Browsing articles in "Database Design"

10 Ways I Can Steal Your Data: eBook

I wrote an eBook sponsored by SolarWinds. I share real life stories of non-traditional, non-hacker ways I can steal your data.  You can download the PDF for free (registration required).

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I’ve also been contributing a blog series over on THWACK, 5 MORE Ways I can Steal Your Data, 5 More Ways I Can Steal Your Data: Work for you and Stop Working for You, 5 More Ways I Can Steal Your Data: Accessing Unmonitored Servers and Services, 5 More Ways I Can Steal Your Data: Ask the Security Guard to Help Me Carry it Out.  There’s one more post coming up soon, too.

Data protection from a data architect’s point of view is going to be a big focus of mine over the next year or so.  I’m hoping it will be yours, too.

How Deep is My Non-Love? Nested Dependencies and Overly Complex Design

Dec 4, 2017   //   by Karen Lopez   //   Blog, Data Modeling, Database, Database Design, SQL Server, WTF  //  No Comments

Relational databases have this nifty concept of objects (just things, not code objects) being dependent upon other things.  Sometimes those dependencies exist due to foreign key constraints, others via references to other things.  One example of the latter can be found in VIEWs.  A database VIEW is an object that references TABLEs or other VIEWS.  Of course, if that VIEW references other VIEWs, then that view must reference TABLEs or another VIEW.  And it’s that or another VIEW that can get modelers into trouble.

I reviewed a database design that had massively dependent VIEWs.  How did I know that? I used a proper data modeling tool to look at all the dependencies for one central VIEW.  And this is what my data modeling tool showed me:

Data Model with hundreds of dependencies (lines) between a handful of objects (squares)

That diagram shows how ONE VIEW is related to a whole bunch of other VIEWs and TABLEs in that design.  In reviewing the model, I saw that many of the VIEWs appeared to be duplicates or had very high overlap of content with other VIEWs. 

How do VIEWs Like This Happen?

There are many reasons one would created a nested VIEW.  Like anything in a hierarchy, you could have objects that could be used independently and as part of a group on a regular basis.  But that only explains one level of a VIEW hierarchy (nest).   What about VIEWs that are nested dozens are levels deep?  And why would a database have such a complex design around one VIEW?  These are the most common reasons I run into bad practices with VIEWs:

  • Designers who don’t understand the massive performance loss for massively nested VIEWS
  • Designers who design for theory, not for real world data stories
  • Designers who have no idea they are referencing another VIEW when they design their VIEW
  • Designers who are following a worst practice of creating a VIEW for every report and every window in an application
  • Designers who don’t collaborate with other designers and create their own set of VIEWs and dependencies
  • Designers who are compensated for doing work fast and not well
  • Designers who use DDL to do design, therefore never seeing the complexity of their designs
  • Data Governance policies that let anyone create objects in a database
  • A team environment were “everyone is a generalist”.

I could go on.  While I can’t go into details here, in my review I recommended complete refactoring of this overly complex design.  It is my guess this complexity was contributing to performance problems experienced in this application.  I also recommended that professional designer was used to refactor other issues with the database design.  I have no idea if this happened.  But I doubted that this application was going to meet its large scale web application goals.

Why Am I Sharing This?

Because so many design issues I find in reviews have the same causes for performance and data quality issues I’ve listed above.  I find that not using a real data modeling or design tool is the main contributing factor.  There’s a reason why physical world architects and engineers use drawings and architectural diagrams. Models are also how they make modifications successful to the items they build.

Yes, physical objects are different than software/application/database objects. My position is that these latter objects need models at least as much as buildings and devices do.  We need tools to reverse engineer objects, to view the dependencies, to search, and to assess.  In other words, to model.  Engineering data solutions requires engineering tools like data modeling tools.  And, yes, data engineers to understand how to use those tools and how to model out the unnecessary complexity.

