Explore Our Courses

Advanced Apache Iceberg

 21 時間
Apache Iceberg is an open-source table format for large-scale data sets that brings the reliability and simplicity of SQL tables to big data. It was designed to solve the challenges of managing big data in data lakes, which often involve handling complex schemas, large files, and diverse data sources.This instructor-led, live training (online or onsite) is aimed at advanced-level data professionals who wish to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions that can handle the complexities of modern big data applications.By the end of this training, participants will be able to:
  • Gain an in-depth understanding of Iceberg’s architecture, including metadata management and file layout.
  • Configure Iceberg for optimal performance in various environments and integrate it with multiple data processing engines.
  • Manage large-scale Iceberg tables, perform complex schema changes, and handle partition evolution.
  • Master techniques to optimize query performance and data scan efficiency for large datasets.
  • Implement mechanisms to ensure data consistency, manage transactional guarantees, and handle failures in distributed environments.
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.
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Apache Iceberg Fundamentals

 14 時間
Apache Iceberg is an open-source table format for large-scale data sets that brings the reliability and simplicity of SQL tables to big data. It was designed to solve the challenges of managing big data in data lakes, which often involve handling complex schemas, large files, and diverse data sources.This instructor-led, live training (online or onsite) is aimed at beginner-level data professionals who wish to acquire the knowledge and skills necessary to effectively utilize Apache Iceberg for managing large-scale datasets, ensuring data integrity, and optimizing data processing workflows.By the end of this training, participants will be able to:
  • Gain a thorough understanding of Apache Iceberg's architecture, features, and benefits.
  • Learn about table formats, partitioning, schema evolution, and time travel capabilities.
  • Install and configure Apache Iceberg in different environments.
  • Create, manage, and manipulate of Iceberg tables.
  • Understand the process of migrating data from other table formats to Iceberg.
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.
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Big Data Consulting

 21 時間
Big Data Consulting refers to the services provided by experts who help organizations manage, analyze, and utilize large and complex data sets to improve their business operations and decision-making processes.This instructor-led, live training (online or onsite) is aimed at intermediate-level IT professionals who wish to enhance their skills in data architecture, governance, cloud computing, and big data technologies to effectively manage and analyze large datasets for data migration within their organizations.By the end of this training, participants will be able to:
  • Understand the foundational concepts and components of various data architectures.
  • Gain a comprehensive understanding of data governance principles and their importance in regulatory environments.
  • Implement and manage data governance frameworks such as Dama and Togaf.
  • Leverage cloud platforms for efficient data storage, processing, and management.
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.
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Azure Data Lake Storage Gen2

 14 時間
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.
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IBM Datastage For Administrators and Developers

 35 時間
IBM DataStage is a powerful extract, transform, load (ETL) tool used in data warehousing and business intelligence that helps organizations integrate and transform large volumes of data, from various data sources, into a unified format.This instructor-led, live training (online or onsite) is aimed at intermediate-level IT professionals who wish to have a comprehensive understanding of IBM DataStage from both an administrative and a development perspective, allowing them to manage and utilize this tool effectively in their respective workplaces.By the end of this training, participants will be able to:
  • Understand the core concepts of DataStage.
  • Learn how to effectively install, configure, and manage DataStage environments.
  • Connect to various data sources and extract data efficiently from databases, flat files, and external sources.
  • Implement effective data loading techniques.
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.
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Python and Spark for Big Data (PySpark)

 21 時間
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 cases.
  • 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
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Data Analysis with Hive/HiveQL

 7 時間
This course covers how to use Hive SQL language (AKA: Hive HQL, SQL on Hive, HiveQL) for people who extract data from Hive
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Knowledge Discovery in Databases (KDD)

 21 時間
Knowledge discovery in databases (KDD) is the process of discovering useful knowledge from a collection of data. Real-life applications for this data mining technique include marketing, fraud detection, telecommunication and manufacturing. In this instructor-led, live course, we introduce the processes involved in KDD and carry out a series of exercises to practice the implementation of those processes. Audience
  • Data analysts or anyone interested in learning how to interpret data to solve problems
Format of the Course
  • After a theoretical discussion of KDD, the instructor will present real-life cases which call for the application of KDD to solve a problem. Participants will prepare, select and cleanse sample data sets and use their prior knowledge about the data to propose solutions based on the results of their observations.
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Apache Kylin: From Classic OLAP to Real-Time Data Warehouse

