Description
In this session, you’ll be shown a 30 minute demo of Gearset’s “Sandbox Seeding and Compliance” functionality. Here’s what to expect:
- Realistic data for reliable testing and debugging: Learn how to create your ideal data seeding process between orgs through templates, smart data relationship handling, and choice over how to match and deploy your data.
- Getting control of your data compliance: Understand how to mask your data, and deploy knowing you have full confidentiality and compliance with the CCPA or GDPR, as well as maintaining a full audit trail through your data deployment history.
Gearset Accelerator videos:
Transcript
without further ado, let's jump right in. So, hello, and welcome to another installment of our accelerator series.
Now for those of you who perhaps are new to our accelerator series, basically, as a team, we've been holding about twenty minute demos of various areas and functionalities of Gear Set, so you can feel more confident and content within the platform.
So today, our lovely Hugh will be showing you a short twenty minute demo of our sandbox seeding and compliance functionality.
Hugh will be covering how you can create your ideal, data seeding process between your orgs or realistic data for reliable testing and debugging, Also, how you can mask your data to get full control over your data compliance as well as maintaining a full audit trail throughout your data deployment history.
As for questions, we have no formal process. We have got quite a few of you here today as well, which is lovely to see.
So feel free to pop them in the chat. Myself, with Charlie, Antonio, and David, we'll try and answer them as we go through, but we do have kind of a dedicated time at the end, for any outstanding questions.
So I will now pass over to Hugh.
Thank you, Beth. Hey, everyone. Thanks for coming along. I suppose the first thing for me to do is, share my screen, and we could dive straight in. I wanna confirm. Can everyone see my screen?
We can.
Lovely.
So supposed to start with, I'll talk a bit about what is data deployment in this case. And the gear set sandbox seeding tool allows you to move data from one Salesforce org to another.
This can be up towards higher environments or downstream from production.
But today's example, let's say our team is looking to push some data, maybe some more realistic data from our production environment downstream for some testing in the lower orgs instead of me or my team going in and manually creating this data with all those relationships.
We want Geasset to try and do as much of that for us as we can.
So I'll select my source here and my target, and you'll see we have a couple of options down at the bottom here.
We can actually deploy this straight away from a template.
So we'll have the option to save any configuration we build out as a template that could be reused, needed, speed up these data deployments even even more.
But today, we're just gonna configure that from scratch and build out this data deployment together.
Now just to note, prior to doing this, I'm assuming my metadata is the same and aligned in between my source and target. If you ever need to check this or align this, you can go and use compare and deploy features or gear set.
We've had some previous webinars on the compare and deploy functionality, so we can share those as needed as well.
So let's go and configure this deployment.
What you're gonna see on this page is we'll see on the left here a list of the objects available for deployment. So we can pick the objects we want to include in our data deployment.
And this stage is really helped out by having a good understanding of your account, kind of your Salesforce data model and relationships within this model.
This is gonna help us filter, deploy what we want. Now one tip we recommend is using the schema builder within your orgs to kind of get a more detailed view of what you're actually including in this data deployment.
You can see here the various different relationships, the lookup IDs, and relationships between these different datasets and get a bit of a bit of behind the scene look at maybe what Gearset's looking to do to pull through for this.
So we can build this configuration from the children objects, such as, let's say, with the opportunity line item.
Using the lookup IDs on this children on this child object, we can bring through any parents referenced objects that we can see in the list down at the bottom here. We can filter on this child as well to specify maybe a bit more detail what we want Giasa to actually retrieve for us in this data deployment.
Now that's not the only way we can configure this deployment. We can also go via the parent.
So if we go through to accounts maybe let's go back to opportunity, actually, include a few more things here.
You'll see on these on the objects on the right that we have a tick box letting us know that we can actually tell gears that only deploy opportunity records that are children of the account records that are being deployed. So, essentially, we're gonna be able to maintain that data integrity, the relationships, references between these.
Now filtering the record is really useful to maybe really refine that dataset. So maybe it's a specific test data that I want to include. So for example, an account here, I can add a field here. Let's use account number as an example, and let's just say, Simplicity, it's not empty. You'll see this has cut down my data appointment down to eleven records that match this field. Now maybe I only want a certain number. I don't need that many for testing.
Up here, you'll see we can actually alter the number of records we want to deploy.
