Category: DataAutomation

High-Level Design Documentation

Sep 15, 2021 by Simon Meacher

Engaging Data Explains:

High Level Design Documentation –

Have you ever needed to create high-level documents of your data automation that explains a project/sprint within your WhereScape Red repository? Maybe so it looks a little like the above?

We recently worked with a client who wanted to represent the amount of change undertaken within a single project. They required something simple yet demonstrated the amount of change within each layer of the data automation.

Instead of creating something new, we re-used the WhereScape RED Overview design, WhereScape used to illustrate the design of the architecture.

A sample data solution shaped into the high-level design.

Engaging Data consultants worked with the client to create a solid naming convention and development standards. With this foundation and the metadata repository, we developed a script that produced an HTML document with details the client was looking for.

The idea continued to develop and now has options to include the following details:

  • Number of Jobs, Procedures and Host Scripts that support each layer.
  • Data Volume per object and summarised per layer
  • Processing time for each layer, with avg completion time & avg time to run

WhereScape RED and 3D speeds up development & documentation of the technical design. This solution utilises the metadata to create support or narrative documents for other business areas.

Build re-usable scripts, dashboards or reports for non-technical business teams & provide clarity around the technical function of your automation.

If you are interested in receiving a copy of the script that produced this report, please email

Data Warehousing Concepts Explained

Aug 3, 2021 by Carl Richards

Engaging Data Explains:

Data Warehousing Concepts

Modern commerce is an environment in which companies are increasingly being required to make complex, data-backed decisions. Dealing with vast amounts of information has become an essential feature of a business, which can often lead to siloed data. This is difficult enough to store, let alone analyse or understand.

In many cases, business use demands require a more sophisticated system, improving data management and providing a holistic overview of essential aspects of the company. One of the best ways to achieve this is to invest in a data warehouse. Yet, many companies are still unaware of what this entails or how it can help their business.

What is Data Warehousing?

In simple terms, a data warehouse is a system that helps an organisation aggregate data from multiple sources. Instead of experiencing the sort of separation and siloing discussed previously, data warehousing makes it possible to draw together information from disparate sources. It’s almost akin to a universal translator of languages. Typically, data warehouses store vast amounts of historical data, and this can then be utilised by engineers and business analysts as required.

Data warehousing is particularly valuable as it essentially provides joined-up information to a company or organisation. This was quite impossible until relatively recently, as data has always been based on separate sources of information. Transactional systems, relational databases, and operational databases are often held entirely separately, and it was almost unthinkable until recently that the data from the systems could be effectively combined.

But in this Information Age, companies are seeking a competitive advantage via the leveraging of information. By combining the vast amount of data generated together into one source, businesses can better understand and analyse key customer indicators, giving them a real insight into the determining factors of the company. Data warehousing can build more robust information systems from which businesses can make superior predictions and better business decisions.

In recent years, the escalation and popularisation of the cloud has changed the potential of data warehousing. Historically, it was more usual to have an on-premise solution, which would be designed and maintained by a company at its own physical location. But this is no longer necessary. Cloud data architecture makes it possible to data warehouse without hardware, while the cloud structure also makes implementation and scaling more feasible.

Data Lakes

However, those who are uninitiated in deep data topics may encounter terminology that can be somewhat baffling! The concept of a data lake seems rather surreal and tends to conjure up imagery that is, ultimately, completely useless! Inevitably, people who have never encountered the concept of data lakes before find themselves imagining an expanse of azure water glittering in the sunlight. Well, data lakes aren’t quite like that.

A data lake is used for storing any raw data that does not currently have an intended use case. It really can be seen as similar to the wine lakes that used to be in the news quite regularly, but it doesn’t seem to be a talking point any longer! You can equally view a data lake as a surplus of information; it is data that may become useful in the future but does not have an immediate usage at this point in time. Thus, it is stored away in a lake until it can be consumed adequately.

