CA, San Jose - Downtown

177 Park Avenue, San Jose, united states, 95113
The venue is located in the heart of downtown San Jose with quick access to Highways 87, 280 or the CalTrain.

Subcategories (16)

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Dremio
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Apache Hama
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Apache Zeppelin
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Apache Kylin
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Apache ActiveMQ
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Apache Accumulo
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Apache Spark
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Data Mining
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Hadoop
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Stream Processing
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Data Warehouse
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Apache ZooKeeper
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Teradata
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Apache Arrow
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Apache Airflow
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Denodo

Explore Our Courses

Python, Spark, and Hadoop for Big Data

21 Hours
This instructor-led, live training in California (online or onsite) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets. By the end of this training, participants will be able to:
  • Set up the necessary environment to start processing big data with Spark, Hadoop, and Python.
  • Understand the features, core components, and architecture of Spark and Hadoop.
  • Learn how to integrate Spark, Hadoop, and Python for big data processing.
  • Explore the tools in the Spark ecosystem (Spark MlLib, Spark Streaming, Kafka, Sqoop, Kafka, and Flume).
  • Build collaborative filtering recommendation systems similar to Netflix, YouTube, Amazon, Spotify, and Google.
  • Use Apache Mahout to scale machine learning algorithms.
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Azure Data Lake Storage Gen2

14 Hours
This instructor-led, live training in California (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.
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Data Mining with Weka

14 Hours
This instructor-led, live training in California (online or onsite) is aimed at beginner to intermediate-level data analysts and data scientists who wish to use Weka to perform data mining tasks. By the end of this training, participants will be able to:
  • Install and configure Weka.
  • Understand the Weka environment and workbench.
  • Perform data mining tasks using Weka.
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SPSS Modeler

14 Hours
This instructor-led, live training in California (online or onsite) is aimed at data analysts or anyone who wishes to use SPSS Modeler to perform data mining activities. By the end of this training, participants will be able to:
  • Understand the fundamentals of data mining.
  • Learn how to import and assess data quality with the Modeler.
  • Develop, deploy, and evaluate data models efficiently.
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Administration of Apache Spark

35 Hours
This instructor-led, live training in California (online or onsite) is aimed at beginner-level to intermediate-level system administrators who wish to deploy, maintain, and optimize Spark clusters.By the end of this training, participants will be able to:
  • Install and configure Apache Spark in various environments.
  • Manage cluster resources and monitor Spark applications.
  • Optimize the performance of Spark clusters.
  • Implement security measures and ensure high availability.
  • Debug and troubleshoot common Spark issues.
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IBM Datastage For Administrators and Developers

35 Hours
This instructor-led, live training in California (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.
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A Practical Introduction to Data Analysis and Big Data

35 Hours
Participants who complete this instructor-led, live training in California will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools. Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class. The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.
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Python and Spark for Big Data (PySpark)

21 Hours
In this instructor-led, live training in California, 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.
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Data Analysis with Hive/HiveQL

7 Hours
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 Hours
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 Hours
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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Datameer for Data Analysts

14 Hours
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
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Data Mining with Excel

14 Hours
This instructor-led, live training in California (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.
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Dremio for Self-Service Data Analysis

21 Hours
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 Hours
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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SQL For Data Science and Data Analysis

14 Hours
  This instructor-led, live training (online or onsite) is aimed at software developers, managers, and business analyst who wish to use big data systems to store and retrieve large amounts of data. By the end of this training, participants will be able to:
  • Query large amounts of data efficiently.
  • Understand how Big Data system store and retrieve data
  • Use the latest big data systems available
  • Wrangle data from data systems into reporting systems
  • Learn to write SQL queries in:
    • MySQL
    • Postgres
    • Hive Query Language (HiveQL/HQL)
    • Redshift 
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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Oracle SQL Intermediate - Data Extraction

14 Hours
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.
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Big Data Analytics for Telecom Regulators

14 Hours
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 Govt. Agencies

35 Hours
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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Big Data Architect

35 Hours
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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Big Data Business Intelligence for Criminal Intelligence Analysis

35 Hours
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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Programming with Big Data in R

21 Hours
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 Hours
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 Hours
The course is part of the Data Scientist skill set (Domain: Data and Technology).
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Big Data - Data Science

14 Hours
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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From Data to Decision with Big Data and Predictive Analytics

21 Hours
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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Data Science for Big Data Analytics

35 Hours
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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Machine Learning and Big Data

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

7 Hours
This instructor-led, live training in California (online or onsite) is aimed at software engineers who wish to use Sqoop and Flume for big data. 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.
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Talend Big Data Integration

28 Hours
This instructor-led, live training in California (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.
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Testimonials (50)

I generally liked the fernando's knowledge.