 14 時間
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
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Dremio for Self-Service Data Analysis

 21 時間
Dremio is an open-source "self-service data platform" that accelerates the querying of different types of data sources. Dremio integrates with relational databases, Apache Hadoop, MongoDB, Amazon S3, ElasticSearch, and other data sources. It supports SQL and provides a web UI for building queries. In this instructor-led, live training, participants will learn how to install, configure and use Dremio as a unifying layer for data analysis tools and the underlying data repositories. By the end of this training, participants will be able to:
  • Install and configure Dremio
  • Execute queries against multiple data sources, regardless of location, size, or structure
  • Integrate Dremio with BI and data sources such as Tableau and Elasticsearch
Audience
  • Data scientists
  • Business analysts
  • 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.
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Apache Arrow for Data Analysis across Disparate Data Sources

 14 時間
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.
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Big Data Business Intelligence for Govt. Agencies

 35 時間
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.
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From Data to Decision with Big Data and Predictive Analytics

 21 時間
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.
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Big Data Architect

 35 時間
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. 
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Programming with Big Data in R

 21 時間
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.
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Big Data Storage Solution - NoSQL

 14 時間
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.
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Big Data & Database Systems Fundamentals

 14 時間
The course is part of the Data Scientist skill set (Domain: Data and Technology).
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Data Science for Big Data Analytics

 35 時間
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.
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Big Data Analytics for Telecom Regulators

 14 時間
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)
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Big Data Business Intelligence for Criminal Intelligence Analysis

 35 時間
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
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Big Data - Data Science

 14 時間
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
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Machine Learning and Big Data

 7 時間
This instructor-led, live training (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.
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.
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Sqoop and Flume for Big Data

 7 時間
Apache Sqoop is a command line interface for moving data from relational databases and Hadoop. Apache Flume is a distributed software for managing big data. Using Sqoop and Flume, users can transfer data between systems and import big data into storage architectures such as Hadoop. This instructor-led, live training (online or onsite) is aimed at software engineers who wish to use Sqoop and Flume for transferring data between systems. By the end of this training, participants will be able to:
  • Ingest big data with Sqoop and Flume.
  • Ingest data from multiple data sources.
  • Move data from relational databases to HDFS and Hive.
  • Export data from HDFS to a relational 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.
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Talend Big Data Integration

 28 時間
Talend Open Studio for Big Data is an open source ETL tool for processing big data. It includes a development environment to interact with Big Data sources and targets, and run jobs without having to write code. This instructor-led, live training (online or onsite) is aimed at technical persons who wish to deploy Talend Open Studio for Big Data to simplifying the process of reading and crunching through Big Data. By the end of this training, participants will be able to:
  • Install and configure Talend Open Studio for Big Data.
  • Connect with Big Data systems such as Cloudera, HortonWorks, MapR, Amazon EMR and Apache.
  • Understand and set up Open Studio's big data components and connectors.
  • Configure parameters to automatically generate MapReduce code.
  • Use Open Studio's drag-and-drop interface to run Hadoop jobs.
  • Prototype big data pipelines.
  • Automate big data integration projects.
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.
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Data Vault: Building a Scalable Data Warehouse

 28 時間
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
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Spark Streaming with Python and Kafka

 7 時間
Apache Spark Streaming is a scalable, open source stream processing system that allows users to process real-time data from supported sources. Spark Streaming enables fault-tolerant processing of data streams. This instructor-led, live training (online or onsite) is aimed at data engineers, data scientists, and programmers who wish to use Spark Streaming features in processing and analyzing real-time data. By the end of this training, participants will be able to use Spark Streaming to process live data streams for use in databases, filesystems, and live dashboards. 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.
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Confluent KSQL