This differs from data warehousing, which is used to deal with information that is known to be useful more efficiently. Data warehousing may deal with data stored in an impenetrable format. Still, there is a clear use case for understanding this information, or it needs to be stored for a particular reason.

When to use a Data Warehouse

There are a variety of reasons that a company or organisation would choose to utilise a data warehouse. The most obvious would be as follows:

  • If you need to start a large amount of historical data in a central location.
  • If you require to analyse your web, mobile, CRM, and other applications together in a single place.
  • If you need more profound business analysis than it has been possible to deliver with traditional analytic tools, by querying and analysing data directly with SQL, for example.
  • In order to allow simultaneous access to a dataset for multiple people or groups.

Data warehousing makes it possible to implement a set of analytical questions that would be impossible to address with traditional data analytics tools. Collecting all of your data into one location and source makes it possible to run queries that would otherwise be completely unfeasible. Instead of asking an analytical program to continually run back and forth, back and forth between several locations, the software can get to grips with one data source and deliver efficient and more holistic results.

Data Warehouse Factors

Many businesses now require data warehousing services to deal with the vast amount of data that is now generated. And that ‘many businesses’ will rapidly become ‘most businesses’, and then ‘virtually all businesses in the near future. But those that are inexperienced in this field are often confused about what factors to take into consideration.

Thus, we would recommend looking at these six key elements when considering warehousing:

  • The sheer scale of data that you wish to store.
  • The type of information that you need to store in the warehouse.
  • The dynamic nature of your scaling requirements.
  • How fast you require any queries to be carried out.
  • Whether manual or automatic maintenance is required.
  • Any compatibility issues with your existing system.

Concerning the first of these factors, data can be somewhat different in terms of its basic structure. Some data may be highly complex, but it can still be quantifiable, easily organised. However, in the era of Big Data there is a vast amount of unstructured data, which cannot be easily managed and analysed. Companies that generate a vast amount of unstructured data and need to collate and understand it are certainly excellent candidates for a data warehousing solution.

There is a lot to learn when it comes to the subject of data. And it can frankly be a little daunting at times. But what is certain is that this topic isn’t going anywhere. Big Data is here to stay. That’s why we have created our Data Vault 2.0 solution. Data Vault can ideally serve your organisations’ data needs when this is becoming an issue of paramount importance.

Automated Deployment

Jun 29, 2021 by Simon Meacher

Engaging Data Explains:

Automated Deployment

Continuous Delivery using Octopus Deploy

With WhereScape RED

Many companies are looking to make Code changes/deployment easier. Often the ability to deploy code to production is surrounded by red tape & audited control. If you don’t have this, count yourself lucky!

Jenkins & Octopus Deploy are two, to name a few (see here), that are helping to automate the deployment of code to production. Allowing companies to adopt a continuous deployment/delivery approach.

For a long time, WhereScape RED has had its own method of automating deployment, using the command line functions.

Why Automate?

Using tools such as WhereScape RED allow elements of automating deployments; however, we know that companies like to use a common toolset for their code deployments; like having a single picture of all the deployments and, in most cases, realise that they want to release multiple code deployments on different platforms because RED doesn’t do everything.


No problem! There are several ways to do this. Our perfered option is to push the deployment application to the code store respository. Afterall, it is more practical to store the changes you want to push to Production and not every change to any objects, including those that are not meant for Production!

Can I do This Now?

WhereScape RED uses a command prompt file to send commands to the admin EXE. All changes will be applied to the destination database (via ODBC). Installation settings/config is set using XML & a log file is created as part of the process. The XML file contains the DSN of the destination database. Let’s come back to this point later. The XML contains all of the settings that are applied when deploying the application. Settings like Alter or Recreate Job. Please make sure you have this set correctly. You do not want to re-create a Data Store table to lose the history!

Permissions are important. The key to running the command line to publish changes to production is that the service account executing the commands has permissions to change the underlying database.