Valentin de Dianous - Informatique ProContact INC.
Course - Big Data Architect

The subject matter and the pace were perfect.

Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course - Programming with Big Data in R

Michael the trainer is very knowledgeable and skillful about the subject of Big Data and R. He is very flexible and quickly customize the training meeting clients' need. He is also very capable to solve technical and subject matter problems on the go. Fantastic and professional training!.

Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course - Programming with Big Data in R

How big data work, data programs, greater knowledge of how our current world works using data

Ozayr Hussain - Vodacom
Course - A Practical Introduction to Data Analysis and Big Data

The practical side of the training.

Patrick - Vodacom PTy Ltd
Course - A Practical Introduction to Data Analysis and Big Data

Interactive topics and the style used by the lecture to simplified the topics for the students

Miran Saeed - Sulaymaniyah Asayish Agency
Course - A Practical Introduction to Data Analysis and Big Data

the trainer and his ability to lecture

ibrahim hamakarim - Sulaymaniyah Asayish Agency
Course - A Practical Introduction to Data Analysis and Big Data

Practical exercises

JOEL CHIGADA - University of the Western Cape
Course - A Practical Introduction to Data Analysis and Big Data

The content, as I found it very interesting and think it would help me in my final year at University.

Krishan - NBrown Group
Course - From Data to Decision with Big Data and Predictive Analytics

Intensity, Training materials and expertise, Clarity, Excellent communication with Alessandra

Marija Hornis Dmitrovic - Marija Hornis
Course - Data Science for Big Data Analytics

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

Teboho Makenete
Course - Data Science for Big Data Analytics

I genuinely enjoyed the many hands-on sessions.

Jacek Pieczątka
Course - Administrator Training for Apache Hadoop

I genuinely enjoyed the big competences of Trainer.

Grzegorz Gorski
Course - Administrator Training for Apache Hadoop

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
Course - Big Data Analytics in Health

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

Dominik Mazur - Capgemini Polska Sp. z o.o.
Course - Hadoop Administration on MapR

Muy poco, se me dificulto mucho y mas por que entre desfasado, no tome los primeras sesiones.

Rolando García - OIT para México y Cuba
Course - Apache NiFi for Administrators

El conocer que hay un programa que tiene muchas aplicaciones potenciales para el trabajo que desarrollo

Jorge Alberto Martinez - OIT para México y Cuba
Course - Apache NiFi for Administrators

La interación del profesor con el alumno, que esperaba a cada alumno jy explicaba si era necesario repetidas veces.

Daniela Guadalupe Tenorio Rodríguez - OIT para México y Cuba
Course - Apache NiFi for Administrators

En realidad todo estuvo interesante

Alvaro Lopez Zenteno Cato - OIT para México y Cuba
Course - Apache NiFi for Administrators

Los temas los maneja muy bien y resuelve las dudas, además nos llevaba paso a paso en las prácticas.

Francisco Javier Hernández Sánchez - OIT para México y Cuba
Course - Apache NiFi for Administrators

La exposicion del maestro

SANDRA RAMIREZ - Organización Internacional del Trabajo
Course - Apache NiFi for Developers

La exposición Excelente

SANDRA RAMIREZ - Organización Internacional del Trabajo
Course - Apache NiFi for Developers

Virtual environment working well and trainer positive attitude

Wojciech Lukawski - Orsted Polska sp. z o.o.
Course - Apache NiFi for Developers

I liked trainer's attitude and choice of examples. Trainer was very willing to help and answer questions. Trainer tried to go with as many examples as possible, even though we were short on time.

