WhereScape faking Data Vault?

Update (2016-07-09): It was true then, but it isn’t true now. WhereScape is now working with Dan Linstedt, the creator of the Data Vault methodology, to deliver automated Data Vault 2.0 solutions.

https://www.wherescape.com/blog/blog-posts/2016/july/wherescape-partners-with-data-vault-inventor-dan-linstedt/

 

I’m a little confused by recent claims about Data Vault and WhereScape Red.

Every WhereScape demonstration I have seen has been Load, Stage, Dimension, Fact, Cube. The tutorial is Load, Stage, Dimension, Fact, Cube. The training is Load, Stage, Dimension, Fact, Cube. The UI boils down to Load, Stage, Dimension, Fact, Cube.

Does that look like Data Vault to you?

Nowhere do I see Hub, Link and Satellite. Just Load, Stage, Dimension, Fact, Cube.

If it looks like Dimensional, presents like Dimensional, trains like Dimensional, and is documented like Dimensional, then there is a pretty good chance it IS Dimensional, and any claim to the contrary is just faking it.

If you want Data Vault, you probably need to be looking at BIReady or Quipu.

We’re proud of the fact that Ajilius is a data warehouse automation company based on Dimensional Modelling. We believe in doing one thing well, not trying to be anything to everyone. If you want a Dimensional data warehouse, buy the product that is firmly committed to this technique.

Ajilius. Keeping it real in data warehouse automation.

DWA vs ETL

A common question is the difference (or similarity) between Data Warehouse Automation and traditional ETL tools.

I like to use an example from my iPad – the difference between the apps Mortgage Calc andPages.

Pages is a spreadsheet. You can edit rows and columns of data, and create formulae using that data.

Mortgage Calc is an app that calculates mortgage payments.

Now, I could write a mortgage calculator in Pages. I could possibly make it look like the Mortgage Calc app rather than a spreadsheet. But which calculations do I use? Which tax rules apply? Are there stamp duties payable? In other words, I have to do a lot of research, a lot of programming, and a lot of testing to make sure I’ve got the basics right. And I’ve also got to maintain that spreadsheet as the rules change.

With Mortgage Calc, I’ve paid a few dollars for an application that has saved me many hours of research and development, and which I’m trusting to give me accurate calculations. In this case, Mortgage Calc is better than Pages, because it does one job, and does it well.

That is the difference between DWA and ETL. An ETL tool is a general purpose programming environment for moving and transforming data between systems. It provides components, in one form or another, which you put together to accomplish one or more tasks.

DWA, on the other hand, is built to do just one task, which is building the code associated with a data warehouse. Ajilius builds dimensional data warehouses. We build transactional, periodic snapshot and accumulating snapshot fact tables; Type 0, 1, 2 and 3 slowly changing dimensions; and move data from multiple data sources into a consolidated presentation layer.

You could do all of that with an ETL tool, but it would be like writing a complex mortgage calculator in a spreadsheet – time consuming, not well understood, and prone to error.

Ajilius generates fast, error free code, that can be easily migrated between data warehouse platforms, at the press of a button.

That’s the advantage of tools like Ajilius. We deliver business value, faster.

Our join editor sucks

I’ve spent today reviewing and discussing our alpha-test feedback.

The best feedback related to the browser UI. The worst feedback to the join editor.

The join editor is used to define the joins between staging tables. We need to know how the business keys from the table being joined relate to the data already in the table.

The way the current version works is that you choose the ‘Join Another Table’ option from the Stage Table menu, then select the table you want to join, finally choosing the join type (ie, inner, left outer) and the join columns. You’d repeat this for as many tables as you wanted to join.

Users gave solid feedback that this did not represent their use cases. It forces the user to make early decisions about the sequence in which tables will be added, doesn’t handle deletion of tables from the join well, and fragments the user’s mental model of the join structure.

We worked through a few alternatives this afternoon, decided on an approach, and now Minh (our Vietnam developer) is due to have it completed by the end of the week. We’ll go back to the alpha users for a re-check on this feature, but then everything should be clear for the beta.

Be a DW hero with PostgreSQL

PostgreSQL and Ajilius can make you a DW Hero.

Lots of organisations don’t know about PostgreSQL, or are afraid that it might not perform. Here are eight simple steps to prove the value of PostgreSQL for data warehousing.

(1) Develop on SQL Server

Your IT management has probably asked you to deliver your data warehouse on SQL Server. There is nothing technically wrong with that decision, but it is going to cost a lot of money if you take it into production. Never mind, let’s humour them, go ahead and implement your development environment. We emphasise development, because using MSDN or SQL Server Developer Edition licences will have a negligible cost.