With this, we have a hard limit of a hundred thousand. But let's say, for simplicity sake, I only wanna take through five. Now what gear set's gonna do is randomly choose five records based on the filters we've set.
So, really, we can use this filter to control what is included in the batch and narrow down this for our dataset.
Now oh, just kicking up an error there because I've left one of my filters.
That's a bit of a mess. There we go.
By clicking through to next, we can now move on to the next steps in the configuration.
Again, on the left hand side here, you'll see the objects we chose in the previous screen. And on the right, you'll see the related objects that can be included as well.
You see a few various different deployment methods, that Giaset has, and we'll talk through those now.
But you have the options to create new records.
New records will be created on the targets, potentially useful for those who haven't done a deployment before to this org. Maybe your target is quite fresh. You just wanna get that subset of data, that test data in our use case here into that environment.
We can also upstart these records. So the source and target might have that data already in them.
And GearSat will match that data using a selected field, which we'll talk about in a second.
And it will update them if there is a match.
And if there's no match, we'll create this as new.
And in a similar fashion, you can also just update existing records. So, again, the source and target records are matched using that field.
And if there is a match, this will be updated. If not, here, so it's gonna go go ahead and not create anything else. It will just update those that exist.
Now in some cases, Gearset can use existing fields here to match these records, just name, email, a few other options that you'll see on the right here.
But in some cases, we have to configure and use our own custom external ID. You'll see on my account object at the top here, I had to go create my own external ID, in both my targets.
And this allows me to then use the up cert and update functions here to match these between my orgs.
Now if no field exists for us to match, yes. It's gonna default to inserting these as new.
You can see this in my opportunity line item example here. Highlighted here, there's no lookup ID or external ID found, so all records we inserted as new there.
Now just before we hit next, you'll see we have some options around when errors occur.
Now if I click into this drop down menu, you'll see we have a couple options here. We can continue to deploy the remaining records, or we can stop deploying the remaining records. But you have a couple of options here.
You can have the option to stop, fix that error, and then pick this data point back up or continue and not have to start and stop.
This second option could be a good method if you're going into an empty sandbox with this test data. So I'm moving this test data into an empty sandbox. I might have to stop and start. I could just fix those errors at the end as needed.
Now we could click next if we're happy with the setup. However, there's another bit of configuration we can optionally do if needed.
You'll see here we have the option to exclude fields in the deployment.
We click into this. You can see that this is gonna allow you to block out troublesome fields or fields you may know to cause an issue that aren't actually needed in deployment.
But you can get quite granular with this within account and all those objects that we've included and the related objects being pulled through by Gearset.
But for this demo, I'm happy with everything. I know nothing's gonna cause an issue. I can go ahead and click next.
Now Beth alluded to it to at the start, the one big bonus of Giaset is the ability to actually mask this data, meet your compliance needs.
When working and moving production information around, you want that realism, but you don't want you want don't want any actual private or sensitive information being visible.
You may need to do this in their environments. You have consultants or third parties working downstream that still need that test data in place, but you want that to be masked.
This is the section we can do that. Along the left here, we have the option to mask these fields by type, meaning any object containing these fields will be masked.
However, on the right, again, with gear set, you can be a bit more granular.
Turn off turn masking on and off for different individual fields.
Now at the top here, you'll see we have a few more options.
My my team, we're based in the UK, so maybe we want this localization bit more relevant to where we're working.
But if that's not enough, you can go into custom fields here and also update these values and set different values for, actually, what you want to be using in that masking.
Now on to the next page.
This is the step where we can see what rules, triggers, and flows are present that could potentially impact the deployment and be affected by the data we're actually moving into this org.
And this could cause issue. It could be a concern for us when we're actually wanting to move this test data from production down to our lower environments, be that a sandbox or a development environment.
And, essentially, what this does here you can see I have one, an opportunity.
Essentially, what this does is it carries out something the gear set's very used to, and I'm sure a lot of you are very used to using with gear set, which is a metadata deployment. To disable these, that is tracked to the comparison history. You can have that audit log of what's happening.
But, also, the benefit of that is you're gonna have the option to roll this back, roll this change back at the end of this process, to essentially turn those rules, those triggers, back on, enable them once that data deployment is complete.
