Engagement Deep-Dive: Continuous Everything in a Regulated Bank

Oct 1, 2020| Daniel Jones

Over the last year EngineerBetter worked with Smarsh, introducing new ways of working that enabled continuous deployment of 19 data services and 20 apps across multiple vSphere environments in a regulated investment bank in 10 weeks - and that was just the start!

“We needed people to not just speak about the ‘right’ thing or merely do it for others, we needed a team to partner with our engineers, model and teach the ‘right’ thing so that it became pervasive organizationally. EngineerBetter does just that and with an empathetic approach that had my teams eager to work with them more often.”

Kyle Campos - EVP Technology, Smarsh

In this blog post we’ll talk about the methodology we used, the technical implementation and challenges, and what we learned from the experience.


We achieved the following outcomes through considered use of approach and methodology:

A > 90% reduction in time-to-production. Smarsh went from the effort of many teams over a period of weeks, to an automated pipeline that would run in around 4 hours. At the most absolutely conservative estimate this was a ~90% reduction in time-to-production, and an even greater reduction in human toil.

Increased technical and methodological knowledge. By pair-programming we were enable to upskill the customer’s engineers at the same time as delivering a system. Just as we learnt a lot on the engagement, Smarsh engineers got insight into Concourse, Cloud Foundry, BOSH, RSpec, and all sorts of other technologies. Even more importantly, by engaging using our methodology, we were able to educate folks in vital concepts such as TDD, lean decision-making, eXtreme Programming, YAGNI, package cohesion principles, continuous delivery, and more.

Self-sufficient teams. After nine months, Smarsh were ready for us to downscale the assistance we gave them. After a year Smarsh’s engineers are developing and operating the system without our help, so we are no longer needed - you could say “our job here is done”.

One team that could deploy everything. For the first time in the customer’s history a single team owned the deployment process from beginning to end, reducing transaction costs, delays and communication overheads.

Tested, promoted products. The entire suite of software and infrastructure was tested and then promoted using our Stopover approach to Concourse pipelines. We could be confident that changes would work before they reached higher environments.

Reproducible environments. Because everything was automated there were no unique ‘snowflake’ environments, making them easier to reason about. We regularly repaved test environments to ensure this reproducibility.

Context-carrying between timezones. Being located in Europe allowed EngineerBetter to be the conduit for context to pass from the India timezone to US timezones. Because we were a single team engaging in remote pair-programming, that context was transferred in a human-friendly manner, rather than passive-aggressive JIRA tickets.

Making the implicit explicit. Previously knowledge of the system had existed in a fragmented fashion, split between documentation and the human experience of various teams throughout the organisation. Instead, now we had one testable infrastructure-as-code pipeline that embodied all of that knowledge.

“EngineerBetter certainly bring a high level of technical competency as their name would suggest, but where they help most is actually their ability to listen, adapt and accelerate cultural transformation.”

Kyle Campos - EVP Technology, Smarsh


Smarsh produce an enterprise communications archiving product imaginatively named Enterprise Archive. It is used by thousands of big-name customers to help them meet regulatory requirements by storing sensitive communications in a secure and searchable way. Whilst Smarsh might not be a household brand, many of their customers are.

Enterprise Archive UI
Smarsh's Enterprise Archive. It remains to be seen if this is where the Captain's logs are kept.

Smarsh have presences in Bengaluru, India; London, England; and around the United States. Whilst the majority of app developers are based in India, the folks deploying and running things are dotted around the globe.

Smarsh’s Enterprise Archive product is available primarily as-a-Service, hosted by Smarsh. Given the nature of the product though, some regulated customers require it to be installed in their own datacentres and often in an airgapped environment - where there is no communication possible to the outside Internet.

Smarsh’s products deal with big data: messages are ingested at high velocities, and stored in large volumes. There’s a lot of processing and reporting that needs to be done, so it’s not a surprise that there are wide variety of different technologies in use: Hazelcast, Storm, Kafka, MongoDB, Elasticsearch, and PostgreSQL, to name a few.

Enterprise Archive is the implementation of regulatory requirements, meaning it’s performance and uptime are serious business. If this stuff isn’t bulletproof, then customers could end up getting fined.

Legacy Installations

Going back a long way, Smarsh’s products were deployed manually by humans. Over time it became clear that automation would help, and like a lot of folks they adopted tools like Puppet and Ansible to help automate parts of the process. This partial automation was an improvement, even though it still required manual intervention and could take many weeks to set up the vast array of components required.

