Unified Interior Regions. Science Centers. Frequently Asked Questions. Educational Resources. Multimedia Gallery. Web Tools. Board on Geographic Names. The National Map. USGS Library. USGS Store. Park Passes. News Releases. Featured Stories. Science Snippets. Technical Announcements. Employees in the News. Well, not exactly. With up to date documentation developers, report creators, data analysts, and organizations as a whole can:. What I like about the tool is that you can generate good looking documentation of your databases in a couple of minutes.
It is fairly simple and small tool. And yet, with features like documentation repository, teamworking, advanced metadata capabilities, diagrams, etc.
A notable feature of the new release, something I have seen only in advanced modeling tools which might be too heavy for documenting database schema, and definitely not user-friendly for describing data dictionaries and clunky exports is the ability to document models that span many different databases, servers or even platforms i.
You can define a link between any two tables, no matter to which database they belong, and mix those tables in the diagrams. This is one of a key features of the tool — easy way and publish a Data Dictionary a definition and description of all the tables and columns.
You can document not only tables, but many other database objects. Full documentation — database schema, descriptions and diagrams are exported to a nice HTML document. One of the issues when managing documentation outside of the database is that when schema changes it is really hard to keep the documentation up to date and in sync with the database. Dataedo handles this nicely. The finance team may use a definition of 'cost of acquisition' that is different from the product team, which may use a different definition of 'engagement' from the data science team, and so on.
Unsurprisingly, teams can get territorial about their definitions especially if changing them would make their numbers look worse. It is best practice to create a data dictionary and thus a single source of truth for all your definitions.
This process is one of the most valuable things the data team can provide for the company. Let's dive into the best and worst methods you can use to create your data dictionary. In this section, I'll focus on comparing data dictionary tools; if you want to know what info your data dictionary should contain, skip ahead to the final section titled " Best Data Dictionary Template. At first glance, our old friend, the spreadsheet seems like a straightforward method.
A spreadsheet is pretty fast to set up, but that is about the only positive we can think of ; once you have created the first version, the nightmare begins.
Having the data team control everything could create a bottleneck, but allowing everyone to edit could lead to incorrect changes. Spreadsheets are better than nothing but will cause you headaches if you use them for any prolonged period. Even though they are speedy to set up, managing them will take much more time than using a tool designed for the purpose.
So, we recommend using spreadsheets as a last resort or as an intermediary while you get a more sophisticated tool set up.
All database management systems DBMSs give you the option to annotate your data, which is like a mini data dictionary service built into the system. You can write comments or descriptions about all the data within the database. Plus, you can modify and track the comments so maintenance is much easier than with a spreadsheet. In addition to this, the data and its explanation are close together.
This closeness reduces friction and means more users will serve themselves rather than opening tickets with the Data team. For one thing, your DBMS provider limits the amount of information you can include in comments.
You can only use the fields your database gives you, which may not be what you want. If you have everything stored in one place , this issue i s probably manageable, but if you use multiple databases, you will not have a single source of truth. Users would then have to hunt through databases to find definitions which adds a ton of friction; the editors are also pretty unwieldy.
The biggest problem is the limited space that DBMS comments give you, which is why you should note the difference between database annotations and data dictionaries. The former provides a brief often technical overview of the term, perhaps using SQL syntax; the latter is often wordier and written in less technical language for a more general audience.
For example, how do you explain that the NaN values in September are there because of a known server error? How do you tell others you already know the query is inefficient, but you are happy with the speed right now for X and Y reasons?
These wordier questions are where database annotations stop, and data dictionaries take over. While database annotations are a vital part of your data architecture, they are not a replacement for a data dictionary; they complement them. Due to the unwieldy nature of the built-in database annotation tools discussed above, some companies have created database documentation tools.
Most of them can connect to multiple databases from different providers simultaneously , read the data and automatically generate documentation. They also have write access; thus, you can update database annotations from within the tool. This combination makes maintaining your docs a million times easier since whenever there is a change to the database, the docs are automatically updated and vice versa. If the source-of-truth is populated first by a process, then the same process can subsequently do the same for all rows that reference it.
