Big Data Training Courses

Python е скалиращ, гъвкав и широко използван език за програмиране за компютърна наука и машинно обучение. Spark е двигател за обработка на данни, използван за търсене, анализ и трансформация на големи данни, докато Hadoop е софтуерна библиотека рамка за съхранение и обработка на данни в голям мащаб.

Това обучение, ръководено от инструктори, е насочено към разработчици, които искат да използват и интегрират Spark, Hadoop, и Python за обработка, анализ и трансформация на големи и сложни набори от данни.

В края на обучението участниците ще могат да:

Създайте необходимата среда, за да започнете обработката на големи данни с Spark, Hadoop, и Python. Разберете характеристиките, основните компоненти и архитектурата на Spark и Hadoop. Научете как да интегрирате Spark, Hadoop, и Python за обработка на големи данни. Разгледайте инструментите в екосистемата Spark (Spark MlLib, Spark Streaming, Kafka, Sqoop, Kafka и Flume). Създайте съвместни системи за филтриране, подобни на Netflix, YouTube, Amazon, Spotify и Google. Използвайте Apache Mahout, за да скалирате алгоритмите за машинно обучение.

Формат на курса

Интерактивна лекция и дискусия. Много упражнения и упражнения. Изпълнение на ръката в живо лабораторна среда.

Опции за персонализиране на курса

За да поискате персонализирано обучение за този курс, моля, свържете се с нас, за да организирате.

Waikato Environment for Knowledge Analysis (Weka) е софтуер за визуализация за минни данни с отворен код. Той осигурява колекция от алгоритми за машинно обучение за подготовка на данни, класификация, кластериране и други дейности за извличане на данни.

Това обучение, ръководено от инструктори, на живо (онлайн или онлайн) е насочено към аналитици на данни и учени на данни, които искат да използват Weka за изпълнение на задачи за изкопаване на данни.

В края на обучението участниците ще могат да:

Настройване и конфигуриране Weka Разбиране на Weka околната среда и работното място. Извършване на задачи за извличане на данни с помощта на Weka.

Формат на курса

Интерактивна лекция и дискусия. Много упражнения и упражнения. Изпълнение на ръката в живо лабораторна среда.

Опции за персонализиране на курса

За да поискате персонализирано обучение за този курс, моля, свържете се с нас, за да организирате.

IBM SPSS Modeler е софтуер, използван за извличане на данни и текстови анализи. Той осигурява набор от инструменти за извличане на данни, които могат да изграждат предсказуеми модели и да изпълняват задачи за анализ на данни.

Това обучение, ръководено от инструктори (онлайн или онлайн) е насочено към анализатори на данни или всеки, който иска да използва SPSS Modeler за извършване на дейности за извличане на данни.

В края на обучението участниците ще могат да:

Разбиране на основните принципи на данните. Научете как да импортирате и оценявате качеството на данните с Моделира. Разработване, внедряване и оценка на моделите на данни ефективно.

Формат на курса

Интерактивна лекция и дискусия. Много упражнения и упражнения. Изпълнение на ръката в живо лабораторна среда.

Опции за персонализиране на курса

За да поискате персонализирано обучение за този курс, моля, свържете се с нас, за да организирате.

Участниците, които завършват това обучение, ръководено от инструктор, ще получат практически, реално разбиране на Big Data и свързаните с него технологии, методики и инструменти.

Участниците ще имат възможност да приложат това знание на практика чрез практикуващи упражнения. Груповото взаимодействие и обратната връзка на инструкторите са важен компонент на класа.

Курсът започва с въведение в елементарните концепции на Big Data, а след това напредва в езиците за програмиране и методологиите, използвани за изпълнение Data Analysis. Накрая, ние обсъждаме инструментите и инфраструктурата, които позволяват Big Data съхранение, дистрибутирана обработка и Scala устойчивост.

Формат на курса

Частна лекция, частна дискусия, практическа практика и изпълнение, понякога изпитване за измерване на напредъка.

In this instructor-led, live training in България, 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.

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

Откриването на знания в бази данни (KDD) е процес на откриване на полезни знания от събиране на данни. Реалните приложения за тази техника на извличане на данни включват маркетинг, откриване на измами, телекомуникации и производство.

В този курс, ръководен от инструктори, ние въвеждаме процесите, участващи в KDD и провеждаме серия от упражнения, за да практикуваме прилагането на тези процеси.

публиката

Аналитиците на данни или всеки, който се интересува от изучаването на интерпретацията на данните за решаване на проблеми

Формат на курса

След теоретична дискусия на KDD, инструкторът ще представи реални случаи, които изискват прилагането на KDD за решаване на проблем. Участниците ще подготвят, избират и почистват набор от данни за проби и ще използват своите предишни познания за данните, за да предложат решения въз основа на резултатите от техните наблюдения.

