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Tim Kirschke

Tim Kirschke is a Managing BI Consultant and and Head of Internal Development at Scalefree . With a background in Applied Mathematics, he specializes in architecting auditable data solutions using Microsoft Fabric, Snowflake, and dbt. A dbt Certified Architect and CDVP2, Tim has led major warehouse implementations and conducts strategic workshops on data automation and enablement.

In Intermediate

Speed Up Your Vault with VaultSpeed – Success Through Automation – Part 2

Watch the Webinar

Speed Up Your Vault with Vaultspeed – Success Through Automation

In this Webinar, we take a closer look at Data Warehouse Automation and how easily it can be implemented. First, we will break down the basics of Data Warehouse Automation.

Then we will show how Data Vault 2.0 can contribute to successful Automation on the basis of a sample COVID-19 data set showing vaccine and infection numbers.

Lastly, Vaultspeed will give a demonstration of their tool that implements our suggested model using the data set thus showing the viewers how “simple and easy” VaultSpeed as a Data Warehouse Automation tool is.

This webinar is for everyone who wants to learn about Data Warehouse Automation and a sneak peek into VaultSpeed.

Watch Webinar Recording

Webinar Agenda

1. Understanding Data Warehouse Automation
2. Automation in Data Vault 2.0
3. Usecase
4. Vaultspeed Demo

In Intermediate

Speed Up Your Vault with VaultSpeed – Success Through Automation – Part 1

Watch the Webinar

Speed Up Your Vault with Vaultspeed – Success Through Automation

In this Webinar, we take a closer look at Data Warehouse Automation and how easily it can be implemented. First, we will break down the basics of Data Warehouse Automation.

Then we will show how Data Vault 2.0 can contribute to successful Automation on the basis of a sample COVID-19 data set showing vaccine and infection numbers.

Lastly, Vaultspeed will give a demonstration of their tool that implements our suggested model using the data set thus showing the viewers how “simple and easy” VaultSpeed as a Data Warehouse Automation tool is.

This webinar is for everyone who wants to learn about Data Warehouse Automation and a sneak peek into VaultSpeed.

Watch Webinar Recording

Webinar Agenda

1. Understanding Data Warehouse Automation
2. Automation in Data Vault 2.0
3. Usecase
4. Vaultspeed Demo

In Data Tools

Document Processing in MongoDB

In continuing our ongoing series, this piece within the blog series will describe the basics of querying and modifying data in MongoDB with a focus on the basics needed for the Data Vault load as well as query patterns. 

In contrast to the tables used by relational databases, MongoDB uses a JSON-based document data model. Thus, documents are a more natural way to represent data as a single structure with related data embedded as sub-documents and arrays collapses what is otherwise separated into parent-child tables linked by foreign keys in a relational database. You can model data in any way that your application demands – from rich, hierarchical documents through to flat, table-like structures, simple key-value pairs, text, geospatial data, and the nodes as well as edges used in graph processing.

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In Data Tools

Processing Enterprise Data with Documents in MongoDB

Today’s enterprise organizations receive and process data from a variety of sources, including silos generated by web as well as mobile applications, social media, artificial intelligence solutions in addition to IoT sensors. That said, the efficient processing of this data at high volume in an enterprise setting is still a challenge for many organizations. 

Typical challenges include issues such as the integration of mainframe data with real-time IoT messages and hierarchical documents.
One of such issues being that enterprise data is not clean and might have contradicting characteristics as well as interpretations. This poses a challenge for many processes such as when integrating customers from multiple source systems.

Though, data cleansing could be considered as a solution to this problem. However, what if different data cleansing rules should be applied to the incoming data set? For example, because the basic assumption for “a single version of the truth” doesn’t exist in most enterprises. While one department might have a clear understanding of how the incoming data should be cleansed, another department, or an external party, might have another understanding. 

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