IBM Datastage For Administrators and Developers - Plan Szkolenia

Data Vault Modeling is a database modeling technique that provides long-term historical storage of data that originates from multiple sources. A data vault stores a single version of the facts, or "all the data, all the time". Its flexible, scalable, consistent and adaptable design encompasses the best aspects of 3rd normal form (3NF) and star schema.

In this instructor-led, live training, participants will learn how to build a Data Vault.

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

• Understand the architecture and design concepts behind Data Vault 2.0, and its interaction with Big Data, NoSQL and AI.
• Use data vaulting techniques to enable auditing, tracing, and inspection of historical data in a data warehouse.
• Develop a consistent and repeatable ETL (Extract, Transform, Load) process.
• Build and deploy highly scalable and repeatable warehouses.

Format of the course

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

Amazon Redshift is a petabyte-scale cloud-based data warehouse service in AWS.

In this instructor-led, live training, participants will learn the fundamentals of Amazon Redshift.

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

• Install and configure Amazon Redshift
• Load, configure, deploy, query, and visualize data with Amazon Redshift

Audience

• Developers
• IT Professionals

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

Azure Data Lake Storage Gen2 is a comprehensive and highly scalable data storage service offered by Microsoft Azure that provides a platform for analytics workloads.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data engineers who wish to learn how to use Azure Data Lake Storage Gen2 for effective data analytics solutions.

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

• Understand the architecture and key features of Azure Data Lake Storage Gen2.
• Optimize data storage and access for cost and performance.
• Integrate Azure Data Lake Storage Gen2 with other Azure services for analytics and data processing.
• Develop solutions using the Azure Data Lake Storage Gen2 API.
• Troubleshoot common issues and optimize storage strategies.

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.

Apache Druid is an open-source, column-oriented, distributed data store written in Java. It was designed to quickly ingest massive quantities of event data and execute low-latency OLAP queries on that data. Druid is commonly used in business intelligence applications to analyze high volumes of real-time and historical data. It is also well suited for powering fast, interactive, analytic dashboards for end-users. Druid is used by companies such as Alibaba, Airbnb, Cisco, eBay, Netflix, Paypal, and Yahoo.

In this instructor-led, live course we explore some of the limitations of data warehouse solutions and discuss how Druid can compliment those technologies to form a flexible and scalable streaming analytics stack. We walk through many examples, offering participants the chance to implement and test Druid-based solutions in a lab environment.

Format of the Course

• Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding

Pivotal Greenplum is a Massively Parallel Processing (MPP) Data Warehouse platform based on PostgreSQL.

This instructor-led, live training (online or onsite) is aimed at developers who wish to set up a multi-node Greenplum database.

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

• Install and configure Pivotal Greenplum.
• Model data in accordance to current needs and future expansion plans.
• Carry out different techniques for distributing data across multiple nodes.
• Improve database performance through tuning.
• Monitor and troubleshoot a Greenplum database.

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.

Greenplum Database is a database software for business intelligence and data warehousing. Users can run Greenplum Database for massive parallel data processing.

This instructor-led, live training (online or onsite) is aimed at administrators who wish to set up Greenplum Database for business intelligence and data warehousing solutions.

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

• Address processing needs with Greenplum.
• Perform ETL operations for data processing.
• Leverage existing query processing infrastructures.

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.

Apache Kylin is an extreme, distributed analytics engine for big data.

In this instructor-led live training, participants will learn how to use Apache Kylin to set up a real-time data warehouse.

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

• Consume real-time streaming data using Kylin
• Utilize Apache Kylin's powerful features, rich SQL interface, spark cubing and subsecond query latency

Note

• We use the latest version of Kylin (as of this writing, Apache Kylin v2.0)

Audience

• Big data engineers
• Big Data analysts

Format of the course

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

MemSQL is an in-memory, distributed, SQL database management system for cloud and on-premises. It's a real-time data warehouse that immediately delivers insights from live and historical data.

In this instructor-led, live training, participants will learn the essentials of MemSQL for development and administration.

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

• Understand the key concepts and characteristics of MemSQL
• Install, design, maintain, and operate MemSQL
• Optimize schemas in MemSQL
• Improve queries in MemSQL
• Benchmark performance in MemSQL
• Build real-time data applications using MemSQL

Audience

• Developers
• Administrators
• Operation Engineers

Format of the course

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

Snowflake to platforma przechowywania danych w chmurze, która oferuje szerokie możliwości przetwarzania i analizy dużych zbiorów danych. Jest to rozwiązanie typu Software-as-a-Service (SaaS), które łączy w sobie elastyczność i skalowalność chmury z możliwościami zaawansowanego magazynu danych. Kluczowe cechy i funkcje Snowflake obejmują:

Architektura oparta na chmurze: Snowflake jest zbudowany w chmurze i zoptymalizowany pod kątem elastyczności i wydajności, co pozwala na łatwą skalowalność zasobów do przechowywania i przetwarzania danych.

Rozdzielenie obciążenia magazynowania i przetwarzania: Unikalna architektura Snowflake rozdziela obciążenie magazynowania danych od obciążenia przetwarzania, co pozwala na niezależne skalowanie i optymalizację tych dwóch aspektów.

Wsparcie dla różnorodnych danych: Snowflake umożliwia przechowywanie i analizę różnorodnych typów danych, w tym danych strukturalnych, półstrukturalnych (np. JSON, XML) i niestrukturalnych.

Wysoka wydajność i optymalizacja zapytań: System oferuje szybkie przetwarzanie zapytań i automatyczną optymalizację, co pozwala na efektywne wykonywanie złożonych analiz na dużych zbiorach danych.

Zabezpieczenia i zarządzanie dostępem: Snowflake zapewnia zaawansowane funkcje bezpieczeństwa, w tym szyfrowanie danych, zarządzanie dostępem na poziomie ról i audytowanie.

Łatwość integracji: Platforma łatwo integruje się z popularnymi narzędziami i usługami ETL, BI oraz analizy danych, co pozwala na tworzenie kompleksowych rozwiązań analitycznych.

Elastyczność i skalowalność kosztów: Model płatności oparty na użyciu pozwala na optymalizację kosztów i płacenie tylko za faktycznie wykorzystane zasoby.

Uniwersalność zastosowań: Snowflake jest odpowiedni dla różnych przypadków użycia, od prostych analiz BI po zaawansowane przetwarzanie danych, w tym analizy big data, machine learning i inne.

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 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.

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. 

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

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.