Apache Kylin е екстремен, разпределен аналитичен двигател за големи данни.

В това обучение, ръководено от инструктори, участниците ще научат как да използват Apache Kylin за създаване на съхранение на данни в реално време.

В края на обучението участниците ще могат да:

Консумиране на данни в реално време с помощта на Kylin Използвайте мощни функции Apache Kylin's, богат SQL интерфейс, спарк кубиране и вторична латентност за запитване

Забележка

Ние използваме най-новата версия на Кайлин (по този текст, Apache Kylin v2.0)

публиката

Big Data инженери [ 0 ] Аналитиците

Формат на курса

Частна лекция, частна дискусия, упражнения и тежка практика

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

This instructor-led, live training in България (online or onsite) is aimed at data scientists who wish to use Excel for data mining.

- By the end of this training, participants will be able to:
- Explore data with Excel to perform data mining and analysis.
- Use Microsoft algorithms for data mining.
- Understand concepts in Excel data mining.

Dremio е платформа за данни с отворен код, която ускорява търсенето на различни видове източници на данни. Dremio интегрира с относителни бази данни, Apache Hadoop, MongoDB, Amazon S3, ElasticSearch, и други източници на данни. Той поддържа SQL и осигурява уеб УИ за строителни запитвания.

В това обучение, ръководено от инструктори, участниците ще научат как да инсталирате, конфигурирате и използвате Dremio като унифициращ слой за инструменти за анализ на данни и основните данни.

В края на обучението участниците ще могат да:

Инсталиране и конфигуриране Dremio Извършване на запитвания срещу множество източници на данни, независимо от местоположението, размера или структурата Интегрирайте Dremio с BI и източници на данни като Tableau и Elasticsearch

публиката

Данни учени Business Аналитиците Данни инженери

Формат на курса

Частна лекция, частна дискусия, упражнения и тежка практика

Забележки

За да поискате персонализирано обучение за този курс, моля, свържете се с нас, за да организирате.

The objective of the course is to enable participants to gain a mastery of how to work with the SQL language in Oracle database for data extraction at intermediate level.

Apache Drill is a schema-free, distributed, in-memory columnar SQL query engine for Hadoop, NoSQL and other Cloud and file storage systems. The power of Apache Drill lies in its ability to join data from multiple data stores using a single query. Apache Drill supports numerous NoSQL databases and file systems, including HBase, MongoDB, MapR-DB, HDFS, MapR-FS, Amazon S3, Azure Blob Storage, Google Cloud Storage, Swift, NAS and local files. Apache Drill is the open source version of Google's Dremel system which is available as an infrastructure service called Google BigQuery.

In this instructor-led, live training, participants will learn the fundamentals of Apache Drill, then leverage the power and convenience of SQL to interactively query big data across multiple data sources, without writing code. Participants will also learn how to optimize their Drill queries for distributed SQL execution.

By the end of this training, participants will be able to:

- Perform "self-service" exploration on structured and semi-structured data on Hadoop
- Query known as well as unknown data using SQL queries
- Understand how Apache Drills receives and executes queries
- Write SQL queries to analyze different types of data, including structured data in Hive, semi-structured data in HBase or MapR-DB tables, and data saved in files such as Parquet and JSON.
- Use Apache Drill to perform on-the-fly schema discovery, bypassing the need for complex ETL and schema operations
- Integrate Apache Drill with BI (Business Intelligence) tools such as Tableau, Qlikview, MicroStrategy and Excel

Audience

- Data analysts
- Data scientists
- SQL programmers

Format of the course

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

Apache Arrow is an open-source in-memory data processing framework. It is often used together with other data science tools for accessing disparate data stores for analysis. It integrates well with other technologies such as GPU databases, machine learning libraries and tools, execution engines, and data visualization frameworks.

In this onsite instructor-led, live training, participants will learn how to integrate Apache Arrow with various Data Science frameworks to access data from disparate data sources.

By the end of this training, participants will be able to:

- Install and configure Apache Arrow in a distributed clustered environment
- Use Apache Arrow to access data from disparate data sources
- Use Apache Arrow to bypass the need for constructing and maintaining complex ETL pipelines
- Analyze data across disparate data sources without having to consolidate it into a centralized repository

Audience

- Data scientists
- 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.