 7 時間
Confluent KSQL is a stream processing framework built on top of Apache Kafka. It enables real-time data processing using SQL operations. This instructor-led, live training (online or onsite) is aimed at developers who wish to implement Apache Kafka stream processing without writing code. By the end of this training, participants will be able to:
  • Install and configure Confluent KSQL.
  • Set up a stream processing pipeline using only SQL commands (no Java or Python coding).
  • Carry out data filtering, transformations, aggregations, joins, windowing, and sessionization entirely in SQL.
  • Design and deploy interactive, continuous queries for streaming ETL and real-time analytics.
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.
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Apache Ignite for Developers

 14 時間
Apache Ignite is an in-memory computing platform that sits between the application and data layer to improve speed, scale, and availability. This instructor-led, live training (online or onsite) is aimed at developers who wish to learn the principles behind persistent and pure in-memory storage as they step through the creation of a sample in-memory computing project. By the end of this training, participants will be able to:
  • Use Ignite for in-memory, on-disk persistence as well as a purely distributed in-memory database.
  • Achieve persistence without syncing data back to a relational database.
  • Use Ignite to carry out SQL and distributed joins.
  • Improve performance by moving data closer to the CPU, using RAM as a storage.
  • Spread data sets across a cluster to achieve horizontal scalability.
  • Integrate Ignite with RDBMS, NoSQL, Hadoop and machine learning processors.
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.
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Unified Batch and Stream Processing with Apache Beam

 14 時間
Apache Beam is an open source, unified programming model for defining and executing parallel data processing pipelines. It's power lies in its ability to run both batch and streaming pipelines, with execution being carried out by one of Beam's supported distributed processing back-ends: Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Apache Beam is useful for ETL (Extract, Transform, and Load) tasks such as moving data between different storage media and data sources, transforming data into a more desirable format, and loading data onto a new system. In this instructor-led, live training (onsite or remote), participants will learn how to implement the Apache Beam SDKs in a Java or Python application that defines a data processing pipeline for decomposing a big data set into smaller chunks for independent, parallel processing. By the end of this training, participants will be able to:
  • Install and configure Apache Beam.
  • Use a single programming model to carry out both batch and stream processing from withing their Java or Python application.
  • Execute pipelines across multiple environments.
Format of the Course
  • Part lecture, part discussion, exercises and heavy hands-on practice
Note
  • This course will be available Scala in the future. Please contact us to arrange.
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Apache Apex: Processing Big Data-in-Motion

 21 時間
Apache Apex is a YARN-native platform that unifies stream and batch processing. It processes big data-in-motion in a way that is scalable, performant, fault-tolerant, stateful, secure, distributed, and easily operable. This instructor-led, live training introduces Apache Apex's unified stream processing architecture, and walks participants through the creation of a distributed application using Apex on Hadoop. By the end of this training, participants will be able to:
  • Understand data processing pipeline concepts such as connectors for sources and sinks, common data transformations, etc.
  • Build, scale and optimize an Apex application
  • Process real-time data streams reliably and with minimum latency
  • Use Apex Core and the Apex Malhar library to enable rapid application development
  • Use the Apex API to write and re-use existing Java code
  • Integrate Apex into other applications as a processing engine
  • Tune, test and scale Apex applications
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.
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Apache Storm

 28 時間
Apache Storm is a distributed, real-time computation engine used for enabling real-time business intelligence. It does so by enabling applications to reliably process unbounded streams of data (a.k.a. stream processing). "Storm is for real-time processing what Hadoop is for batch processing!" In this instructor-led live training, participants will learn how to install and configure Apache Storm, then develop and deploy an Apache Storm application for processing big data in real-time. Some of the topics included in this training include:
  • Apache Storm in the context of Hadoop
  • Working with unbounded data
  • Continuous computation
  • Real-time analytics
  • Distributed RPC and ETL processing
Request this course now! Audience
  • Software and ETL developers
  • Mainframe professionals
  • Data scientists
  • Big data analysts
  • Hadoop professionals
Format of the course
  •     Part lecture, part discussion, exercises and heavy hands-on practice
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Apache NiFi for Administrators