Integration with Octopus

Octopus deploy uses PowerShell as it’s common execution code. So we have adapted all of our WhereScape BAT files to PowerShell in order to get everything working.

Building a list of repeatable tasks within Octopus is easy & provides an opportunities to create a standard release process that meets with your companies standards/processes. Tasks like database backup, metadata backup and much much more!

It can even run test scripts!

We used a PowerShell script to create a full backup of the database, to be used should the deployment fail. With a larger database, this may not always be the best solution. Depending on your environment set up you may have options to use OS snapshots or other methods to roll back the changes. The good news is Octopus Deploy works with most technology, so you should find something that works for your technology stack.

Recently, we been playing with creating rollback WhereScape applications on the target data warehouse. This is great for restoring the structure of the objects quickly and easily. Reducing risk is a great value add!

Go, Go, Go!

Triggering the deployment was easy, we could set this up in many ways, but used “does the application files exists” trigger to get things started – until the humans learned to trust the Octopus process.

However, linking the release to Jira is just as simple. Imagine, you’ve completed development and want to sent the code to UAT. You click the button to update the ticket…….wait a few seconds…..and the code is deployed! It’s complicated to set up, but you get the idea.

Final Thoughts

Octopus is a great tool and the automation really helps to control the process of deployments. Coupled with WhereScape automation, this provides and excellent end to end solution for data warehousing.

If you are interested in CI/CD and WhereScape RED/3D, book a call us and find out how it could help your team.

Contact Us

* indicates required

Documenting the Modern Day Data Warehouse

May 28, 2021 by Simon Meacher

Engaging Data Explains:

Documenting The Modern Day Data Warehouse

When you’re operating a modern-day data warehouse, documentation is simply part of the job. But it’s not necessarily the easiest or most logistically straightforward part of the process, while also being important. Documentation is, in fact, invaluable to the continued development, expansion, and enhancement of a data warehouse. It’s therefore important to understand everything that is entailed in adequately documenting, in order to ensure that your data warehouse processes run smoothly.

Understanding your Audience

One of the first things to understand is who you are compiling the documentation for. Support, developers, data visualisation experts, and business users could all be possible recipients. Before you answer this question, you really need to fully understand the way that your organisation operates, and open the lines of communication with the appropriate departments.

A two-way dialogue will be productive in this ongoing process. This process of communication will then help ensure that you keep the documents in line with the design. This is vitally important, as any conflicts here can render the whole process less than constructive than is ideal.

And it’s especially vital considering how fast documentation moves nowadays. Everything has gone online, and is based on Wiki. Whether it’s Confluence, SharePoint, or Teams, all sorts of Wiki documents are being produced by businesses with the intention of sharing important information. These shareable documents are updated with increasing regularity, meaning it is important to get your strategy in place before beginning.

Different approaches to data warehouse design can also impact the amount of time that a document is live before being updated. If you are lucky enough to make weekly changes to your data warehouse, you will be making incremental changes to the documentation itself. Development teams spend hours on updating the documentation rather than doing what they are good at….developing data solutions! Naturally, minimising this where possible is always preferable. 

Self-Service Business Intelligence

Documentation is also crucial in self-service business intelligence. The integration of private and local data in this area, into existing reports, analyses or data models, requires accurate documentation. Data can be drawn in this area from Excel documents, flat files, or a variety of external sources.

By creating self-service functionality, business users can quickly integrate data into what can often be vital reports. Local data can even be used to extend the information delivered by data warehousing, which will limit the workload that is inevitably incumbent on data management. The pressure on business intelligence can be quite intense, so anything that lessens the load is certainly to be welcomed.

Another important aspect of documentation is that it reduces the number of questions that are typically directed at the IT and data warehousing teams. One thing anyone that works in IT knows only too intimately is the vast amount of pressure that can be heaped upon them by both internal and external enquiries. Again, anything that reduces this will certainly be favourable.