(2) Develop using Ajilius

Design and build your data warehouse using Ajilius. Use the power of data warehouse automation to generate a fully scripted, high performance data warehouse. Get all your user and technical documentation, and start testing user queries and reports. You’ll save hundreds of hours of development time using Ajilius, and eliminate the risks of bad ETL.

(3) Set up a PostgreSQL server

Download PostgreSQL and set up a separate development server, running on your choice of operating system. Create an empty database, and record the server name, database name, user-id and password.

(4) Clone your Metadata Repository

Go to the Warehouse List screen, and select the Clone Warehouse option. An independent copy of the metadata repository will be created under your chosen name.

(5) Change your Warehouse target

Go to your Warehouse List screen, and select the Change Warehouse option. Now select PostgreSQL as your target type, and enter the server details that you recorded in step (3).

(6) Generate Scripts

Go to your Warehouse List screen, and select the Generate Scripts option. Check each of the Create, Update, Schedule and Migrate script options, and enter the directory where scripts are to be written.

(7) Deploy

You now have a 100% compatible version of your original data warehouse, running on PostgreSQL. Because you used Ajilius to build your data warehouse, you are guaranteed portability across data warehouse platforms. All your extracts and loads have been fully replicated, your data has been migrated, and you can repeat your test queries and reports.

(8) Profit!

Here is where you become a DW Hero.

Demonstrate to your project stakeholders their data warehouse running on PostgreSQL. Discuss the cost savings that will come from running on this platform. Show that you’ve already done the migration, in just minutes of work and a few hours of processing time. Imagine proving that you can save hundreds of thousands of dollars in production licensing, for no cost to your organisation.

That’s HEROIC!

We’re the only data warehouse automation company that fully supports PostgreSQL data warehouses as a first-class citizen, and we’ve done so from the very first lines of code we wrote.

Read more about Ajilius, and the power of the PostgreSQL Data Warehouse.

Virtual loads and dimensions

Some data warehouse automation products do a good job on green-fields projects, but make it very difficult to integrate into an existing data warehouse architecture. Some don’t do it at all, while others require that you reverse engineer and rebuild the ETL / ELT processing before you can integrate new tables. We’re different.

Ajilius makes it easy to integrate with an existing data warehouse through Virtual Tables.

The purpose of a Virtual Table is to provide a mechanism to create Ajilius metadata over an existing table, without having to re-load or re-process its data. There are two types of Virtual Tables used in Ajilius – Virtual Loads and Virtual Dimensions.

Virtual Loads exist to define data that has already been loaded into the warehouse by other processes. This scenario is used where the table is to be processed in conjunction with data completely controlled by Ajilius, and may also be used in drip-feeding scenarios.

Virtual Dimensions enable you to integrate complete dimensions that have been created and maintained by external processes. If your existing warehouse has already built the perfect SCD2 dimension, there is no need to re-design and re-write that table just to fit it into our metadata structures.

Once defined, Virtual Tables work exactly the same as any other Ajilius load or dimension table.

Virtual Tables are a powerful concept when you are adding the power of Ajilius to an existing data warehouse environment.

PostgreSQL data sources

PostgreSQL can be a data source for any data warehouse built using Ajilius.

If you are using PostgreSQL as your DBMS for ERP (ie, xTuple or Odoo), HR (Advantec, perhaps) or CRM (such as OroCRM, Bitnami or Tryton), then Ajilius can easily bring data from these systems into your data warehouse.

We offer two sourcing mechanisms for PostgreSQL data, optimised depending on the target platform.

Low latency applications, such as near real-time retail transaction warehouses, are best supported by direct connections from the target database to the source. We use Foreign Data Wrappers from a PostgreSQL warehouse, or the PGNP OLEDB connection for Linked Servers from Microsoft SQL Server.

Where latency is not an issue, it can sometimes be faster to use bulk copy processes to extract and load via disk files. In this case we use the COPY bulk export utility.

Extracts from PostgreSQL are also required for cloud-hosted warehouses, such as Redshift, drawing data from on-premise systems. Ajilius is perfectly able to handle this requirement due to its scripted nature.

Building data warehouses from PostgreSQL data sources just got a lot easier.

PostgreSQL and Ajilius

PostgreSQL is a great option for dimensional data warehouses. Using Ajilius to build and deploy your PostgreSQL data warehouse helps you to get the most out of this exciting platform.

The biggest advantage of PostgreSQL is that it is free. At a time when competing databases cost tens of thousands of dollars per core, and typically require 8 or more cores in production, PostgreSQL could cut more than $100,000 from your software budget.

Over the next week, we will publish a series of posts that describe how you can use Ajilius and PostgreSQL to maximum effect, wrapping up with some advice that could make you a DW Hero in your organisation.

Stay tuned!