The process created above did not include the ability to populate foreign key metadata automatically, but this could be done quickly and accurately. The only caveat is that a database needs to use foreign keys to track dependencies. Next, within the dynamic SQL after row counts are collected, a new block of T-SQL is added to the stored procedure that collects all foreign key metadata from the current database:.
This query joins a handful of system views to collect and organize details about every foreign key defined in the database, including column numbers, in case it is a multi-column foreign key. Rerunning the stored procedure with these changes in place results in foreign key metadata being added to the table. The following is a sample of the newly populated data:.
To test this, sample data will be added to a primary key column:. If desired, a process could be added to the data dictionary population that joins columns that reference a primary key column and copies data elements that are typically manually entered.
A list could be made of columns that this feature should be used for, or all columns could be acted on. A sample query that acts on all columns in this fashion would look like this:. This T-SQL parses the foreign key column and updates notes for all data elements that foreign key directly to a primary key column that has notes entered.
The following query can validate the results, returning all ProductID columns that now have notes entered in them:. The results show 12 data elements that have been automatically updated from the update query above:. This process is highly customizable and allows for data to systematically be updated in one place and propagated throughout the data dictionary automatically.
This example only updated the notes, but the same process could update any other column for which the metadata would be similar or identical to its parent object. The ultimate goal here is to save time by reducing as much as possible the manual steps required to maintain the data dictionary. A data dictionary is not a fully-functional documentation system in itself.
It contains details about data elements that can fuel documentation, but this is not a system that is friendly to non-engineers. If a sales manager wants to know what metrics could be useful for a new quarterly report, they have one of two options:. Ideally, when a data dictionary nears completion, taking time to determine how to integrate it into existing or new documentation systems is important. This is the step that makes this information readily available to an organization, both for technical and non-technical folks.
This data dictionary is exceptionally flexible, and its user can choose to add, remove, or change the details of any data element. The processes described in this article can be reused to populate any amount of columns, metrics, or metadata. The only limit is the imagination of the person implementing it.
Different systems will have different documentation needs. For example, the application name, service name, or software build a column was introduced could be added for analysts with development-leaning needs. Similarly, for operators interested in performance or server details, metrics such as space used, fragmentation, or reads could add those types of details as well.
Adjusting and adding to the code used here can allow for additions, modifications, or removals to be made with minimal effort or risk of breaking it. A data dictionary is a versatile documentation tool that can save time, resources, and money. It can systematically reduce or remove the need for frequent, error-prone, and manual updates to extensive documentation systems. In addition, a data dictionary can be automated to further reduce the maintenance needed as applications grow and develop.
Because it is stored as a table in a database, this data dictionary can be connected to applications easily and consumed by analysts, documentation, or development teams in need or clarification on what a data element is, where it comes from, or how it works. In addition, an application can be used to update data in the data dictionary, further removing a database administrator, developer, or architect from the manual tasks of maintaining documentation and moving those tasks to the people closest to the documentation itself.
Processes that populate a data dictionary can be run regularly to maintain and update automatically populated fields, such as row counts, foreign key data, or auto-updating metadata. While a homegrown solution is not right for every organization, it provides an easy starting point to realize the benefits of an organized documentation system and highlights the reasons why good database documentation is critical to the success of any teams that rely on databases for the success of their applications.
Customization allows for a data dictionary to be tailored to fit the needs of any application. The code presented in this article provides a starting point from which a data dictionary can evolve to be as efficient as possible. Feel free to take this code and produce the perfect documentation system for a given organization or application. Try it free. Fortnightly newsletters help sharpen your skills and keep you ahead, with articles, ebooks and opinion to keep you informed.
In his free time, Ed enjoys video games, traveling, cooking exceptionally spicy foods, and hanging out with his amazing wife and sons. View all articles by Edward Pollack. What is a data dictionary? Can use this data in other applications via APIs or reporting processes No process is without downsides.
Building a data dictionary from scratch like anything homegrown entails: Time Resources Maintenance The larger the project, the more work this will be, and it is quite likely that an exceptionally complex documentation project would benefit from a careful analysis of different approaches to ensure the best one is chosen. Why a data dictionary? Why not system views? FROM sys. ON tables. ON types.
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