Keep your data, metadata, and pipeline in the palm of your hand. Spot differences between orgs in seconds and receive alerts for critical test failures to keep business (and deployments) running smoothly.
- A powerful and easy-to-use org comparison tool.
- Validate and solve problems ahead of time with the Problem Analysis engine.
- Oversight and critical notifications for Apex code coverage falling below a threshold, as well as any Apex Class test failures.
- Protect your metadata and data from accidental or malicious changes and roll back to previous states saved from daily backups.
- Build out your DevOps pipeline with in-depth and visual CI.
It’s the night before “go live”, and you’re making a change set but you’re not sure what’s in it – it’s been a long project! Thankfully, you manage to get it uploaded, but then, disaster strikes. You haven’t checked the Apex tests! Some critical dependency is missing, and the change set you’re working on won’t validate.
When deployments happen at the end of projects, it can lead to last-minute problems. Gearset is here to help you keep on top of this – running tests, assisting deployments, and even providing you with the ability to create a pipeline. Last-minute deployments will be a thing of the past.
This in-depth overview will dive into Gearset’s features, ideal use cases, setup effort, and the potential impact a tool like Gearset can offer your organization.
Features and Use
Gearset has an incredibly comprehensive list of features, which has grown a lot since the product was first launched. If it can be deployed, backed up, or automated, Gearset has you covered. We’ll explore each of the features Gearset has to offer, as well as the different ways that I have engaged with it.
Compare and Deploy
At the heart of Gearset is the goal to make deployments easy and painless, while offering clear insight into what has changed. There are so many use cases here, it’s hard to know where to begin. At first glance, this is a great change set alternative, but there is so much more to unpack…
What’s Different Between These Two Orgs?
Gearset allows you to select any two orgs (you have access to) and compare the metadata.
Why is this important? Well, they don’t need to be parented to the same production org, and can even be a Developer edition. This unlocks a whole lot of potential that was previously limited to making Packages or using dev tools like SFDX.
- Built something in your experimental dev org that you want to move into a sandbox for testing? Check.
- Starting a fresh production and needing to rescue some features from the old org? Check.
As time progresses and a UAT sandbox drifts out of sync, there may be undeployed work in there that’s being tested. At this point, it can be difficult to see exactly what’s been touched – especially with multiple deployments coming in over time. Comparing a sandbox to production org can give you a really useful scope of “what’s not the same”, as well as how far out of sync things really are – a real help for the “should we refresh this” question that always arises.
What about the big one: deployment prep? Change sets aren’t great – this is a pure and simple truth! Gearset lets you filter down into the metadata types you need, so you’re only getting a list of changes and items that are relevant to you. This is great for Experience Cloud deployment, especially as it can be hard to know what’s needed, and easy to become overwhelmed by the sheer scope of it.
Once validated, Gearset allows you to schedule a deployment at a time of your choosing. You will then be notified of the result via SMS. For those post-work-hour deployments, there will now only be the post-deployment steps to worry about.
In terms of how long it takes to spin up a comparison, it used to be the case that, with hefty orgs with a lot of configuration or packages, Gearset could take a fair amount of time to pull down the metadata for comparison. As the product has improved, however, Live Comparisons have been introduced, which displays the comparison results in real time as batches are processed – meaning you can get to selecting what you need faster, while Gearset finishes working out the finer details.
Once all is “ready to roll”, the Gearset Problem Analysis engine will step in – another life saver! This looks over the dependencies you might have missed, and references the things that might not exist (or be different) in the destination.
As well as flagging anything that should be included to make your deployment more likely to validate without a hitch, we’re left with the ability to make deployment notes and attach to a Jira ticket. This makes sure our deployment is recorded in all the right places without the need to go digging through different systems. These can be enforced from the settings page which may seem a little frustrating when trying to push a deployment through, but it’s certainly for the best. You’ll thank yourself three weeks later as you’re trying to recall your previous thought process!
It’s worth noting that Gearset isn’t just limited to comparing orgs – it can also connect to Git repositories or zip files, meaning you can push changes to more flexible places. I’ve used this for features I might want to come back to time and time again (some useful subflows I run on Activity). Any time I set up with a new org, I can deploy from my Git – and if I do improve these flows or add new ones, this can then be pushed back. This saves me from having to keep a developer org sitting around for storage.
Help – I Don’t Know What Changes I Made!
We’ve all been in the situation where we’ve become too focused on making the coolest, most powerful Flow ever, and then got a little too carried away; perhaps we didn’t write down the fields, components, actions, or subflows it needed to work. Then, when it’s time to plan the deployment, that knowledge is forgotten.