As the limits of tools like Puppet become clear, Smarsh adopted more modern cloud-native tools like Pivotal Cloud Foundry (now VMware Tanzu Application Service) and Concourse CI. These were used in different places for different purposes.

When EngineerBetter first engaged with Smarsh there were different deployment technologies used for part of the installation process, but not all of it. Deploying the product involved numerous teams, with lots of coordination between them.

Building Trust

EngineerBetter and Smarsh originally started working together to create Golang Kubernetes operators for the myriad data services that Smarsh need to run.

The original need changed. In the process of evaluating the effort required to write custom operators (upstream open source versions either didn’t exist or didn’t have all the required features) the folks at Smarsh looked at BOSH for the first time. They recognised that BOSH’s lifecycle management already provided some of the more complicated issues that an operator would need to address ‘for free’.

So instead of writing Kubernetes operators EngineerBetter went on to assist more generally, and to deliver instructor-led training on Concourse, Cloud Foundry and BOSH, on-site in Bengaluru. On a personal note, we certainly relished the chance to visit India, loved the food, and brought back awesome gifts for our families.

Full Automation

Over the months that we worked together, Smarsh’s ambitions for a fully-automated deployment system grew - something that we positively encouraged. We discussed how we could assist a team that would, for the first time, deploy everything in the Enterprise Archive product automatically.

Our proposal was this:

EngineerBetter would provide three Engineers and a Backlog Manager to form a team joining Smarsh folks from a variety of disciplines. We would operate from a single, ordered backlog of stories prioritised by the Backlog Manager. We would remote pair-program to deliver and upskill at the same time. We would operate as a lean, eXtreme Programming team, doing the most important thing in the simplest way.

The team would integrate all the disparate components of the Smarsh product to be deployed via a single, monolithic Concourse pipeline dubbed the ‘megapipeline’. This would invert Conway’s law, provide a single-pane-of-glass view of deployments, and enable easier promotion of changes between environments. We would automate everything that was previously manual, find all the code and processes that needed changing in order to achieve this, and work around those whilst feeding back information to developers inside Smarsh.

Concourse UI showing a megapipeline
A deliberately-obscured view of the deployment megapipeline

How We Worked

Our cross-functional team worked from a single, ordered backlog in Pivotal Tracker. Stories in this backlog are created and prioritised by the Backlog Manager, who ensures that each has clearly-defined acceptance criteria. Work isn’t considered complete until the Backlog Manager has run through manual acceptance steps, a process that ensures firstly that things really do work, and secondly that the intent of the story was correctly communicated in the first place.

A product team
The Product/Backlog Manager writes stories that present user value, and engineers always take tasks from the top of the backlog.

Each week the Backlog Manager would conduct a pre-IPM (more on IPMs later) wherein the technical lead (or ‘anchor’) on the team would give advice on whether the upcoming and unpointed stories in the backlog make sense to engineers, are in a logical order, and contain enough information to be estimated upon.

Typically at the beginning of the week an Iterative Planning Meeting (IPM) is held, facilitated by the Backlog Manager and attended by all engineers. The Backlog Manager goes through upcoming stories in the backlog, explains the desired outcome, and then asks the engineers to estimate the complexity of the stories in the abstract unit of ‘story points’.

The IPM ensures that:

Like most agile teams, the morning started with a standup meeting. Standups are one of the most oft-abused facets of agile practice, and we’re very keen that ours do not fall into this trap. A good standup should last no more than two minutes per pair.

A standup consists of:

A standup does not feature tales of woe, becrying how awful yesterday was, or a blow-by-blow account of what the engineers did. We don’t need this because we pair-program and rotate between pairs, and write status updates on stories at the end of the day.

We made use of Pair.ist in order to better visualise who is working together, and what the tracks of work are. Pair.ist can also automatically recommend pairings, to ensure that folks rotate.

Example Pair.ist UI
An example of Pair.ist in action, showing who is working on track of work, but not individual stories.

As the Smarsh engineers we were pairing with in the morning were based in Bengaluru, we held all meetings using Zoom. In the morning we’d have a quick standup, and then after (our) lunchtime the Bengaluru folks would sign off, and we’d have another quick standup with the American Smarsh engineers.

When pairing, we’d use a combination of Zoom for screen-sharing and Visual Code Studio Live Share. The former allows full-screen screen-share and visual annotations, whilst VS Code Live Share allows multi-user editing of code and sharing of a terminal. In-person pairing would have been preferable as each of these tools has its quirks, but that wasn’t really possible for an inter-continental team!