Waldemar Sobiecki - Orsted Polska sp. z o.o.
Course - Apache NiFi for Developers

The trainer is very polite, tolerant, helpful. I was not afraid to ask for help when I couldn't handle something myself. I like that most of the exercises we did together without splitting into separate groups or by ourselves. That way we could ask questions on regular basis.

Elżbieta Doniek - Orsted Polska sp. z o.o.
Course - Apache NiFi for Developers

The fact that we were able to take with us most of the information/course/presentation/exercises done, so that we can look over them and perhaps redo what we didint understand first time or improve what we already did.

Raul Mihail Rat - Accenture Industrial SS
Course - Python, Spark, and Hadoop for Big Data

I liked that it managed to lay the foundations of the topic and go to some quite advanced exercises. Also provided easy ways to write/test the code.

Ionut Goga - Accenture Industrial SS
Course - Python, Spark, and Hadoop for Big Data

I was benefit from the guidance and sharing life examples + answering all questions.

Marta Melloch - Amazon Development Center Poland Sp. z o.o.
Course - Data Mining and Analysis

I really enjoyed the all the best.

Halil polat - Amazon Development Center Poland Sp. z o.o.
Course - Data Mining and Analysis

The information given was interesting and the best part was towards the end when we were provided with Data from Durex and worked on Data we are familiar with and perform operations to get results.

Jessica Chaar
Course - Data Mining and Analysis

Very tailored to needs.

Yashan Wang
Course - Data Mining with R

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

john ernesto ii fernandez - Philippine AXA Life Insurance Corporation
Course - Data Vault: Building a Scalable Data Warehouse

I enjoyed the good real world examples, reviews of existing reports.

Ronald Parrish
Course - Data Visualization

I liked the examples.

Peter Coleman
Course - Data Visualization

I liked the examples.

Peter Coleman
Course - Data Visualization

I am a hands-on learner and this was something that he did a lot of.

Lisa Comfort
Course - Data Visualization

Trainer was enthusiastic.

Diane Lucas
Course - Data Visualization

The trainer was so knowledgeable and included areas I was interested in.

Mohamed Salama
Course - Data Mining & Machine Learning with R

I feel more confident with coding now. I've never done it before but now I understand that it's not rocket science and I can do it when necessary.

Anna - Birmingham City University
Course - Foundation R

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
Course - KNIME Analytics Platform for BI

APRENDIZAJE DE UNA HERRAMIENTA NUEVA

MARIA ELENA DOMINGUEZ ESCUDERO - Reckitt Benckizer
Course - KNIME Analytics Platform for BI

I learned a lot - not only in theoretical knowledge but I also applied that knowledge during the training and therefore I really understood what process mining is and how it works. Thanks a lot!

Julia Dörre - Techniker Krankenkasse
Course - Process Mining

Sufficient hands on, trainer is knowledgable

Chris Tan
Course - A Practical Introduction to Stream Processing

very interactive...

Richard Langford
Course - SMACK Stack for Data Science

Commitment and willingness to explain secondary topics.

Marek - Krajowy Rejestr Długów Biuro Informacji Gospodarczej S.A.
Course - Apache Spark Fundamentals

Machine Translated

The trainer's practical experience, not coloring the discussed solution, but also not introducing a negative connotation. I feel that the trainer is preparing me for real and practical use of the tool - these valuable details are usually not found in books.

Krzysztof Miodek - Krajowy Rejestr Długów Biuro Informacji Gospodarczej S.A.
Course - Apache Spark Fundamentals

Machine Translated

Having hands on session / assignments

Poornima Chenthamarakshan - Intelligent Medical Objects
Course - Apache Spark in the Cloud

1. Right balance between high level concepts and technical details. 2. Andras is very knowledgeable about his teaching. 3. Exercise

Steven Wu - Intelligent Medical Objects
Course - Apache Spark in the Cloud

La combinación de teoría y práctica con herramientas como databricks

Graciela Saud - Servicio de Impuestos Internos
Course - Spark for Developers

Doing similar exercises different ways really help understanding what each component (Hadoop/Spark, standalone/cluster) can do on its own and together. It gave me ideas on how I should test my application on my local machine when I develop vs when it is deployed on a cluster.

Thomas Carcaud - IT Frankfurt GmbH
Course - Spark for Developers

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