Worry not, as Gearset understands this and can provide a Depends On and Used By list for each metadata item. This allows you to quickly add things you might have forgotten, and see exactly where your precious Flow is used.
Making several deployments in a UAT as part of the end of a project phase can be time consuming, especially if it runs over a few weeks. Then there are all of the tweaks to consider, from last-minute changes based on feedback, to the features that needed extra deployment steps.
Sometimes, a big deployment can be better broken up into a few separate steps, as features or areas are finished. The Deployment History tab is for keeping track of all of these. It not only tells you what you have deployed, but also helps you consolidate these disparate packages into one big one – ready to push to live, staging, or wherever it’s got to go.
Simply check the boxes of the deployments you want to merge, pick the source and destination, and Gearset will tidy it up for you. A huge time saver!
The same can be applied to re-running deployments. Any deployment from history can be cloned and sent off to a different destination – perfect for pushing to UAT and staging at the same time, or even pushing changes down to a couple of sandboxes without having to re-create everything.
Lastly, for sharing what you’ve been up to with clients, colleagues, or managers – there’s a lovely Download Report button that generates a well-formatted PDF detailing what your deployment contained (what was created, updated, and deleted), as well as any tests that were run.
Part of the stress of deployments is clicking the button – whether it’s going live, UAT, or just merging changes, “peace of mind” can be hard to come by. Knowing that Gearset allows you to undo mistakes, mishaps, or even something that wasn’t tested as well as it should have been, provides that “peace of mind”.
Combined with scheduling deployments and taking org backups nightly, this sense of peace provides both consultants and internal admins with a reason to keep the tonic-of-choice in the bottle on deployment day!
The way it works is super simple – Gearset automatically saves a snapshot of your target’s metadata before each deployment, and it compares the snapshot to the target’s current state. The same easy-to-read UI is present, as well as the differences between what was deployed and what was there before, allowing you to select exactly what needs to be rolled back. This is also great as if just one element is out of line, you don’t need to roll everything back.
It’s a feature I only used once or twice in my time with Gearset, but every time I planned a deployment, I felt at ease knowing that, should the worst happen, I had the power to fix it at the click of a button (or two).
Automated Unit Testing
Apex tests can be a bit of a sticky subject – they can be the biggest, hardest-to-fix, nasty surprise in a deployment (or late on in a project) when one little validation rule causes everything to go wrong. Likewise, a change of a validation in production (when nobody is looking) can break a long established process with no warning. As is the theme here, Gearset steps in to stop this from happening.
Running Unit Tests before a deployment is a must, but what about running them every day? Gearset can be set up to keep an eye on tests and report on coverage, as well as pinging a notification when things aren’t what they should be. These can be plugged into Slack, Teams, and Chatter, or even texted to a phone number (though personally, I only did that for the most critical jobs).
Having this set up in production, a staging environment, and UAT allowed me to keep an eye on code coverage % and test failures as they changed, then take steps well ahead of any deployment to make sure nothing would break. As a result, there was no last-minute stress.
This also helps keep both developers and admins accountable for the changes they are making, as with Code Coverage and tests failing to errant flows and validation rules, it can quickly become a “finger-pointing” exercise if this comes up at the eleventh hour. Tolerances can be set on an org-by-org basis, which can be helpful for ensuring you only receive alerts when something serious is happening.
Following nicely on from Tests and unexpected changes, it can be challenging to work out what’s not quite the same as it was yesterday (or was it last week?), but working out who made the change can be even harder!
There is the setup audit log, but this can be a maze to navigate and won’t give you the detail you may require. It’s also populated with every single log of a change, which makes it hard to see the “forest for the trees”.
When did a Flow screen element change? What was added? These are details you might need to know in a hurry. You may also need to quickly set things back to the way they were a few days ago. Again, Gearset has your back – search through your monitoring history to find when the item in question changed, then run a comparison, validate, and deploy. Then all will be as it was before.
Data backup comes in two flavors depending on what’s required: Standard (daily) and High Frequency (hourly). The setup isn’t any more difficult than navigating the Data Backup page and creating a job, assuming you have the org “hooked up” already.
Much like Apex tests, alerts can be set up to run, both if a backup fails, or under special conditions – for example, if more then a certain number of records is deleted, created, or edited, these can then send an email to whoever needs to know that something might be out of the ordinary.
Likewise, the flexibility extends to the objects that are being backed up – allowing things of less importance or clutter to be left behind, and only backing up what’s important.
