Big Data Courses Online

ライブインストラクター主導のオンライントレーニングBig Data コースはを使用して提供されます インタラクティブなリモートデスクトップ!

中 各参加者が実行できるコースBig Data QwikCourseが提供するリモートデスクトップでの演習。


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Big Data Training


Big Data Methodology

About

In this course, we are going to introduce a way of designing big data applications. The underlying idea is to incorporate techniques from model-driven engineering into a DevOps development life cycle. Why is such an approach suitable and fruitful for data-intensive software? The question is fair, and we shall answer it first. We will start by advocating the use of models in DevOps. We will then look at some benefits of applying model-driven DevOps to big data application construction. Finally, we will introduce our methodology proposal and how the DICE IDE gives support to it.

Content

  • Model-Driven DevOps
  • Model-Driven DevOps for Big Data
  • Methodology Proposal
    • Architecture Modelling
    • Architecture Analysis
    • Architecture Experimentation
  • The DICE Methodology in the IDE

7 hours

¥287,598

Practical DevOps for Big Data

About

Big data is a major trend in information and communication technologies (ICT). With the constant proliferation of mobiles and sensors around the world, the always growing amount of user-generated contents on the social Web, and the soon advent of the Internet of things, through which our everyday equipments (smartphones, electrical appliances, vehicles, homes, etc.) will communicate and exchange data, this data flood that characterises our modern society opens new opportunities and pose interesting challenges. Governments too, today tend to automate more and more public services. Therefore, the ability to process efficiently massive and ever-increasing volumes of data becomes crucial. 

Content

  • Big Data Matters
  • Characteristics of Big Data Systems
  • Big Data for Small and Medium-Sized Enterprises

7 hours

¥287,598

Fundamentals of BigData

About

Big data is a generic term given to datasets that are so large or complicated that they are difficult to store, manipulate and analyse. The three main features of big data are:

  • volume: the sheer amount of data is on a very large scale
  • variety: the type of data being collected is wide-ranging, varied and may be difficult to classify.
  • velocity: the data changes quickly and may include constantly changing data sources.

The lack of structure in Big Data is considered to be the aspect creating the most difficulties. For this reason, traditional data analysis and organisation methods such as relational databases or SQL are no longer useful when it comes to Big Data . However, when the correct techniques are applied to Big Data, a vast amount of useful information can be revealed. Processing Big Data allows professionals such data scientists to spot and analyse hidden patterns and relationships which wouldn't have been easy to interpret before.

Big data is used for different purposes. In some cases, it is used to record factual data such as banking transactions. However, it is increasingly being used to analyse trends and try to make predictions based on relationships and correlations within the data. Big data is being created all the time in many different areas of life. Examples include:

  • scientific research
  • retail
  • banking
  • government
  • mobile networks
  • security
  • real-time applications
  • the Internet.

Content

  • Definition
  • Characteristics
  • Architecture
  • Technologies
  • Applications

7 hours

¥287,598

Practical DevOps for Big Data / Related Work

About

Data are not only part of almost all economic and social activities of our society, but have managed to be viewed as an essential resource for all sectors, organisations, countries and regions. But why this is a reality? It is expected that by 2020 there will be more than 16 zettabytes of useful data (16 Trillion GB). Data are not only part of almost all economic and social activities of our society like velocity, variety and socioeconomic value, flags a paradigm shift towards a data-driven socioeconomic mode which suggests a growth of 236% per year from 2013 to 2020. Thus, data blast is indeed a reality that Europe must both face and endeavour in an organised, forceful, user-centric and goal-oriented approach. It is obvious that data exploitation can be the leading spear of innovation, drive cutting-edge technologies, increase competitiveness and create social and financial impact.

Content

  • Importance of Big Data for the Business
  • DICE Value Proposition
  • Positioning DICE to the market
  • Business requirements and DICE

7 hours

¥287,598

Practical DevOps for Big Data / Monitoring

About

Big Data technologies have become an ever more present topic in both academia and industrial world. These technologies enable businesses to extract valuable insight from their available data, hence more and more SMEs are showing increasing interest in using these types of technologies. Distributed frameworks for processing large amounts of data, such as Apache Hadoop[1], Spark[2], or Storm[3] gained in popularity and applications developed on top of them are more and more prevalent. However, developing soft- ware that meets these high-quality standards expected for business-critical Cloud applications remains a challenge for SMEs. In this case model-driven development (MDD) paradigm and popular standards such as UML, MARTE, TOSCA[4] hold strong promises to tackle this challenge. During development of Big Data applications it is important to monitor performance for each version of the application. Information obtained can be used by software architects and developers to track the evolution in time of the developed application. Monitoring is also useful in determining main factors that impact the quality of different application versions. Throughout the development stage, running applications tend to be more verbose in terms of logged information so that developers can get insights about the developed application. Due to verbosity of logs, data- intensive applications produce large amounts of monitoring data, which in turn need to be collected, pre-processed, stored and made available for high-level queries and visualization. It is clear that there is a need for a scalable, highly available and easy deployable platform for monitoring multiple Big Data frameworks. Which can collect resource-level metrics, such as CPU, memory, disk or network, together with framework level metrics collected from Apache HDFS, YARN, Spark and Storm.