They're going through to the next step. This is the predeployment summary, and this next page is going to lay out all the steps Giasat is taking to deploy that data. If we scroll down this long list, maybe go down to actually where we're updating and upsurting this, you're gonna see if I click into campaign here, that will open up the actual steps involving campaign on the right. And when we press deploy data here, yes, it's essentially gonna tick through this, as a list a bit in lifetime, acts a bit like a progress bar in a way, and that's gonna let us know what steps are actually taking place within our data deployment. We have a peek, peek behind the curtain in a way.
Now on this page, you'll see a few more options at the bottom. This is another place we can save that template and that friendly name so I know the configuration I just put together. That's what I want to be using regularly to maybe update more test data in different environments.
I could save this, share it across my team, and make this a bit more of a repeatable process, saving just that bit more time in our testing and development process.
Now if any errors do occur throughout this process, they'll be flagged on this list and show us what step they happened at.
For example, I've got one from earlier.
Here, you'll see that we actually give the specific problem.
We give the Salesforce error IDs if we have them.
And where we can, if we know what the solution might be or what the issue is, we're gonna try and give you as much information around that, give you a solution, there and then, and also link link you through to the documentation site where we might have a bit more of a detailed explanation to troubleshoot this as needed.
And another point we've raised today is around the audit history and having a good track of what's actually happening with your data deployments.
Now similar to the metadata comparison, if we go in here in old time, you're gonna be able to see the full history of all the data deployments that have happened within my gear set team. As needed, we can jump through all of these when we want to, see duration, few actions we can dive in, get the details there. If it's a successful one that we want to repeat, we can go in there, maybe template that configuration out, and use it again.
Now, overall, this is essentially a to be a bit of a high level overview of what it looks like to take data from one source to to a target with gear set, masking that, being able to configure that and filter that to what you want to include, and also be able to handle awesome fields by excluding them or handle those figures, flows, and validation rules as needed.
That is kind of everything from data deployment. So I think what we can do now is potentially look at some of the questions, take a look at the chat now if we have any questions.
Absolutely. Well, thank you so much for that, Hugh.
So we've got a few questions, but, folks, feel free to kind of pop them into chat as well. We have still got about fifteen minutes left dedicated time, so there's absolutely no pressure.
So one of the questions that we have got is, can you mask records that are already in the org?
Yes. That's a good question. One, we get quite a lot. Sometimes this not as well known functionality of Gear Set, but, essentially, what you can do in that case is actually choose choose the source as the org and choose the target as that same org. So matching that matching those records is gonna be easy because they're the same. Any fields that we use to match those will be the same, and you can mask those and essentially carry out that data deployment as needed.
Awesome. Thank you very much.
Also got another one. Can you deploy record types? Is that something you can do with, data seeding?
So my colleagues might be able to help, but I believe record types are metadata. So with the data deployment, that's a no. However, handily, you can do that with compare and deploy. It will handle the metadata for you.
Perfect.
Another question. I thought it'd be quite popular.
So data deployments. Are you or are we able to do this to production? And if so, how do we go about doing that?
Good question. Yeah. It comes around a lot of the controlled and maybe some security because data could be, sensitive and hard to handle and, quite sensitive. So you'll see at the bottom here on my screen that the current data deployments are allowed through to production. So we're gonna give you as much warning as we can. But if I head into the account settings here, you'll see in the data management section here, bit of a toggle switch around data deployment, to production logs for my team. I can turn this on and off as needed.
This is probably a good point to, to point out that there's no deployment rollback, should a data point fail. So compared to a metadata, comparing to point where you can rollback. With data, all you can do is essentially stop when errors, occur, but you can't roll these back.
So I suppose it's a decision you have to make based on your use case. If you want to be moving or have that option for your team, we're gonna give you as much warning as we can and have a big old, are you sure you want to do this before every deployment as well?
If it's have that kind of prewarning oversight before you make kind of any changes to production, that completely makes sense.
So we have got a few more questions that have just popped up as well. I believe Antonio has been replying back into the chat.
So, again, opening up the floor to Charlie, David, and Antonio, Hugh as well. So, I think the one that Antonio is replying to at the moment, I'll leave him to to reply in the chat.
So, again, if people, or no. So we got a question from Morgan, saying, with masking, is it possible to define certain manipulations like standardization of email addresses, or is the masking closer to random pregenerated data?