The data warehouse team also has huge responsibility within any organisation. They are required to produce a vast amount of information for front-end business users, and getting documentation right can certainly assist with this process.

Importance of Transparency

One important aspect of documentation that can sometimes be overlooked is the importance of transparency. This works on every level of an organisation, with the importance of sharing everything related to documents absolutely vital. Once this level of transparency is implemented, people who understand the data deeply can improve the documentation, or suggest changes to the Extract, Transform, and Load (ETL) and Extract, Load, and Transform (ELT), if this is indeed deemed necessary.

Conversely, it’s also important to understand that not all technology is suitable for documentation. As much as businesses and organisations would love this process to be completely holistic, this is not always possible.

Thus, packages such as Power BI, QlikView and QlikSense, and even Microsoft’s trusty Excel, are not necessarily ready to be documented. These software packages can use data, but often do not have the ability to provide a document set that explain how the data is being used, and for what purpose. Recently, Power BI has taken steps to ensure that the app can help with data lineage, but this remains better suited to IT teams, as opposed to Business Users.

Attempting to document data across multiple technologies is tricky, but Wikis can provide IT teams with the ability to collate all of this information into a central hub of knowledge, making access much more logistically convenient.


Ultimately, IT departments, data warehousing teams, and report developers should all be encouraged to produce documentation that contributes to the overall aims of their organisations. Anything excessively technical is not good enough for modern business requirements, especially considering the importance of communication, and of ensuring that everyone within an organisation is acquainted with as much vital data as possible.

Modern-day technology makes this goal a reality, and this means that it is increasingly an expectation of end-users. Failing to prepare properly in this area could indeed mean preparing to fail, as organisations will simply have failed to meet the compelling desires of the market. It is therefore vital for documentation to be dealt with diligently.

Getting this piece right, will go a long way to help with data governance!

If you would like to know more about how Engaging Data help companies to automate documentation, please contact us on the below.

Contact Us

* indicates required

Big Data and DataVault

May 10, 2021 by Carl Richards

Engaging Data Explains:

Big Data and DataVault

Knowing how and where to find the needle more easily, and where in the specific haystack it resides

Big Data has been a hot potato topic for more than a few years now, and this phenomenon will play a central role in the future of commerce. Collecting, collating and comprehending Big Data will no longer be a matter of commercial interest; it will instead increasingly become a commercial imperative.

It should come as no surprise then that investment in technologies related to Big Data is already becoming almost ubiquitous. A report from NewVantage Partners, which collected executive perspectives from 60 Fortune 1000 companies, found that 97% of them invest in Big Data and AI initiatives. NewVantage also discovered that the vast majority of this investment (84%) was focused on deploying advanced analytics capabilities to enable business decision making.

Big Understatement

And when we use the term ‘Big Data’, it’s reasonable to conclude that ‘big’ is an understatement! For example, in 2018, Internet users generate approximately 2.5 quintillion bytes of data every day. That’s 912 quintillion bytes every year! And 90% of this data has been generated in just the last five years. The rate of growth and development of this curve is exponential.

Thus, it’s one thing to recognise the importance of Big Data, and quite another to be prepared for it. We’re talking about a veritable avalanche of information! In many cases, utterly unstructured information. Indeed, Forbes noted in 2019 that 95% of businesses cite the need to manage unstructured data as a problem for their business. Which, given the sheer scale of Big Data, is hardly surprising. Making the most of Big Data is not so much searching for a needle in a haystack; more like looking for a needle in a universe entirely comprised of haystacks.

This reality means that implementing the best business intelligence solutions will become essential. Dealing with the sheer volume of Big Data will demand this. And data warehousing is one element of this process that will be critically important. The analytical qualities delivered by this aspect of the overall Big Data management process will prove critical in the success of the efforts of companies to benefit from the information explosion.

