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 data modelers with a useful cross reference that improves modeling productivity.
It is particularly useful for the dictionary to be a filterable/sortable Excel document, but out of the box ERwin, one of the leading data modeling tools, includes a notably inflexible reporting capability. Luckily, it is possible to directly query the ERwin “metamodel”. However, I found the ERwin documentation a bit hard to decipher and not quite accurate. Hopefully this post will save modelers some steps in figuring out how to query the metamodel.
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 active assistance from the CA help desk. No one involved, including me, could find on the web a list of actions to take to try and solve the problem, although there were a few hints scattered around (if we missed it, please add the link in a comment). Continue reading →
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 challenges are fundamental. Big Data and NoSQL bring lots of what we know about data elements, inherent data design, and data management into question. I think considering these elements closely leads to a sensible to-do list for relational database professionals. Continue reading →
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 “good enough” data design, similar to the one pictured, enabled a significant application bug.
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 player in harnessing and capitalizing on new data technologies. Continue reading →
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 business person knows that item color doesn’t belong in the order table because it describes the item, not the order. Everyone knows that the order isn’t green! Continue reading →
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 behind the standards. In general, it seems to me that following standards is trivial for someone who understand the principles, but impossible for someone who doesn’t. But there doesn’t seem to be general understanding of data modeling principles. Continue reading →
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 definition and conceptual design. Continue reading →
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 are quicker to stand up, more intuitive and don’t need the configuration, support, and hardware that the bigger players require.”
A Quick Overview
At first look QlikView is fairly accessible to those experienced with BI tools. A “.qvw” QlikView file contains three classes of user-facing components: a script-based data integration language that runs when the user requests a “reload”, a data modeling component that looks deceptively like a relational data modeling tool, and a familiar array of data visualizations: graphics, charts, lists, etc.
In the past I’ve never understood what people really mean they say “think outside the box” but Jim Harris, in a recent OCDQ blog post, helped me figure it out.
Mr. Harris ends with this provocative line: “the bottom line is Google and Facebook have socialized data in order to capitalize data as a true corporate asset.” The post starts with a cold war analogy and proceeds to describe how Facebook and Google have made big money as “internet advertising agencies:” offering free services with which users (like us) serve up personal data in return for use of the service, then selling advertising space based on our data (hopefully anonymized).