Picking this topic up from the last post, I focused on enriching the data released. This will allow further exploration of this data.
Lets use our previous schema as our starting point. The previous post produced a good starting point for the task at hand. The records from the previous post were stored in a table as shown in Figure 1.
Figure 1 – License plate table readings.
Browsing Hacker News, I recently found out about the City of Oakland releasing almost 3 million records of license plate reader data. The conversation there is way better than any blurb I could come up with. However, this is a neat opportunity to mine this data as an academic exercise.
From the source, they are hosting a list of CSV files with various bits of information. Common to all files, and of critical importance is the date and time of the tag reading and the latitude and longitude of each reading. Supplemental information as the site of the reading and source of such is often given as well. Most worrisome is the fact that the data has not been cleansed and includes the actual license tag for each reading instead of some ID. This would be the first thing to go after for data to be re-shared and used here. Continue reading
So last night I found this on my lab machine…
Silly node ran out of space. Spawning up extra node promptly made this a non-issue.
It is so nice to work with open source tools built to handle failure gracefully. A few years ago the above scenario would have prompted a weekend-at-the-colo to the dismay of family and my sanity. These are interesting times!
Because I will have to do this again in no time, here I jot down the quickest way for me to install 12c. This is the usual series of following steps, running into an error, searching for a fix, lather, rinse repeat.
This blog post is about OBIEE reporting. Specifically, it is about skipping the data warehouse and reporting from the transactional database instead. Oracle’s OBIEE, like most BI reporting tools, is designed to use star/snowflake schemas as the underlying structures to report from. Additionally, OBIEE’s metadata layer is very rich and extensively well thought out, allowing for a great deal of flexibility. Oracle’s metadata tool (Admin Tool) allows us to leverage this flexibility and features to bridge the gap between an OLTP and an OLAP model. I am not negating the need for a data warehouse, I am just wondering if all BI reporting projects merit one.
So the question to ask is: Is OBIEE up to the task?
I’ve added ability to consume URLs whose output is XML as a data-source for Flex OLAP Cube. Check it out 🙂
Going thru this exercise has helped me understand a bit about creating data cubes and the uses they serve. This is but one of the many interesting (to me) things I am exposed to at work. Doing this from scratch provides me with insight unattainable with a ‘shrink-wrap’ tool.
Note – I had originally fetched data from accross the web but had to store xml files internally for show and tell. Enjoy.
I wanted to write the post ‘Slicing Your Own OLAP Cube’ but I am not there yet. From my last post, a recurring theme in my friends comments was that the dimensions and measures that can be inspected where set in stone. I thought I was doing fairly well but I can see their point. Having a cube and having it sliced in a way you don’t need is kinda useless.