Data Vault 2.0

That’s where Data Vault comes in. Data Vault 2.0 comprises a raft of sophisticated architecture and techniques that enable businesses to both store current and historical data in a singular and easily accessible location, along with the ability to create analytics based on this information. Data Vault is effectively a unique design methodology for large scale data warehouse platforms, ensuring that Big Data is dealt with more quickly, more efficiently, and more effectively.

Data Vault offers several advantages over competitors. The first reason for this is that it’s possible to convert any system to Data Vault determinations. This means that existing objects can be translated to Data Vault entities, and every single item will have a corresponding match in the new Data Vault architecture. Every main definition can then be mapped by hubs and every relationship between these via links. This means that the whole operation is more flexible and user-friendly.

Another significant advantage of Data Vault is its enhancement of agility. This is particularly important, as the ability of network software and hardware to automatically control and configure itself makes it easier to deal with the almost unfathomable scope of Big Data.

Smaller Pieces

Data Vault makes it possible to divide a system into smaller pieces, with each individual component available for separate design and development. This means every constituent part of the system can have its own definitions and relationships and that these can be combined at a later date by related mapping. This makes it possible to develop a project steadily yet still see instant results. It also makes managing change requests much more straightforward.

Another asset of the Data Vault approach is that it applies to numerous different systems. This means that separate sources can be transformed into Data Vault entries without any laborious procedures being involved. It is particularly advantageous in the contemporary climate, as almost every enterprise system relies on several different data types from various data sources.

The Data Vault modelling technique is thus adaptable to all types of sources, with a minimum of fuss. This makes it much more feasible to link different data sources together, making analysis more joined-up and holistic. It is well-known that being the entity that is the most adaptable to change is vital across a wide variety of niches, and this applies in the rapidly evolving data analysis environment.

But possibly the most compelling reason to choose Data Vault is that our offering provides companies with a method of standardisation. With Data Vault implemented, companies can standardise their entire DWH system. This standardisation enables members of the company to understand the system more easily, which is undoubtedly advantageous considering the innate complexity of this field.

Meeting the Needs

It is commonplace for complex and sophisticated solutions to be delivered to business users, which nevertheless fail to understand and adapt to the company’s actual requirements in that area. Everyone wants to show off their fancy piece of kit, but often developers aren’t as keen to listen! This can manifest for a variety of reasons. Still, the important thing to note is that Data Vault is designed to meet the requirements of the business, rather than requiring a business to reorganise itself to comply with the needs of the package.

This is important at a time when the dynamic complexity associated with data is escalating. Enterprise data warehouse systems must provide accurate business intelligence and support a variety of requirements. This has become a critical reality in a business marketplace in which the sheer volume of data being generated is overwhelming.

Data Vault solves these problems with a design methodology that is ideal for large scale data warehouse platforms. With an approach that enables incremental delivery and a structure that supports regular evolution over time, Data Vault delivers a standard for data warehousing that elevates the whole industry.

Data Masking

Jun 15, 2020 by Marketing

Engaging Data Explains:

Data Masking

The Data Masking Challenge 

One of our clients had an interesting data masking requirement. How to mask Production data to meet with GDPR and IT security policies. The data needed to be human readable enabling the development and testing teams to create a data feed for a new Client Portal system. However, the core system did not have the ability to mask the data, only scramble or obfuscate. The core system was extremely complex, built & expanded on over 10 years. It is difficult to understand the system & how data is stored because documentation didn’t exist!

Furthermore, the architecture restraints meant there was not enough storage space to hold a second (in-line) database with masked production data.

Is This A Common Problem?

