David Sietz
Systems Architect, Open Source Contributor
David Sietz is a solutions architect at International Association of Privacy Professionals with more than 25 years of hands-on experience. Starting his IT career in Munich Germany, his professional history as a data architect, system designer, and adult educator, instilled in him a sense of IT with the business customer in mind.
David's specialty is architecting, designing, and constructing of viable solutions that are properly engineered for their purpose and longevity. His breadth of knowledge of data management, microservice architecture, and building cloud platforms allows him to bridge disciplines and provide MVP solutions.
Presentations
Data as a Service Overview
Wednesday, 1:30 PM EST
Should Information Management systems apply the services architecture? Many data provisioning and BI systems are monolithic, tightly coupled, difficult to scale, and stumble when it comes to delivering MVP in a timely manner.
In this session we will look at the common obstacles such systems inherently bring with them, and how the Data as a Service architecture pattern addresses many of these issues.
Agenda
- setting expectations
- anti-patterns
- DaaS pattern
- using a business lens
Using Talend Open Studio for DaaS
Wednesday, 3:15 PM EST
Data as a Service delivers MVP of real-time data management, while avoiding many of the anit-patterns that traditional data provisioning and BI systems portray. However, building out a Data as a Service system doesn't require high up-front costs and the welding of multiple products. Learn how the open source product Talend Open Studio can be used to build out a DaaS system that delivers faster and more scalable solutions to your customer.
In this session we will look a close look at the key components of the Data as a Service architecture pattern by walking through an example implemented using Talend.
Agenda
- DaaS Architecture
- Reusable jobs
- Sourcing
- Provisioning
- Consuming
An Introduction to Test Data Generation
Thursday, 1:30 PM EST
Continuous Integration has redefined our testing practices. Testing has become more focused, efficient, and re-positioned further upstream in the development life-cycle. Unfortunately, our testing systems haven't evolved in lock-step - specifically the provisioning of realist test data.
It remains common practice to extract, cleanse and load production data into our non- production environments. This is a lengthy process with serious security concerns, and still doesn't satisfy all our data content requirements.
What if there is a better way of providing realist test data? What if it could be generated on-demand as part of the Continuous Integration process - without the heavy databases and traditional batch jobs?
Come join us in a journey as we walk through the concepts, building blocks and implementation of the light-weight Test Data Generation package that addresses this automated testing niche.
We'll provide an overview of the Rust language, explain how a mathematical framework from the 1950s was rediscovered, and provide an overview of Machine Learning patterns that were applied.
Agenda
- overview
- Rust
- Markov Decision Process
- machine learning
Adopting the Open Source Model
Thursday, 3:15 PM EST
Open source is growing with leaps and bounds. Even corporations are adopting the open source products and reaping the numerous benefits. But there’s more than the products that need to be adopted – the Open Source Model itself is also highly valuable.
Find out how adopting the Open Source Model can improve your organization’s “time to market”, product quality, professional climate, and Agile adoption. We’ll focus on what constitutes an Open Source mindset, how to implement it, and some of the pitfalls and anti-patterns to avoid.
Agenda
- defining ownership
- open source contribution models
- implementing open source in the enterprise
- pitfalls & anti-patterns