At the end of the week we’d hold an agile retrospective, again online using Zoom and Postfacto. Retros are vital for allowing the team to self-tune and improve, and also for folks to air grievances, show appreciation, and build empathy and trust.

Example Postfacto UI
An example of a Postfacto retro board, in this instance that of the London Continuous Delivery meetup.

Technology Challenges

There were a number of technical challenges:

  1. Deploying into an air-gapped and regulated environment
  2. Bootstrapping the environment
  3. Automating the deployment of a huge number of systems

Challenge 1 - The Airgap

Systems inside the end-client’s environment couldn’t communicate with the outside Internet. Files could be transferred into the target environment via a SFTP syncing mechanism:

  1. We upload files to Smarsh’s SFTP server
  2. Someone or something inside the bank writes a file to a special NFS mount
  3. An internal system in the bank pulls files from the SFTP server
  4. Files are now available on the internal NFS server

Of course being enterprise software there were more unexpected idiosyncrasies than this, but hopefully you get the idea. It gave us a basic mechanism by which to get files into the target environment, but it wasn’t very continuous.

Challenge 2 - Bootstrapping the Environment

Our starting point with the target environment was a collection of ESXI hosts, connected to switches that embodied a predefined network topology. That, and a virtual Windows desktop.

The Windows desktop had very few useful tools installed on it, and we didn’t have administrator access.

Challenge 3 - All The Stuff To Deploy

There was a huge amount of stuff that needed deploying:

All of these needed hooking together, and had historically been configured manually. This meant that there were lots of interdependencies to unpick before things could be fully automated.

Whilst there weren’t a lot of extant tests at our disposal, there were some full-system end-to-end tests that we could run to check that the entire system was working. They would take about an hour to run and test user-facing functionality, so whilst they give a great degree of confidence, they wouldn’t give fast feedback and were not granular enough to allow fast debugging.

The Implementation

Given the number of constraints in the engagement, the solution was rather complex. I’ll do my best to break it down into intelligible chunks.

Getting the First Files into the Airgap

First of all, we needed to be able to get files into the bank.

Our Concourse pipelines (detailed later) would upload tested assets to SFTP. To initially move the files, we’d need a human to trigger the manual sync process.

Whilst Concourse automates the uploading of tested assets to SFTP, initially a human must trigger the sync process inside the bank.

Bootstrapping an Automatable Environment

Now we could get some files into the bank in a manual way, we needed to create API-driven systems inside the bank that could be automated.

We would be using BOSH to deploy and manage virtual machines in the target environment, which has plugins that allow it to talk to VMware vSphere, but not to ESXI hosts directly.

Why not Kubernetes?

It is of note that we were given hypervisors to work with, and at the time vSphere did not offer its Tanzu Kubernetes integration. Introducing Kubernetes would have meant an additional layer of abstraction for little additional benefit.

That meant that we needed to install vSphere, but writing scripts to do this automatically from a Windows machine with no access to MinGW or Git Bash was going to be painful. So we needed to create ourselves a Linux jumpbox with a useful set of tools on it.

We get a VM image into the bank manually, boot it, and then run a baked-in script to install vSphere, BOSH, Concourse and more. After this, a Concourse pipeline is set to automatically sync new files.

Once we had a useful Linux jumpbox, we authored scripts to run on that jumpbox to deploy and configure VMware vSphere, giving us a usable IAAS. Further scripts then installed BOSH, Concourse, and Credhub using Stark & Wayne’s BUCC tool. But wait! It wasn’t that simple, because we can’t download the things when we need them inside the bank.

Everything that we needed to run from the jumpbox had to be baked into the VM image. As we continually found more things that needed adding to the jumpbox, we automated the creation of the VM image’s .ova file in a Concourse pipeline running in Smarsh.

Getting Files into the Bank Automatically

Once we had a Concourse running inside the bank, we could then use it to run automation to pull in new files and put them in a place that the main deployment pipeline would be able to more-easily consume them.

The ‘sync’ pipeline would trigger the SFTP-to-NFS sync, find new files, and upload them into the internal environment’s MinIO buckets.

Deploying Everything in a Megapipeline

Everything up until this point has been bootstrapping. We now had an API-driven IAAS in the form of vSphere, BOSH as a means of declaratively deploying things on that infrastructure, and Concourse to perform the automation.