The more companies we speak to, the more complex or complicated situations we find. From our experience, we’ve found a pattern emerging in the common problems or requirements:

  • Old Tech – Ageing trading platforms/core systems or sources of data often don’t have the functionality to masked data. Those that do or have extensions/plug in to mask the data often take a long time to process or do not have the flexibility to fit every scenario.
  • Quick turnaround – Near realtime data is nice to have, but not always a real requirement.
  • Specific/varied masking – Different types of masking needed, obfuscation, scrambled, encrypted or human readable & randomised.
  • Storage – Limitations on storage or infrastructure makes it difficult to store an entire copy of production. 
  • Cost – Large database providers offer alternative tools with the same effect but also command a very large price tag.
  • Time – Developers can develop hand-cranked specific solutions which take reasonable amounts of time to develop but much longer to test to ensure the solution is working as expected.
  • Doing the right thing – Most clients want to do the right thing to meet regulatory requirements but see this as a complicated housekeeping chore and recognize the risk but choose to ignore it.

Engaging Data Discovery

We had a lot of options to solve this problem, but selected Redgate Data Masker and here is why:

  • After a review of the underlying data structure, it was too difficult, costly & time intensive to try to transfer the data into the Test environment and apply masking rules.  
  • We discovered that it would take 32 to 48 hours to copy the “majority” of the data from Production to UAT environments. Doing this would copy most but not all of the data creating a potential for leaving things behind. Plus it would take more time to run the system’s own obfuscation processes (another 8 hours).
  • Masking not Obscuring. Create human-readable values. i.e. Mr. Smith converts to Mr. Jones. This was not available from the trading platform’s masking function.
  • Defined values. Create predictable values, such as a telephone number set format or date of birth.
  • There was a lack of documentation regarding the location of personally identifiable data. This could result in the process missing part of the system if we processed the whole database.
  • We had a requirement to build in a verification process, comparing the masked data against the source. This report would answer the question – “have we missed masking any records?”

We created a simple plan to extract the data, load into a SQL database and then mask. Only taking required data increased efficient use of storage and reduced processing time. This would allow the Client’s development team to export the masked data and transfer into the Client Portal. 

Choosing The Right Tool

Identifying the data was a difficult manual process because of the core system’s table/column naming convention. Engaging Data’s Consultant used the WhereScape 3D product, which documented the structure of the system into a metadata layer. The consultant worked with the business teams to update the metadata layer & highlight fields that contained personally identifiable data. In addition, we added business definitions. Using an agile approach, each columns type of data masking requirement was agreed, along with how data joined and stored/reused in different tables. Helpfully, WhereScape 3D provided all the known diagrams and suggested relationships, helping to reduce the investigation time.

At the end of this exercise, WhereScape 3D produced detailed documents of the core systems data structure as well as analysis of the data cardinality/profiles. It uncovered some interesting points about the system, including some parts of the system that held personally identifiable data, that the client had not known existed.

Putting The Data Masking Solution Together

Using the information within the metadata; WhereScape’s Red imported the physical structure of the system and automating the extraction of data into a SQL database on a scheduled basis. We started off daily, but later to increase to every hour.

Now that the data was at rest in the SQL database, our consultant used Redgate’s Data Masker to convert the personally identifiable data to a data set, based on the agreed rules held within the metadata. Once the rules had been designed, WhereScape’s Red scheduler automated the masking so that it started as soon as the loading has completed. 

Data processing, including masking and being loaded into the target database, took place within 4 hours (initially). Not too onerous and very timely compared to other options. More importantly, meant we reduced processing time by a further hour.

Did The Data Masking Work?

Using WhereScape Red, the Engaging Data consultant was able to build a comparison process, that utilised the metadata (only using those field marked as containing personally identifiable data) and compare the values before and after the process. 

The processed ends with an automatic email of the data masking comparison report. This report contains a summary of field error analysis as well as a number of field errors per record. The latter was used to fail the process & prevent the data from be transferred to the target database. Automating this, enabled the Client to feel confident that the process was working correctly.

In Conclusion

All sorts of tools can be used to mask data. We find the best of them will automate the process allowing you to decide how to mask, when to mask & how frequent to do it.  

If you would like to learn more about this Redgate‘s Data Masker, WhereScape Red or how we can help with your data project, please feel free to contact

Contact Us

* indicates required