Good question, Morgan. I'm just reading through that again, just so I can make sure I understand the question fully.
I suppose I suppose with that one, what you can do is have the ability to go into the custom fields and make that data look maybe have a bit more control over what you want that to look like, Morgan. I suppose, it'd be a question of what maybe what you want that data to look like.
Like, we ten accounts where I'd like to.
So just for everyone else who's aware, Morag said, example would be ten accounts where I'd like to set all their emails to a different domain in a depth sandbox.
What we can do if kind of any questions or outside questions that we have, what we will do is reply with an email as well to you folks just so we can answer kind of fully, and make sure that we're giving the right answers to you as well. So, either way, we'll we'll kinda give you an answer in the next day or so just as kind of reassurance, there. But, let me see if we've got anything else.
We've got quite a few.
So let me have a look. We have a question from Ihor too.
When data is fetched from production, is it stored somewhere on gear sets? And following on from that, would have access to that data?
Anyone able to answer that question at all? If not, we'll send over kind of our more security, articles, if not.
That is right.
So I believe we do have some security articles around this. And, David, I while you popping up there, you might know the answer, but I'm pretty sure we don't join for data deployments. We don't store this data on our side. It's only during transit.
Correct.
I was about to say the only way you would have access to that data is if you're backing it up. Yep.
Brilliant.
Antonio's just answering the chat as well from Bruno's question of can we schedule data deployments to happen daily?
Unfortunately, not. This isn't something that Gearset currently does at the moment.
But, you know, any kind of feedback regarding improvements to Gearset or anything that you'd like to see within Gearset as well, we do have a feedback forum.
So Gearset relies heavily on feedback from you folks. So, again, if there's things that you want to be able to see within the platform, I would highly highly recommend that you post those suggestions.
And then from kind of the popularity within those suggestions so people can kind of rate, and agree with what you want to see.
The team will then draw their work from that as well. So your suggestion could be pretty crucial as how gearset is shaped. So, again, as a side note, folks, that is, something that we kind of would love to hark on there as well.
So another question from Bruno is when disabling validation rules slash flows during a data deployment, does gear set automatically reenable them after deployment completes?
Anyone want to take that one?
Yep. Bruno, on that one, so, essentially, what you'll have at the end of that data deployment is an option to carry out that metadata, rollback.
Now, again, Carly and Tony David, I believe this isn't automatic. This is a a manual step you'll have to take. I remember to do as a post date deployment step.
But Gearset will do as much of that heavy lifting as you can. You'll be able to carry it out from that screen, as needed.
Just another thing to bear in mind as well is that will come up in the comparison history, so you'll be able to track all those metadata deployments that happen as well.
Perfect.
Lovely.
Any more questions? I can't see at the moment. Again, folks, if you do have any outstanding questions, please kinda send them over our way or, as you, I'm sure, will be able to demonstrate just in the bottom right hand corner there. We've got a blue icon where you can chat to our lovely customer success engineers, team there. So, if you're running into any technical issues or perhaps you'd want some guidance as to kind of how you'd run a data sealing deployment, anything like that, they will apply typically under ten minutes, if not quicker. They can share articles with you, jump on screen shares, and they are a great resource to have. So highly recommend you using them there, for any kind of needs that you'd, you may run into.
Just to flag those there too.
So, kind of with five minutes left, at the moment, I can't see any more questions. Again, feel free to to email us at success at gearset dot com, where you'll be able to speak to to us team, or as well we have the in app chat. So, what I will do is wrap up for today. Thank you so much, Hugh, for showing all of that. I hope that you folks at home now feel that you're able to be more confident within your data deployments within Gear Set, kind of namely how to get the data you need for your deployments, how you can choose kind of how to match or create your data, as well as how to kind of mask the important fields that you want to, as well and have that full oversight into your deployment history for that kind of data compliance side of things. So, again, if you wanted any more information about data seeding or in fact any other areas of gear set, we have an array of articles for you to read through on our website.
And we also do have a learning platform as well at your at your resist. So you can have a nice guide of how to use different functionalities within gear set, and the platform too.
So, if there is anything else that you need, again, reach us at success at gear set dot com, and we have it in our chat. But I will wrap up for today. Thank you everyone for joining. It's been lovely to see you, and we will see you for the next part of our accelerator series. Thank you so much.