The Key to Keys at the North Texas SQL Server User Group – 17 March

Mar 15, 2016   //   by Karen Lopez   //   Blog, Data Modeling, Database, Database Design, DLBlog, Speaking, SQL Server  //  No Comments

I’m visiting Dallas this week to speak at the North Texas SQL Server User Group this Thursday.  I’ll be speaking about keys: primary keys, surrogate keys, clustered keys, GUIDs, SEQUENCEs, alternate keys…well, there’s a lot to cover about such a simple topic.  The reason I put this presentation together is I see a lot of confusion about these topics. Some of it’s about terminology (“I can’t find anything about alternate keys in SQL Server…what the heck is that, anyway”), some of it is misunderstandings (“what do you mean IDENTITIES aren’t unique! of course they are…they are primary keys!”), some of it is just new (“Why the heck would anyone want to use a SEQUENCE?”).

We’ll be chatting about all these questions and more on Thursday, 17 March at the Microsoft venue in Irving, Texas starting at 6PM.

Attendance is free, but you need to register at http://northtexas.sqlpass.org/ to help organizers plan for the event.

Don’t worry if you don’t know about SQL Server or don’t use it: this presentation will focus on some SQL Server specific features, but the discussion is completely portable to other DBMSs.

So many of us have learned database design approaches from working with one database or data technology. We may have used only one data modeling or development tool. That means our vocabularies around identifiers and keys tend to be product specific. Do you know the difference between a unique index and a unique key? What about the difference between RI, FK and AK? These concepts span data activities and it’s important that your team understand each other and where they, their tools and approaches need to support these features. We’ll look at the generic and proprietary terms for these concepts, as well as where they fit in the database design process. We’ll also look at implementation options in SQL Server and other DBMSs.

Hope to see you there!

Is Logical Data Modeling Dead?

Feb 16, 2016   //   by Karen Lopez   //   Blog, Data Modeling, Data Stewardship, Database Design  //  7 Comments

KeepCalmAndModelOnOne of the most clichéd blogging tricks is to declare something popular as dead.  These click bait, desperate posts are popular among click-focused bloggers, but not for me. Yet here I am, writing an “is dead” post.  Today, this is about sharing my responses on-going social media posts. They go something like this:

OP: No one loves my data models any more.

Responses: Data modeling is dead.  Or…data models aren’t agile.  Or…data models died with the waterfalls. Or…only I know how to do data models and all of you are doing it wrong, which is why they just look dead.

I bet I’ve read that sort of conversation at least a hundred times, first on mailing lists, then on forums, now on social media.  It has been an ongoing battle for modelers since data models and dirt were discovered…invented…developed.

I think our issues around the love for data modeling, and logical data models specifically, is that we try to make these different types of models be different tasks.  They aren’t.  In fact, there are many types, many goals, and many points of view about data modeling.  So as good modelers, we should first seek to understand what everyone in the discussion means by that term.  And what do you know, even this fact is contentious.  More on that in another post.

I do logical data modeling when I’m physical modeling.  I don’t draw a whole lot of attention to it – it’s just how modeling is done on my projects.

Data Modeling is Dead Discussion

One current example of this discussion is taking place right now over on LinkedIn. Abhilash Gandhi posted:

During one of my project, when I raised some red flags for not having Logical Data Model, I was bombarded with comments – “Why do we need LDM”? “Are you kidding”? “What a waste of time!". The project was Data Warehouse with number of subject areas; possibility of number of data marts.

and

I have put myself into trouble by trying to enforce best practices for Data Modeling, Data Definitions, Naming Standards, etc. My question, am I asking or trying to do what may be obsolete or not necessary? Appreciate your comments.

There are responses that primarily back up the original poster’s feelings of being unneeded on modern development projects.  Then I added another view point:

I’ll play Devil’s advocate here and say that we Data Architects have also lost touch with the primary way the products of our data modeling efforts will be used. There are indeed all kinds of uses, but producing physical models is the next step in most. And we have lost the physical skills to work on the physical side. Because we let this happen, we also have failed to make physical models useful for teams who need them.