Advances in technologies and the increasing amount of information are transforming how business is conducted in many industries, including government. Government data generation and digital archiving rates are on the rise due to the rapid growth of mobile devices and applications, smart sensors and devices, cloud computing solutions, and citizen-facing portals. As digital information expands and becomes more complex, information management, processing, storage, security, and disposition become more complex as well. New capture, search, discovery, and analysis tools are helping organizations gain insights from their unstructured data. The government market is at a tipping point, realizing that information is a strategic asset, and government needs to protect, leverage, and analyze both structured and unstructured information to better serve and meet mission requirements. As government leaders strive to evolve data-driven organizations to successfully accomplish mission, they are laying the groundwork to correlate dependencies across events, people, processes, and information.

High-value government solutions will be created from a mashup of the most disruptive technologies:

- Mobile devices and applications
- Cloud services
- Social business technologies and networking
- Big Data and analytics

IDC predicts that by 2020, the IT industry will reach $5 trillion, approximately $1.7 trillion larger than today, and that 80% of the industry's growth will be driven by these 3rd Platform technologies. In the long term, these technologies will be key tools for dealing with the complexity of increased digital information. Big Data is one of the intelligent industry solutions and allows government to make better decisions by taking action based on patterns revealed by analyzing large volumes of data — related and unrelated, structured and unstructured.

But accomplishing these feats takes far more than simply accumulating massive quantities of data.“Making sense of thesevolumes of Big Datarequires cutting-edge tools and technologies that can analyze and extract useful knowledge from vast and diverse streams of information,” Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote in a post on the OSTP Blog.

The White House took a step toward helping agencies find these technologies when it established the National Big Data Research and Development Initiative in 2012. The initiative included more than $200 million to make the most of the explosion of Big Data and the tools needed to analyze it.

The challenges that Big Data poses are nearly as daunting as its promise is encouraging. Storing data efficiently is one of these challenges. As always, budgets are tight, so agencies must minimize the per-megabyte price of storage and keep the data within easy access so that users can get it when they want it and how they need it. Backing up massive quantities of data heightens the challenge.

Analyzing the data effectively is another major challenge. Many agencies employ commercial tools that enable them to sift through the mountains of data, spotting trends that can help them operate more efficiently. (A recent study by MeriTalk found that federal IT executives think Big Data could help agencies save more than $500 billion while also fulfilling mission objectives.).

Custom-developed Big Data tools also are allowing agencies to address the need to analyze their data. For example, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has helped medical researchers find a link that can alert doctors to aortic aneurysms before they strike. It’s also used for more mundane tasks, such as sifting through résumés to connect job candidates with hiring managers.

Audience

If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you.

It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing.

It is not aimed at people configuring the solution, those people will benefit from the big picture though.

Delivery Mode

During the course delegates will be presented with working examples of mostly open source technologies.

Short lectures will be followed by presentation and simple exercises by the participants

Content and Software used

All software used is updated each time the course is run, so we check the newest versions possible.

It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.

Day 1 - provides a high-level overview of essential Big Data topic areas. The module is divided into a series of sections, each of which is accompanied by a hands-on exercise.

Day 2 - explores a range of topics that relate analysis practices and tools for Big Data environments. It does not get into implementation or programming details, but instead keeps coverage at a conceptual level, focusing on topics that enable participants to develop a comprehensive understanding of the common analysis functions and features offered by Big Data solutions.

Day 3 - provides an overview of the fundamental and essential topic areas relating to Big Data solution platform architecture. It covers Big Data mechanisms required for the development of a Big Data solution platform and architectural options for assembling a data processing platform. Common scenarios are also presented to provide a basic understanding of how a Big Data solution platform is generally used.

Day 4 - builds upon Day 3 by exploring advanced topics relatng to Big Data solution platform architecture. In particular, different architectural layers that make up the Big Data solution platform are introduced and discussed, including data sources, data ingress, data storage, data processing and security.

Day 5 - covers a number of exercises and problems designed to test the delegates ability to apply knowledge of topics covered Day 3 and 4.

Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry.

When traditional storage technologies don't handle the amount of data you need to store there are hundereds of alternatives. This course try to guide the participants what are alternatives for storing and analyzing Big Data and what are theirs pros and cons.

This course is mostly focused on discussion and presentation of solutions, though hands-on exercises are available on demand.

The course is part of the Data Scientist skill set (Domain: Data and Technology).

Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.

Vespa is an open-source big data processing and serving engine created by Yahoo. It is used to respond to user queries, make recommendations, and provide personalized content and advertisements in real-time.

This instructor-led, live training introduces the challenges of serving large-scale data and walks participants through the creation of an application that can compute responses to user requests, over large datasets in real-time.

By the end of this training, participants will be able to:

- Use Vespa to quickly compute data (store, search, rank, organize) at serving time while a user waits
- Implement Vespa into existing applications involving feature search, recommendations, and personalization
- Integrate and deploy Vespa with existing big data systems such as Hadoop and Storm.

