Data Mesh – Webinar
Are central data platform teams attempting to solve an unsolvable problem? While domain-driven design heavily influenced the way we design operational systems, central data platforms kept being developed as centralized monoliths. The concept of a data mesh saw the light in response to the many downsides of monolithic data platforms.
Details
Speakers: Niels Zeilemaker, Guillermo Sánchez Dionis, Steven Nooijen
Registration
Fill in the form below to get immediate access to the Data Mesh Webinar. After filling in the form below, you will receive a confirmation e-mail with a link to the webinar.
What to Expect
After this webinar, you will be familiar with the following topics:
In this webinar, you will
- learn what a data mesh is.
- get insight into the three main architectural failure modes of a monolithic data platform and the required paradigm shift.
- learn to point out the differences between operation and analytical data, as well as the differences in access patterns, use cases, personas of data users, and technology used to manage these datatypes.
- gain an understanding of the characteristics of a successful data mesh.
- become familiar with several critical questions to answer to successfully implement a data mesh.
A centralized and monolithic data platform knows several architectural downsides.
Speakers
This Data Mesh Webinar is hosted by the following experienced speakers:
Niels Zeilemaker
Niels is Chief of Technology at GoDataDriven and works for a wide range of companies where he engineers features and builds models.
He has finished his Ph.D. thesis at the Technical University of Delft where he researched P2P systems, primarily focusing on privacy and cooperation, including applying encryption and anonymization techniques in the P2P domain.
Guillermo Sánchez Dionis
Guillermo is an Analytics Engineer with a really pragmatic and business-oriented approach to Big Data Analytics.
He is a clear example of the evolution of the Data Analyst role towards a more engineering-focused role powered by modern data platforms. His skills range from writing production-grade data pipelines to building data warehouses or modeling any type of data. He does this while bringing software engineering best practices into his work, which contributes to building scalable and resilient solutions.
Steven Nooijen
Steven likes to combine thorough data analysis with strategic business thinking. Being a data science consultant since 2014, he has demonstrated the added value of analytics in several industries now, often redefining business processes using data-driven insights. To make his point Steven has mastered not only R and Python but also PowerPoint. This makes him an excellent translator who brings value by bridging the gap between business stakeholders and data scientists.
Related Content about Data Platforms
- Apache Spark
- Data Engineering
- Data Platforms
- Open Source
- Analytics Engineering
- data
- Data Platforms
- Apache Spark
- Data Engineering
- Data Platforms
- Open Source
- Analytics Engineering
- Data Platforms
- Analytics Engineering
- Data Platforms
- Data Engineering
- Data Platforms