We just keep telling the builders how much they should love our logical models, but have failed to make the results of logical modeling useful to them.

I’ve talked about this in many of my presentations, webinars (sorry about the autoplay, it’s a sin, I know)  and data modeling blog posts. It’s difficult to keep up with what’s happening in the modern data platform world.  So most of us just haven’t.  It’s not that we need to be DBAs or developers.  We should, though, have a literacy level of the features and approaches to implementing our data models for production use.  Why? I addressed that as well.  Below is an edited version of my response:

We Don’t All Have to Love Logical Data Modeling

First of all, the majority of IT professionals do not need to love an LDM. They don’t even need to need them. The focus of the LDM is the business steward/owner (and if i had my way, the customer, too). But we’ve screwed up how we think of data models as artefacts that are "something done on an IT project".  Sure, that’s how almost all funding gets done for modeling, and it’s broken. But it’s also the fact of life for the relatively immature world of data modeling.

We literally beat developers and project managers with our logical data modeling, then ask them “why don’t you want us to produce data models?” We use extortion to get our beautiful logical data models done, then sit back an wonder why everyone sits at another lunch table. 

I don’t waste time or resources trying to get devs, DBAs or network admins to love the LDMs. When was the last time you loved the enterprise-wide AD architecture? The network topology? The data centre blueprints and HVAC diagrams?

Data Models form the infrastructure of the data architecture, as do conceptual models and all the models made that would fill the upper rows of the Zachman Framework. We don’t force the HVAC guys to wait to plan out their systems until a single IT application project comes along to fund that work. We do it when we need a full plan for a data centre. Or a network. Or a security framework.

But here we are, trying to whip together an application with no models. So we tell everyone to stop everything while we build an LDM. That’s what’s killing us.  Yes, we need to do it. But we don’t have to do it in a complete waterfall method.  I tell people I’m doing a data model. then I work on both an LDM and the PDM at the same time. The LDM I use to drive data requirements from business owners, the PDM to start to make it actually work in the target infrastructure. Yes, I LDM more at first, but I’m still doing both at the same time. Yes, the PDM looks an awful lot like the LDM at first.

Stop Yelling at the Clouds

The real risks we take is sounding like old men yelling at the clouds when we insist on working and talking like it is 1980 all over again.  I do iterative data modeling. I’m agile. I know it’s more work for me. I’d love to have the luxury of spending six months embedded with the end users coming up with a perfect and lovely logical data model. But that’s not the project I’ve been assigned to. It’s not the team I’m on. To work against the team is a demand that no data modeling be done and that database and data integration be done by non-data professionals. You can stand on your side of the cubicle wall, screaming about how LDMs are more important, or you can work with the data-driving modeling skills you have to make it work.

Are Your Data Models Agile or Fragile: Sprints
When I’m modeling, I’m working with the business team drawing out more clarity of their business rules and requirements. I am on #TeamData and #TeamBusiness. When the business sees you representing their interests, often to a hostile third party implementer, they will move mountains for you. This is the secret to getting CDMs, LDMs, and PDMs done on modern development projects. Just do them as part of your toolkit.  I would prefer to data model completely separately from everyone else. I don’t see that happening on most projects.

The #TeamData Sweet Spot

My sweet spot is to get to the point where the DBAs, Devs, QA analysts and Project Managers are saying "hey, do you have those database printouts ready to go with DDL we just delivered? And do you have the user ones, as well?" I don’t care what they call them. I just want them to call them.  At that point, I know I’m also on #TeamIT.

The key to getting people to at least appreciate logical data models is to just do them as part of whatever modeling effort you are working on.  Don’t say “stop”.  Just model on.  Demonstrate, don’t tell your teams where the business requirements are written down, where they live.  Then demonstrate how that leads to beautiful physical models as well. 