Content

  • Introduction
  • Motivations
  • Existing solutions
  • How the tool works
  • Open Challenges
  • Application domains

7 hours

¥287,598

Use of UML Diagrams for Big Data

About

Documenting Big Data Architectures can entail re-use of classical notations for software architecture description augmented with appropriate notations aimed at isolating and identifying the data-intensive nature of Big Data applications. In this vein, the DICE ecosystem offers a plethora of ready-to-use tools and notations to address a variety of quality issues (performance, reliability, correctness, privacy-by-design, etc.). In order to make profit of these tools, the user has to use the explicit notation we have defined to support their scenario. The notation in question entails building-specific UML diagrams enriched with specific profiles, that is, the standard UML mechanism to design domain-specific extensions --- in our case, the mechanism in question was used to define stereotypes and tagged values inside the DICE Profiles and specific to model data-intensive constructs, features, and characteristics. The DICE profiles tailor the UML meta-model to the domain of DIAs. For example, the generic concept of Class can become more specific, i.e., to have more semantics, by mapping it to one or many concrete Big Data notions and technical characteristics, such as compute and storage nodes (from a more abstract perspective) or Storm Nimbus nodes. Besides the power of expression, the consistency of the models behind the DICE profile remains guaranteed thanks to the meta-models and their relations we defined using the UML standard. 

Content

  • Introduction
  • Methodological Overview
  • Existing Solutions and UML Modelling Summary
  • Quick Reference Scenario
  • DICE UML Modelling in Action: A Sample Scenario
    • 5.1Step a: DPIM Model
    • 5.2Step b and c: DPIM Model Refinement
    • 5.3Step d: DTSM Model Creation
    • 5.4Step e: DDSM Model Creation

7 hours

¥287,598

Discover Technology-Specific Modeling

About

When all essential architecture elements are in place, by means of architectural reasoning in the DPIM layer, it could be made available ad-hoc model transformations that parse DPIM models and produce equipollent DTSM models where the specified data processing needs are exploded (if possible) into a possible configuration using appropriate technologies (e.g., Spark for streaming or Hadoop for batch). At this layer it should be provided for architects and developers several key technological framework packages that can evaluate possible alternatives for Technological Mapping and Logical Implementation, that is, selecting the technological frameworks that map well with the problem at hand and implementing the needed processing logic for that framework. 

Content

  • Introduction
  • DTSM Modelling Explained: The Apache Storm Example
    • Storm Concepts
    • Storm Profile

 


7 hours

¥287,598

Practical DevOps for Big Data and UML Diagrams

About

Documenting Big Data Architectures can entail re-use of classical notations for software architecture description augmented with appropriate notations aimed at isolating and identifying the data-intensive nature of Big Data applications. In this vein, the DICE ecosystem offers a plethora of ready-to-use tools and notations to address a variety of quality issues (performance, reliability, correctness, privacy-by-design, etc.). In order to make profit of these tools, the user has to use the explicit notation we have defined to support their scenario. The notation in question entails building specific UML diagrams enriched with specific profiles, that is, the standard UML mechanism to design domain-specific extensions --- in our case, the mechanism in question was used to define stereotypes and tagged values inside the DICE Profiles and specific to model data-intensive constructs, features, and characteristics. The DICE profiles tailor the UML meta-model to the domain of DIAs. For example, the generic concept of Class can become more specific, i.e., to have more semantics, by mapping it to one or many concrete Big Data notions and technical characteristics, such as, compute and storage nodes (from a more abstract perspective) or Storm Nimbus nodes. Besides the power of expression, the consistency of the models behind the DICE profile remains guaranteed thanks to the meta-models and their relations we defined using the UML standard. In essence, the role of these diagrams and their respective profiles is twofold:

  1. Provide a high level of abstraction of concepts specific to the Big Data domain (e.g., clusters, nodes…) and to Big Data technologies (e.g., Cassandra, Spark…);
  2. Define a set of technical (low level) properties to be checked/evaluated by tools.

Content

  • Introduction
  • Methodological Overview
  • Existing Solutions and UML Modelling Summary
  • Quick Reference Scenario
  • DICE UML Modelling in Action: A Sample Scenario
    • Step a: DPIM Model
    • Step b and c: DPIM Model Refinement
    • Step d: DTSM Model Creation
    • Step e: DDSM Model Creation

 


14 hours

¥575,196

Learn Spark with Python

About

Python is a significant-level programming language well known for its reasonable linguistic structure and code readability. Spark is an information handling motor utilized in questioning, breaking down, and changing huge information. PySpark permits clients to interface Spark with Python.

Content

  • Understanding Big Data
  • Features of Spark
  • Features of Python
  • Features of PySpark
  • Resilient Distributed Datasets Framework
  • Spark API Operators
  • Python with Spark
  • Using Amazon Web Services (AWS) EC2 Instances for Spark
  • Setting Up Databricks
  • Setting Up the AWS EMR Cluster
  • Basics of Python Programming
  • Working on a Spark DataFrame Project Exercise
  • Machine Learning with MLlib
  • Random Forests and Decision Trees
  • K-means Clustering
  • Recommender Systems
  • Implementing Natural Language Processing
  • Streaming with Spark on Python

21 hours

¥862,794


学習中Big Data 難しい?


Big Dataの分野で ライブのインストラクター主導の実践的なトレーニングコースから学ぶことは、ビデオ学習資料を見ることに比べて大きな違いがあります。 参加者は、集中力を維持し、質問や懸念についてトレーナーと対話する必要があります。 Qwikcourseでは、トレーナーと参加者は を使用します DaDesktop は、離れた場所からインタラクティブな実践的なトレーニングを実行したいインストラクターや学生向けに設計されたクラウドデスクトップ環境です。


Big Dataです 良い分野?


今のところ、さまざまなIT分野に多大な仕事の機会があります。 Big Data のほとんどのコース は、ポートフォリオに大きく貢献する可能性のある実践的なトレーニングと経験を備えたIT学習の優れた情報源です。



Big Data オンラインコース, Big Data トレーニング, Big Data インストラクター主導, Big Data ライブトレーナー, Big Data トレーナー, Big Data オンラインレッスン, Big Data 教育