As ever with Gearset, the UI is standout – easy to use and presenting changes in a digestible way. The Job History tab for any data backup run makes it easy to spot where any sudden changes may have popped up. You can then drill down into these, right to the record, with the big and friendly Quick Restore button, which reassures you that, if these changes were unwanted, accidental, or otherwise, they can be set to their previous state at the click of a button.
A big concern in working with sandboxes is the need for real or familiar data. This can be a GDPR nightmare waiting to happen, or it is simply a risk in terms of testing processes that send emails.
Gearset has you covered here with Data Masking – when deploying data, you can choose to mask certain fields (e.g. Last Name or Email). Gearset will endeavor to populate the field with realistic looking data so users testing won’t see blank fields or a garbled mess!
Pipelines and CI
For those of us who started out as “accidental admins”, or found other routes into the Salesforce space, the concept of Gits and deployment pipelines can be quite daunting. Gearset aims to tackle this by making it much easier for everyone to get comfortable with working between multiple repositories and orgs – all without it becoming too much of a hassle.
It’s easy to set up a pipeline as long as you have a Repro setup and have added it to the Source Control tab (in the app), then you’re ready to go. From here, you can add existing CI jobs you might have running – great for those who’ve been on Gearset a while and want to join the dots or create new ones. These can be set up to commit changes or to sync the full org over.
From here, you can then use the same metadata filters Gearset uses to power its comparison – this means you can make sure you’re only moving metadata types that actually matter to you, to keep things nice and clean.
Once you have all your environments set up, you can connect them using the intuitive drag-and-drop builder to see the flow of changes. You can also use this page to view any merge requests that might have come through, or you can address conflicts. This makes it the perfect page for someone looking to see the full picture in an involved CI setup.
As with all areas of Gearset, notifications can be set for failures (and successful runs too) to go via text message, Slack, and Teams. The granularity of the settings for each job is too extensive to list. However, this should be the go-to solution for visualizing your deployment pipeline if you have one already set up. And, for those uninitiated, it’s a great introduction to getting one set up.
New User Experience and Setup
To get up and running with Gearset, very little setup is needed. Starting out sends you to a “quick compare wizard” to help you connect a pair of orgs, and begin checking out the differences – a quick and easy introduction to the most core feature of the product.
From there though, to get the most out of Gearset, connecting the orgs you work with is the main time sink, as well as setting up the filters for tests and metadata changes. If you’re a solo consultant with a wide client base, this might take a fair amount of time, so it’s worth settling in with a cup of coffee and just getting through it. The same applies if you have several Gits and a pipeline to build.
That said, the setup is smooth and easy to understand, and once you’re in the zone, connecting a handful of orgs isn’t so bad – I do wish though, that like Environment Hub, you could authorize all sandboxes at the same time as production.
The Gearset support team have been fantastic for me, over a great many years – offering a live chat (which sits in the bottom-right corner of the screen) at nearly all times, the Gearset team are clued up not only on the features of their product, but also the common deployment issues too.
On more than one occasion, I have quizzed them about what needs to be deployed for a pretty tricky Experience Cloud, or a deployment that won’t quite validate (especially after sandbox preview!), and I have received speedy and knowledgeable help each time.
Once you’ve started using Gearset, it will be hard to go back – the “peace of mind” it has given me, my co-workers, and my clients over the years cannot be overstated.
It’s easy to get off the ground with this app and you’ll start seeing value right away. While there is a learning curve in terms of getting to know the various metadata items and finding your way around the different menus, it’s incredibly easy to start using some of the more advanced features Gearset has to offer.
The UI is clear and concise; the comparison results provide an easy-to-digest log of changes, and at the same time, you’ll see all of the in-depth, org-to-org detail you could possibly need. Frankly, you’ll never be able to look at change sets in the same way again!
Likewise, keeping an eye on code and metadata changes provides an overview for those with many users and many sandboxes – providing a good metric on what’s changing or what’s broken, and allowing actions to follow.
Gearset has a multi-tiered pricing structure which can be found here, covering everything from Compare and Deploy to comprehensive data backup and masking.
You can also opt for a free 30-day trial to take Gearset for a spin and ensure it’s the right platform for your organization.
Whether you’re an accidental admin or a senior consultant, Gearset brings something to the table. In offering features across all key areas of the platform (deployment, metadata, unit testing, and data), there really are benefits for everyone.
Gearset offers a free trial, so there’s no excuse not to give it a spin and see how it can bring “peace of mind” to your deployment strategy.