Logical Data Modeling isn’t dead.  But we modelers need to stop treating it like it’s a weapon. Long Live Logical!

 

Thanks to Jeff Smith (@thatjeffsmith | blog ) for pointing out the original post.

7 Databases in 170 Minutes: Workshop at NoSQLNow!

Jan 26, 2016   //   by Karen Lopez   //   Blog, Database, Database Design, DLBlog, Events, NoSQL, Speaking, Training  //  No Comments

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My friend Joey D’Antoni ( @jdanton | blog ) and I will be giving a workshop at NoSQLNow! about new database and datastore technologies like Hadoop, Neo4j, Cassandra, Vertica, Document DB, and others.  This will be a fast-paced, demo-heavy, practical sessions for data professionals.  We’ll talk about where a modern data architecture would best use these technologies and why it’s not an either/or question for relational solutions in a successful enterprise. And, as always, our goal is to make the time we spend fun and interactive.   This session will be a great starting point for some other session on Monday that go into data modeling for NoSQL as well as for all the other in-depth, database-specific talks the rest of the week.

Sunday, April 17, 2016
Level:
Intermediate

imageWe’ve been busy keeping relational data consistent, high quality, and available. But over the last few years, new database and datastore technologies have come to the enterprise with different data stories. Do we need all our data to be consistent everywhere? What does data quality mean for analytics? Will we need relational database?

Learn how traditional and new database technologies fit in a modern data architecture. We will talk about the underlying concepts and terminology such as CAP, ACID and BASE and how they form the basis of evaluating each of the categories of databases. Learn about graph, Hadoop, relational, key value, document, columnar, and column family databases and how and when they should be considered. We’ll show you demos of each.

Finally, we will wrap up with 7+ tips for working with new hybrid data architectures: tools, techniques and standards.

 REGISTER

Use code “DATACHICK” to save:

$100 off for  Tutorials Only + Seminar Only Registration and $200 off for Full Event, Conference+Tutorials, Conference +Seminar, and Conference Only Registration.

Super early registration ends 29 January, so take advantage of both discounts now (yes, they stack!).

Database Design Throwdown, Texas Style

Jan 21, 2016   //   by Karen Lopez   //   Blog, Data, Data Modeling, Database, Database Design, DLBlog, Events, Fun, Snark, Speaking, SQL Server  //  3 Comments

SQLSaturday #461 - Austin 2016

It’s a new year and I’ve given Thomas LaRock (@@sqlrockstar | blog ) a few months to recover and ramp up his training since our last Throwdown.  The trophies from all my wins are really cluttering my office and I feel back that Tom has not yet had a chance to claim victory.  So we will battling again in just a few days.

I’ll be dishing out the knowledge along with a handkerchief for Tom to wipe up his tears at SQL Saturday #461 Austin, TX on 30 January 2016.  This full day community-driven event features real database professionals giving free presentations on SQL Server and Data Platform topics.  All you need to do is register (again, it’s free) before all the tickets are gone.

Database Design Throwdown

Speaker(s):  Karen Lopez Thomas LaRock

Duration: 60 minutes

Track: Application & Database Development

Everyone agrees that great database performance starts with a great database design. Unfortunately, not everyone agrees which design options are best. Data architects and DBAs have debated database design best practices for decades. Systems built to handle current workloads are unable to maintain performance as workloads increase.Attend this new and improved session and join the debate about the pros and cons of database design decisions. This debate includes topics such as logical design, data types, primary keys, indexes, refactoring, code-first generators, and even the cloud. Learn about the contentious issues that most affect your end users and how to avoid them.

One of the other great benefits of attending these events is that you get to network with other data professionals who are working on project just like yours…or ones you will likely work on at some point.

Join us an other data pros to talk about data, databases and projects. And make sure you give a #datahug to Tom after the Throwdown. He’s gonna need it.

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