Tag: Data Modeling
-
To SQL or to NoSQL?
Recently there was a great post at Dzone recounting how one “tech savvy startup” moved away from its NoSQL database management system to a relational one. The writer, Matt Butcher, plays out the reasons under these main points: Our data is relational We need better querying We have access to better resources Summing up: “The bottom…
-
DIY Data Dictionary: ODBC Reporting from the ERwin Metamodel
Application developers and business people accessing relational databases need data dictionaries in order to properly load or query a database. The data dictionary provides a source of information about the model for those without model access, including entity/table and attribute/column definitions, datatypes, primary keys, relationships among tables, and so on. The data dictionary also provides…
-
The ERwin Model Mart Access Problem
On two successive client assignments as a data modeler I’ve waited while client technicians wrestled with getting access to the ERwin Model Mart. In short, clicking on File, Mart, Connection, and logging in to the Model Mart failed every time, with various error messages. In both cases the teams lost literally months, in spite of…
-
Relational DB Pros: The Times They Are A-Changin’
Recently I read a thoughtful post at the PASS Business Analytics Conference site discussing how different the world is now for database professionals. Author Chris Webb focuses on the data science side in this post. His analysis made me think of the challenges and opportunities “big data” serves up to relational database designers. To me these…
-
Data Design Matters
As important as it is, data modeling has always had a geeky, faintly impractical tinge to some. I’ve seen application development projects proceed with a suboptimal, “good enough”, model. The resulting systems might otherwise be well-architected, but sometimes strange vulnerabilities emerge that track directly to data design flaws. Recently I saw an example where a…
-
Skills of the Data Architect
One common theme in recent tectonic shifts in information technology is data management. Analyzing customer responses may require combing through unstructured emails and tweets. Timely analysis of web interactions may demand a big data solution. Deployment of data visualization tools to users may dictate redesign of warehouses and marts. The data architect is a key…
-
Lessons from the puppy poster
In some presentations, I assert that top-down data modeling should result in not only a business-consistent model but also a pretty well normalized model. One of the basic concepts behind normalization is functional dependency. In layperson’s terms, functional dependency means separating entities from each other and putting attributes into the obviously correct entity. For example, a…
-
Selected data modeling best practices
Recently I was in a conversation about data modeling standards. I confess that I’m not really the standards type. I understand the value of standards and especially how important it is to follow them so others can interpret and use work products. It is just that I prefer to focus on understanding of the principles…
-
Big Data opportunities and NoSQL challenges
As a relational database professional I couldn’t help but feel like something would be lost with the emergence of the new Big Data/NoSQL database management systems (DBMS). After about two years of buzz around the topic, I’m really excited about the emerging possibilities. However, I’m pretty sure we’ll miss the relational model’s strengths in requirements…
-
A QlikView QuickStart: first steps for learning QlikView desktop
QlikTech’s QlikView reporting and analysis tool is among a new class of Business Intelligence (BI) software tools. As Ben Harden reported in a recent blog post, BI vendors like SAP, Microsoft, and IBM have traditionally sold “to the IT enterprise, but companies like QlikTech and Tableau are targeting the business and bypassing IT. Their tools…