We created a deployment ‘megapipeline’ that, given an IaaS, deployed everything. Absolutely every component that makes up Enterprise Archive was deployed by a Concourse pipeline some 7,000 lines long.

Whilst this approach is unwieldy, it has a number of benefits that make it worthwhile:

Of course, there are downsides. The pipeline was massive, and browsers struggled to render it on slower machines. Sometimes changes were being made that only took effect at the end of the pipeline, and so sometimes there’d be a lot of waiting to do.

We used a combination of the pipeline itself and the end-to-end tests to iterate on decoupling some of the bi-directional dependencies, and automating previously-manual steps.


Continuous deployment without testing is reckless, and not acceptable in any context, let alone a regulated bank!

Everything that we created needed to be tested before being attempted in the bank. This meant that we needed automation to test the automation.

Testing the Bootstrap

The deployment pipeline deployed Enterprise Archive itself, but the preliminary bootstrap step was required - we needed to have VMware vSphere installed, and to have the likes of Concourse, Credhub, BOSH, Docker Registry and MinIO installed.

This was the job of the IaaS-paving pipeline, which also generated system-wide secrets, like TLS CA certificates.

The IaaS-paving pipeline performed the exact same steps as humans would do when they first got their hands on the customer environment. The pipeline automated the creation of the jumpbox OVA image, deployed it to ESXI, then sshd into the jumpbox to run the setup scripts that we had in source control. Once the test environment was successfully bootstrapped, we knew that we could promote the tarballs containing the OVA and the scripts across the airgap via SFTP.

An advantage of this approach is that the IaaS-specific components can be swapped out. Different IaaS-paving pipelines could be created for vSphere, AWS, Azure, Google Cloud, and if the interface was well-defined, the same deployment pipeline could be used ‘on top’. In fact, later work we did with Smarsh worked on exactly this.

This approach also had the added advantage that we could automatically create and destroy test environments with ease.

Testing the Deployment Pipeline

Once we had bootstrapped a test environment, it was straightforward to then run the deployment pipeline on the new test environment.

In order for the tests to be meaningful we would have to subject our test environments to the same restrictions as inside the bank, and to emulate the airgap.

Concourse Resources

Concourse uses custom plugins called resources that represent versionable things in the outside world, such as Git repos, Docker images and files in an S3 bucket. These plugins tend to assume that they can establish connections directly to things like GitHub, which won’t work inside an airgap.

We created ‘fetch’ pipelines outside of the airgap that pulled the latest versions of resources (ie Git repos, Docker images, BOSH releases, PivNet tiles, jars from Artifactory) and wrapped them up as tarballs.

Concourse requires all things it gets from the outside world to be versioned for reproducibility. This meant that we had to make use of the Concourse semver resource to maintain monotonically-increasing version numbers for everything we fetched, which could in turn be written into the names of the tarballs.

The tarballed resources are uploaded to each environment’s MinIO blobstore by the ‘sync’ pipeline (see below).

The fetch pipelines ran outside of a specific test environment, and their output (a bunch of versioned tarballs in an S3 bucket) could be shared by many environments.

The ‘fetch’ pipeline tarballs assets that are needed, and puts them in an S3 bucket. ‘Sync’ pipelines in external non-airgapped environments copy files from S3 to the environment’s MinIO, and ‘sync’ pipelines inside the airgap instead copy from NFS.

Sync Pipelines

Whilst test environments outside the airgap could have reached out directly to the original data sources, that would have meant that the deployment pipeline we were building and testing would behave different inside and outside of the airgap. That would invalidate our tests.

We wrote variations of the sync pipelines that, rather than syncing files from NFS, would pull from S3 the tarballs placed there by the ‘fetch’ pipeline, and then put them into a test environment’s MinIO.

Things We Learned

Whilst we were briefed with upskilling the fine folks we worked with at Smarsh, we learned a whole heap of things too. The technical learnings are too numerous to mention (and some we’d rather forget, like how incredibly-not-cloud-native Townsend is), but here are some of the bigger lessons we learned:

How Can We Help You?

Do you need to improve your time-to-production, increase quality, decrease time-to-recovery all whilst upskilling your staff?

Do you have large, complicated infrastructure endeavours that you would benefit from treating as software projects?

Do you have capable staff who would benefit from an extra set of hands to enact a cloud transformation? Do you want to combine training and delivery?

As shown above, EngineerBetter have experience in achieving great outcomes in highly-constrained and less-than-ideal circumstances. If you need similar outcomes, maybe you should get in touch.

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