Audience

- Developers

Format of the course

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

To meet compliance of the regulators, CSPs (Communication service providers) can tap into Big Data Analytics which not only help them to meet compliance but within the scope of same project they can increase customer satisfaction and thus reduce the churn. In fact since compliance is related to Quality of service tied to a contract, any initiative towards meeting the compliance, will improve the “competitive edge” of the CSPs. Therefore, it is important that Regulators should be able to advise/guide a set of Big Data analytic practice for CSPs that will be of mutual benefit between the regulators and CSPs.

The course consists of 8 modules (4 on day 1, and 4 on day 2)

Advances in technologies and the increasing amount of information are transforming how law enforcement is conducted. The challenges that Big Data pose are nearly as daunting as Big Data's promise. Storing data efficiently is one of these challenges; effectively analyzing it is another.

In this instructor-led, live training, participants will learn the mindset with which to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement organizations around the world will be examined to gain insights on their adoption approaches, challenges and results.

By the end of this training, participants will be able to:

- Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation
- Implement industrial big data storage and processing solutions for data analysis
- Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation

Audience

- Law Enforcement specialists with a technical background

Format of the course

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

This classroom based training session will explore Big Data. Delegates will have computer based examples and case study exercises to undertake with relevant big data tools

Objective : This training course aims at helping attendees understand why Big Data is changing our lives and how it is altering the way businesses see us as consumers. Indeed, users of big data in businesses find that big data unleashes a wealth of information and insights which translate to higher profits, reduced costs, and less risk. However, the downside was frustration sometimes when putting too much emphasis on individual technologies and not enough focus on the pillars of big data management.

Attendees will learn during this course how to manage the big data using its three pillars of data integration, data governance and data security in order to turn big data into real business value. Different exercices conducted on a case study of customer management will help attendees to better understand the underlying processes.

This instructor-led, live training in България (online or onsite) is aimed at technical persons who wish to learn how to implement a machine learning strategy while maximizing the use of big data.

By the end of this training, participants will:

- Understand the evolution and trends for machine learning.
- Know how machine learning is being used across different industries.
- Become familiar with the tools, skills and services available to implement machine learning within an organization.
- Understand how machine learning can be used to enhance data mining and analysis.
- Learn what a data middle backend is, and how it is being used by businesses.
- Understand the role that big data and intelligent applications are playing across industries.

Apache Sqoop е интерфейс на командния ред за преместване на данни от релационни бази данни и Hadoop. Apache Flume е разпределен софтуер за управление на големи масиви от данни. Използвайки Sqoop и Flume, потребителите могат да прехвърлят данни между системите и да внасят големи данни в архитектури за съхранение като Hadoop.

Това обучение на инструктори, провеждано на живо (на място или дистанционно), е насочено към софтуерни инженери, които желаят да използват Sqoop и Flume за прехвърляне на данни между системите.

До края на обучението участниците ще могат да:

- Погълнати големи данни с Sqop и Flume.
- Приемаданни от множество източници на данни.
- Преместване на данни от релационни бази данни в HDFS и Hive.
- Експортиране на данни от HDFS към релационна база данни.

Формат на курса

- Интерактивна лекция и дискусия.
- Много упражнения и упражнения.
- Практически изпълнение в жива лаборатория среда.

Опции за персонализиране на курса

- За да заявите персонализирано обучение за този курс, моля, свържете се с нас, за да организирате.

Talend Open Studio за Big Data е отворен код ETL инструмент за обработка на големи данни. Тя включва среда за развитие, за да взаимодейства с Big Data източници и цели и да изпълнява работни места, без да се налага да пише код.

Това обучение, ръководено от инструктори, на живо (онлайн или онлайн) е насочено към технически лица, които искат да използват Talend Open Studio за Big Data за опростяване на процеса на четене и разширяване чрез Big Data.

В края на обучението участниците ще могат да:

Инсталирайте и конфигурирайте Talend Open Studio за Big Data. Свържете се с Big Data системи като Cloudera, HortonWorks, MapR, Amazon EMR и Apache. Разберете и инсталирайте големите компоненти на данни и конекторите на Open Studio. Конфигуриране на параметри за автоматично генериране на код MapReduce. Използвайте интерфейса за изтегляне и изтегляне на Open Studio's, за да изпълните Hadoop работни места. Прототип на големите тръби за данни. Проекти за автоматизиране на големите данни.

Формат на курса

Интерактивна лекция и дискусия. Много упражнения и упражнения. Изпълнение на ръката в живо лабораторна среда.

Опции за персонализиране на курса

За да поискате персонализирано обучение за този курс, моля, свържете се с нас, за да организирате.

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