 21 時間
Apache NiFi (Hortonworks DataFlow) is a real-time integrated data logistics and simple event processing platform that enables the moving, tracking and automation of data between systems. It is written using flow-based programming and provides a web-based user interface to manage dataflows in real time. In this instructor-led, live training (onsite or remote), participants will learn how to deploy and manage Apache NiFi in a live lab environment. By the end of this training, participants will be able to:
  • Install and configure Apachi NiFi.
  • Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes.
  • Automate dataflows.
  • Enable streaming analytics.
  • Apply various approaches for data ingestion.
  • Transform Big Data and into business insights.
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.
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Apache NiFi for Developers

 7 時間
Apache NiFi (Hortonworks DataFlow) is a real-time integrated data logistics and simple event processing platform that enables the moving, tracking and automation of data between systems. It is written using flow-based programming and provides a web-based user interface to manage dataflows in real time. In this instructor-led, live training, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi. By the end of this training, participants will be able to:
  • Understand NiFi's architecture and dataflow concepts.
  • Develop extensions using NiFi and third-party APIs.
  • Custom develop their own Apache Nifi processor.
  • Ingest and process real-time data from disparate and uncommon file formats and data sources.
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.
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Apache Flink Fundamentals

 28 時間
Apache Flink is an open-source framework for scalable stream and batch data processing. This instructor-led, live training (online or onsite) introduces the principles and approaches behind distributed stream and batch data processing, and walks participants through the creation of a real-time, data streaming application in Apache Flink. By the end of this training, participants will be able to:
  • Set up an environment for developing data analysis applications.
  • Understand how Apache Flink's graph-processing library (Gelly) works.
  • Package, execute, and monitor Flink-based, fault-tolerant, data streaming applications.
  • Manage diverse workloads.
  • Perform advanced analytics.
  • Set up a multi-node Flink cluster.
  • Measure and optimize performance.
  • Integrate Flink with different Big Data systems.
  • Compare Flink capabilities with those of other big data processing frameworks.
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.
続きを読む...

Introduction to Graph Computing

 28 時間
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 and Distributed Graph Processing) 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
続きを読む...

Apache Spark MLlib

 35 時間
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
続きを読む...

Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP

 21 時間
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.
続きを読む...

Hortonworks Data Platform (HDP) for Administrators

 21 時間
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 (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.
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.
続きを読む...

Apache Ambari: Efficiently Manage Hadoop Clusters

 21 時間
Apache Ambari is an open-source management platform for provisioning, managing, monitoring and securing Apache Hadoop clusters. In this instructor-led live training participants will learn the management tools and practices provided by Ambari to successfully manage Hadoop clusters. By the end of this training, participants will be able to:
  • Set up a live Big Data cluster using Ambari
  • Apply Ambari's advanced features and functionalities to various use cases
  • Seamlessly add and remove nodes as needed
  • Improve a Hadoop cluster's performance through tuning and tweaking
Audience
  • DevOps
  • System Administrators
  • DBAs
  • Hadoop testing professionals
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
続きを読む...

Impala for Business Intelligence

 21 時間
Cloudera Impala is an open source massively parallel processing (MPP) SQL query engine for Apache Hadoop clusters. Impala enables users to issue low-latency SQL queries to data stored in Hadoop Distributed File System and Apache Hbase without requiring data movement or transformation. Audience This course is aimed at analysts and data scientists performing analysis on data stored in Hadoop via Business Intelligence or SQL tools. After this course delegates will be able to
  • Extract meaningful information from Hadoop clusters with Impala.
  • Write specific programs to facilitate Business Intelligence in Impala SQL Dialect.
  • Troubleshoot Impala.
続きを読む...

