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The Relational Model and the Importance of Information
I recently changed cell phones and, after a day of getting used to the new phone, I wondered what took me so long. It got me to thinking that most of corporate America’s technology organizations will feel the same way once they do some tuning to their information management environments.
It is hard to make changes to something that is functionally working. As long as SAP is running – albeit slower than some would like – or the fraud detection system is working – though it is too slow to stop much of the fraud – performance can always be promised as being “around the corner.” One more round of parameter tuning, one more patch, one more code tweak and it will perform. However, most of the time, the improvements are minor at best.
At the same time, the data is growing, the plans for the usage of the system are growing and the reliance is larger than ever.
The last few years has seen an explosion of the possibilities in data storage. Many of them are valid for the enterprise. If you have the workloads that will excel (i.e., perform best) in these environments, maybe it’s time to jump the shark and build or move a workload into a new platform. It does not (should not) be a bet-your-career type of move. It should be something that offers you the chance to establish the new platform in the shop, try it out and understand it better.
To point, most data warehouses sit on relational databases on HDD. The data is stored “row-wise” (as opposed to by independent columns or grouping of columns). The data is accessed with a traditional business intelligence tool with most access being non-interactive reporting and reports done by someone who is not the end user. Mobile is “not supported” and the cloud has security issues, therefore not utilized.
These thought patterns need to change for organizations to remain competitive in the current landscape of information leadership. Of course, new applications where nothing is yet entrenched is fertile area for breaking into newer technologies. Many of these have unique characteristics such as data type, data size, security requirements or performance expectations that make it a best fit for something other than a shared data warehouse.
There are many workloads that could benefit from a platform change, or, in the case of data warehouses, from turning on some of the analytic features in the database. Transactional databases have added several features that make them analytic. These include in-database analytics, scale out, a columnar orientation and a high use of memory.
Columnar orientation optimizes those queries that access a small percentage of the overall bytes in the tables. I’ve found there can be quite a large percentage of queries in an analytic environment meeting this condition. However, you have to choose how to group columns at table definition time. Choosing nothing is equivalent to row orientation. Usually you can group columns or isolate columns in storage.
The analytic workload – and I’m throwing the data warehouse in there – is going to be almost always optimized with columnar. However, some things will regress and that fear is keeping many from making the step forward to columnar except in new, isolated workloads where the performance expectations across the board are not set yet.
In-memory databases are about performance advantages and about where the data is stored (and how it is persisted). Some HDD/SSD-based DBMS have added this capability while others avoid discriminating the data and put it all in memory.
The relational model, including its data pages, is covered in Chapter 2 of my book, Information Management: Strategies for Gaining a Competitive Advantage with Data. Here is that chapter so you can read more about the continued usefulness of the relational model, as well as multidimensional databases, a new strain in the relational model called NewSQL and some recommended machines for relational databases that utilize the solid state storage option well:
No matter what business you’re in, if you think about what sets it apart from the competition and where you need to take it, most of these strategies involve having access to reliable information when you need it. What used to be optional and prioritized well after the operational aspects of storefront, supply chain and transacting business is what now sets companies apart. If you look at the leaders in industry, they are masters of the information asset. They save more information, make it more accessible to a broad internal community and have developed business leaders that are able to consume the data and advance the business with it.
The key is to get the right workload – a combination of the data and its processing – into the right platform based on its unique characteristics. Today, there are many options including many that are not been considered by organizations, at their peril. These include master data management, Hadoop, NoSQL systems, data stream processing and graph databases. And for all of it, there is the cloud computing option. I describe the options in the book.
Expectations for information will go nowhere but up over time. Again, that platform choice is crucial. Most organizations need more possibilities at their disposal.
Want to read more on info management? You can order your very own copy of William’s new book, Information Management: Strategies for Gaining a Competitive Advantage with Data at a 25% discount. No coupon code required.
About the Author
William McKnight (@WilliamMcKnight) is President of McKnight Consulting Group. He is an internationally recognized authority in information management. His consulting work has included many of the Global 2000 and numerous midmarket companies. His teams have won several best practice competitions for their implementations and many of his clients have gone public with their success stories. His strategies form the information management plan for leading companies in various industries.
William is a very popular speaker worldwide and a prolific writer with hundreds of articles and white papers published. William is a distinguished entrepreneur, and a former Fortune 50 technology executive and software engineer. He provides clients with strategies, architectures, platform and tool selection, and complete programs to manage information.
Computing functionality is ubiquitous. Today this logic is built into almost any machine you can think of, from home electronics and appliances to motor vehicles, and it governs the infrastructures we depend on daily — telecommunication, public utilities, transportation. Maintaining it all and driving it forward are professionals and researchers in computer science, across disciplines including:
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