Spark for Developers
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Πιθανά σενάρια και περιπτώσεις
zhaopeng liu - Fmr
Course: Spark for Developers
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Μελέτη περίπτωσης
国栋 张
Course: Spark for Developers
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Όλα τα τμήματα της συνεδρίασης
Eric Han - Fmr
Course: Spark for Developers
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Γνωρίζουμε ότι γνωρίζουμε πολλά περισσότερα για το περιβάλλον
John Kidd
Course: Spark for Developers
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Ο εκπαιδευτής έκανε την κατηγορία ενδιαφέρουσα και διασκεδαστική, η οποία βοηθά αρκετά με όλες τις ημερήσιες προπονήσεις
Ryan Speelman
Course: Spark for Developers
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Νομίζω ότι ο εκπαιδευτής είχε ένα εξαιρετικό ύφος του συνδυασμού χιούμορ και ιστορίες πραγματικής ζωής για να κάνουν τα θέματα που είναι πολύ προσιτά. Θα ήθελα πολύ να συστήσω αυτόν τον καθηγητή στο μέλλον.
Course: Spark for Developers
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Ο Ερνέστο έκανε μια σπουδαία δουλειά εξηγώντας τις έννοιες υψηλού επιπέδου για τη χρήση του Spark και των διάφορων ενοτήτων του.
Michael Nemerouf
Course: Spark for Developers
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Μου άρεσε η μεθοδολογία που περίμενε ο Jorge
Experian Colombia S.A
Course: Spark for Developers
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Αυτό είναι ένα από τα καλύτερα hands-on με ασκήσεις μαθήματα προγραμματισμού που έχω πάρει ποτέ.
Laura Kahn
Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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Αυτή είναι μια από τις καλύτερες online εκπαιδευτικές ασκήσεις που έχω πάρει ποτέ στην καριέρα μου για 13 χρόνια. Συνεχίστε την καλή δουλειά!
Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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Ο Ρίτσαρντ ήταν πολύ πρόθυμος να ξεφύγει όταν θέλαμε να θέσουμε ημι-σχετικές ερωτήσεις σχετικά με πράγματα που δεν περιλαμβάνονται στο πρόγραμμα σπουδών. Οι εξηγήσεις ήταν σαφείς και ήταν μπροστά για τις προειδοποιήσεις σε οποιαδήποτε συμβουλή μας έδωσε.
ARM Limited
Course: Spark for Developers
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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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εργασίες εξάσκησης
Pawel Kozikowski - GE Medical Systems Polska Sp. Zoo
Course: Python and Spark for Big Data (PySpark)
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Νομίζω ότι ο εκπαιδευτής είχε ένα εξαιρετικό ύφος του συνδυασμού χιούμορ και ιστορίες πραγματικής ζωής για να κάνουν τα θέματα που είναι πολύ προσιτά. Θα ήθελα πολύ να συστήσω αυτόν τον καθηγητή στο μέλλον.
Course: Spark for Developers
Translated by
Αυτή είναι μια από τις καλύτερες online εκπαιδευτικές ασκήσεις που έχω πάρει ποτέ στην καριέρα μου για 13 χρόνια. Συνεχίστε την καλή δουλειά!
Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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.
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
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.
Many real world problems can be described in terms of graphs. For example, the Web graph, the social network graph, the train network graph and the language graph. These graphs tend to be extremely large; processing them requires a specialized set of tools and processes -- these tools and processes can be referred to as Graph Computing (also known as Graph Analytics).
In this instructor-led, live training, 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.
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
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 (onsite or remote) 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.
Stream Processing refers to the real-time processing of "data in motion", that is, performing computations on data as it is being received. Such data is read as continuous streams from data sources such as sensor events, website user activity, financial trades, credit card swipes, click streams, etc. Stream Processing frameworks are able to read large volumes of incoming data and provide valuable insights almost instantaneously.
In this instructor-led, live training (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. - Integrating the most appropriate stream processing library with enterprise applications and microservices
Audience
- Developers - Software architects
Format of the Course
- Part lecture, part discussion, exercises and heavy hands-on practice
Notes
- To request a customized training for this course, please contact us to arrange.
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
Audience
- Application developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
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.
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.
Python is a high-level programming language famous for its clear syntax and code readibility. Spark is a data processing engine used in querying, analyzing, and transforming big data. PySpark allows users to interface Spark with Python.
In this instructor-led, live training, 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 circumstances. - Use different tools and techniques for big data analysis using PySpark.
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
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.
Audience
- Data analysts - Data scientists - Data engineers
Format of the Course
- Part lecture, part discussion, exercises and heavy hands-on practice
Notes
- To request a customized training for this course, please contact us to arrange.
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:
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spark.mllib contains the original API built on top of RDDs.
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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