Data Virtualization with Denodo Platform

 14 時間
Denodo is a data virtualization platform for managing big data, logical data warehouses, and enterprise data operations. This instructor-led, live training (online or onsite) is aimed at architects, developers, and administrators who wish to use Denodo Platform to optimize and accelerate data management through data virtualization. By the end of this training, participants will be able to:
  • Install and configure Denodo Platform.
  • Understand the features and architecture of Denodo Platform.
  • Understand the key concepts, benefits, and use cases for data virtualization.
  • Learn how to configure and manage the Denodo Platform server.
  • Implement data security, user access, and services authentication.
  • Apply the tools and techniques for operations monitoring and performance optimization.
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.
続きを読む...

Apache Airflow

 21 時間
Apache Airflow is a platform for authoring, scheduling and monitoring workflows. This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Apache Airflow to build and manage end-to-end data pipelines. By the end of this training, participants will be able to:
  • Install and configure Apache Airflow.
  • Author, schedule and monitor complex data pipelines
  • Manage many ETLs (Extract, extract, Transform, Load).
  • Scale and secure Apache Airflow.
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.
続きを読む...

Apache Hama

 14 時間
Apache Hama is a framework based on the Bulk Synchronous Parallel (BSP) computing model and is primarily used for Big Data analytics. In this instructor-led, live training, participants will learn the fundamentals of Apache Hama as they step through the creation of a BSP-based application and a vertex-centric program using the Apache Hama frameworks. By the end of this training, participants will be able to:
  • Install and configure Apache Hama
  • Understand the fundamentals of Apache Hama and the Bulk Synchronous Parallel (BSP) programming model
  • Build a BSP-based program using Apache Hama BSP framework
  • Build a vertex-centric program using Apache Hama Graph Framework
  • Build, test, and debug their own Apache Hama applications
Audience
  • Developers
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.
続きを読む...

Zeppelin for Interactive Data Analytics

 14 時間
Apache Zeppelin is a web-based notebook for capturing, exploring, visualizing and sharing Hadoop and Spark based data. This instructor-led, live training introduces the concepts behind interactive data analytics and walks participants through the deployment and usage of Zeppelin in a single-user or multi-user environment. By the end of this training, participants will be able to:
  • Install and configure Zeppelin
  • Develop, organize, execute and share data in a browser-based interface
  • Visualize results without referring to the command line or cluster details
  • Execute and collaborate on long workflows
  • Work with any of a number of plug-in language/data-processing-backends, such as Scala (with Apache Spark), Python (with Apache Spark), Spark SQL, JDBC, Markdown and Shell.
  • Integrate Zeppelin with Spark, Flink and Map Reduce
  • Secure multi-user instances of Zeppelin with Apache Shiro
Audience
  • Data engineers
  • Data analysts
  • Data scientists
  • Software developers
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
続きを読む...

Apache Accumulo Fundamentals

 21 時間
Apache Accumulo is a sorted, distributed key/value store that provides robust, scalable data storage and retrieval. It is based on the design of Google's BigTable and is powered by Apache Hadoop, Apache Zookeeper, and Apache Thrift. This instructor-led, live courses covers the working principles behind Accumulo and walks participants through the development of a sample application on Apache Accumulo.
Format of the Course
  • Part lecture, part discussion, hands-on development and implementation, occasional tests to gauge understanding
続きを読む...

Apache Druid for Real-Time Data Analysis

 21 時間
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
続きを読む...

MemSQL

 28 時間
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
続きを読む...

Amazon Redshift

 21 時間
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.
続きを読む...

Pivotal Greenplum for Developers

 21 時間
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.
続きを読む...

Greenplum Database

 14 時間
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.
続きを読む...

Samza for Stream Processing

 14 時間
Apache Samza is an open-source near-realtime, asynchronous computational framework for stream processing.  It uses Apache Kafka for messaging, and Apache Hadoop YARN for fault tolerance, processor isolation, security, and resource management. This instructor-led, live training introduces the principles behind messaging systems and distributed stream processing, while walking participants through the creation of a sample Samza-based project and job execution. By the end of this training, participants will be able to:
  • Use Samza to simplify the code needed to produce and consume messages.
  • Decouple the handling of messages from an application.
  • Use Samza to implement near-realtime asynchronous computation.
  • Use stream processing to provide a higher level of abstraction over messaging systems.
Audience
  • Developers
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
続きを読む...

Last Updated:

お客様の声(25)

多くの実用的な例、同じ問題へのさまざまなアプローチ方法、そして現在のソリューションを改善するためのあまり知られていないトリックなど

Rafał - Nordea

コース - Apache Spark MLlib

Machine Translated

トレーナーはコンセプトをよく理解していました

Josheel - Verizon Connect

コース - Amazon Redshift

Machine Translated

The live examples

Ahmet Bolat - Accenture Industrial SS

コース - Python, Spark, and Hadoop for Big Data

how the trainor shows his knowledge in the subject he's teachign

john ernesto ii fernandez - Philippine AXA Life Insurance Corporation

コース - Data Vault: Building a Scalable Data Warehouse

I enjoyed the Maven training and how to configure it. I like to use Java programming language.

Robert Cost - Corning Incorporated

コース - Apache ActiveMQ

trainer's knowledge

Fatma Badi - Dubai Electricity & Water Authority

コース - Big Data - Data Science

very interactive...

Richard Langford

コース - SMACK Stack for Data Science

Sufficient hands on, trainer is knowledgable

Chris Tan

コース - A Practical Introduction to Stream Processing

During the exercises, James explained me every step whereever I was getting stuck in more detail. I was completely new to NIFI. He explained the actual purpose of NIFI, even the basics such as open source. He covered every concept of Nifi starting from Beginner Level to Developer Level.

Firdous Hashim Ali - MOD A BLOCK

コース - Apache NiFi for Administrators

Trainer's preparation & organization, and quality of materials provided on github.

Mateusz Rek - MicroStrategy Poland Sp. z o.o.

コース - Impala for Business Intelligence

Open discussion with trainer

Tomek Danowski - GE Medical Systems Polska Sp. Z O.O.

コース - Process Mining

Get to learn spark streaming , databricks and aws redshift

Lim Meng Tee - Jobstreet.com Shared Services Sdn. Bhd.

コース - Apache Spark in the Cloud

Very useful in because it helps me understand what we can do with the data in our context. It will also help me

Nicolas NEMORIN - Adecco Groupe France

コース - KNIME Analytics Platform for BI

That I had it in the first place.

Peter Scales - CACI Ltd

コース - Apache NiFi for Developers

Instructor very knowledgeable and very happy to stop and explain stuff to the group or to an individual.

Paul Anstee - Northrop Grumman

コース - Apache Accumulo Fundamentals

Nice training, full of interesting topics. After each topic helpful examples were provided.

Pawel Wojcikowski - MicroStrategy Poland Sp. z o.o.

コース - Teradata Fundamentals

practical things of doing, also theory was served good by Ajay

Dominik Mazur - Capgemini Polska Sp. z o.o.

コース - Hadoop Administration on MapR

practice tasks

Pawel Kozikowski - GE Medical Systems Polska Sp. Zoo

コース - Python and Spark for Big Data (PySpark)

Recalling/reviewing keypoints of the topics discussed.

Paolo Angelo Gaton - SMS Global Technologies Inc.

コース - Building Stream Processing Applications with Kafka Streams

The VM I liked very much The Teacher was very knowledgeable regarding the topic as well as other topics, he was very nice and friendly I liked the facility in Dubai.

Safar Alqahtani - Elm Information Security

コース - Big Data Analytics in Health

I genuinely enjoyed the hands passed exercises.

Yunfa Zhu - Environmental and Climate Change Canada

コース - Foundation R

I generally liked the fernando's knowledge.

Valentin de Dianous - Informatique ProContact INC.

コース - Big Data Architect

Richard's training style kept it interesting, the real world examples used helped to drive the concepts home.

Jamie Martin-Royle - NBrown Group

コース - From Data to Decision with Big Data and Predictive Analytics

This is one of the best hands-on with exercises programming courses I have ever taken.

Laura Kahn

コース - Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP

The example and training material were sufficient and made it easy to understand what you are doing.

Teboho Makenete

コース - Data Science for Big Data Analytics

Upcoming Courses

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Big Data Analytics for Telecom Regulators

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