# Customizing Schema (fields & mappings) ### About this export | Field | Value | | --- | --- | | **content_type** | lesson | | **platform** | contentstack-academy | | **source_url** | https://www.contentstack.com/academy/courses/data-insights-data-ingestion-profile-construction/data-insights-course-3--customizing-schema | | **course_slug** | data-insights-data-ingestion-profile-construction | | **lesson_slug** | data-insights-course-3--customizing-schema | | **markdown_file_url** | /academy/md/courses/data-insights-data-ingestion-profile-construction/data-insights-course-3--customizing-schema.md | | **generated_at** | 2026-04-28T06:55:44.147Z | > Part of **[Data Ingestion & Profile Construction](https://www.contentstack.com/academy/courses/data-insights-data-ingestion-profile-construction)** on Contentstack Academy. **Academy MD v3** — structured for retrieval; no quiz or assessment keys. #### Video details #### At a glance - **Title:** 12-data-insights-custom-schema - **Duration:** 9m - **Media link:** https://cdn.jwplayer.com/previews/fGpn7GIn - **Publish date (unix):** 1752872612 #### Streaming renditions - application/vnd.apple.mpegurl - audio/mp4 · AAC Audio · 113479 kbps - video/mp4 · 180p · 180p · 140452 kbps - video/mp4 · 270p · 270p · 157205 kbps - video/mp4 · 360p · 360p · 174433 kbps - video/mp4 · 540p · 540p · 224977 kbps #### Timed text tracks (delivery) - **thumbnails:** `https://cdn.jwplayer.com/strips/fGpn7GIn-120.vtt` #### Transcript The main thing that I want to cover in this session is how the fields and the mappings work and how their relationship is to stream. So a perfect example of that is we have a common schema, a bunch of the heavy lifting has already been done, we'll connect MailChimp in a little while and it automatically gets the fields and mappings, but it is inevitable that you're going to want to push custom data into Linux. So I built a very simple, also Game of Thrones themed CSV, where we're pulling in things like email and first name and last name, and we use the attribute field, which comes kind of pre-mapped. But then you run into this field where it's likelihood to rain, which is, you know, akin to say like a custom score that's coming out of your warehouse or whatever it may be. So this is a thing that isn't in the system, it's totally custom. We need to make sure that when we upload this data, whichever stream we upload it to, it's going to ultimately get mapped properly to that particular profile. So I wanted to walk through that example together anyway, so we can just start to do that. So if I were to upload this information right now with no mappings, you would see it in the streams. So I'm not going to do that, but you could ultimately upload this to whatever stream you would want. You could go over here and you would see the raw data in the stream that actually was received, but it's not going to have anywhere to go with it. So it's going to sit in the stream as sort of a metric. It's never going to get mapped to the profile. It's just going to sort of confuse you. So it's really, really important to know that streams to Eric's point are kind of just a construct to help you separate the logic for mapping that data to a particular field. So in our case, we want to add the field likelihood to rain. The way that I would start to do this, and there's a couple of different approaches, but because we know that this field is net new, you're going to first need to go create a field, right? So you need to put a place on the profile where this information can actually live. We don't want to use any of the common scheme of fields, so I'm going to go to create new. You first select an ID. This can be any sort of sluggified input, so I'm just going to paste likelihood to rain. We'll just call it that for now. We'll say, you know, custom score on like rain, which is really just surfaced in the information in the UI. We'll choose the data type. So in our case, it looks like it's an integer. We want to be able to do things like greater than less than in the segmentation engine. That's one really important point when you're choosing the data type. It impacts the way that you can actually leverage that particular field in segmentation. So for instance, if I were to make this a string, it would work, but when I go to build a segment, if I wanted to find anybody that was like greater than 50 on likelihood terrain, it's not going to work. It's not going to allow you to do that because it can't do a sort of numeric operation on a string. So those kind of things are really important to keep in mind as you're doing custom mapping. Obviously there's lots of different data types that you could use. There's arrays, there's string arrays, there's time arrays, there's maps, et cetera, et cetera. We're just going to choose an integer in this case. You can add an optional description. This is just purely for the UI, so we won't do that here. You could choose it as an identity key. If it's a string, you can't do that with an integer. We don't want to do that with likelihood terrain. If I were to accidentally do that, the net result would essentially be that anybody that had, say, like a 95 score of likelihood terrain would be merged into one super profile, which is definitely not what you would want to do. So that's why only clicking the identity key when it's absolutely necessary and you want to use it to merge data together is very, very important. For a merge operator, because it's an integer, you'll see that there's a number of different ways that you can ultimately handle this data. Maybe I want the total purchase amount over time. You could do the integer as a sum. As new events come in, it's going to continue to add those up so that you have this one value that represents total purchase over time. You could just count the events. So instead of having an understanding of, like, the specific number, it's just how many times I've seen this sort of field change. The max number, the min number, the latest, the oldest. In our case, we want it to just represent the actual score value, and we want it to be the latest value so that if a new one comes in tomorrow when we push this information, it gets updated and overridden. So we're going to do latest. And then we'll leave all of the other fields. They're all grayed out, actually, in this case, because you can't set them. If we were to set or choose an array or a map, you'd have some more options to Eric's kind of earlier point on the size limit and how long some of that information hangs around. The other thing that's really important is you can flag your fields as if they contain PII or not. Because a CDP is collecting user information, a lot of that information can be personally identified, which becomes really, really important. We have lots of controls in the UI to hide or show that information for particular users. In a lot of cases, our customers will maybe encrypt or encode or Base64 encode an email address to never expose that. So there's lots of controls that you'll see around privacy. This just lets you know the field may contain PII so that throughout the UI, we can put the proper controls in place. And then there's some categorization options that are totally optional. They just show up in the actual profile kind of filtering when you're exploring. So we'll just say that that's behavior for now. So this is how we're going to configure our field. If I hit create field, this essentially just gives it a place to live on the profile. But there's still no logic on how do I take the raw data that's coming in and map it to that particular profile, right? There's still that kind of gap. So that's where mappings come in. So if I go back to my field, likelihood terrain, I'll go to current mappings and I see that there's no mappings. So we'll hit create new mapping and you'll see three new concepts that we have not touched on yet. So well, two of the three we haven't touched on yet. So stream is going to say, where do I want to create this mapping to? I can choose one of the streams that exist, or we'll just do demo custom CSV. I'm going to copy this and put it over here so I don't forget that. And then you have an opportunity to do an expression or a condition. So what the expression does is there's a few different functions that we have. So you saw it like on email, as an example, where it's going to take it, it's going to lowercase it, it's going to do some basic validation. There's things for phone number to normalize. There's ways that you can split, you can uppercase, you can lowercase. There's a whole list of the different expressions in our documentation that we can kind of come back to. But this is one of the, I wouldn't call it a cleansing layer by any means, but it's one of the ways that you can manipulate some of the endpoints to make them consistent. In our case, we're just going to do likelihood to rain. So we just want to like take a direct translation, oops, I pasted the wrong value. That's not what we want. So all I want to do is I want to take the key that'll ultimately come in as likelihood to rain in our CSV. And I want to say anytime that that value comes in, we're going to, as long as it's on the demo custom CSV stream, we're going to map it up to that profile. The conditions we'll come back to, and I'll show you a few different examples, but conditions give you a way to say, like when we're mapping conversion events or events in general, if it's a particular type, then I want to map it in this way. So you can add essentially if else equals type logic to a particular mapping so that there's conditional logic, whether it should map that field or not, as opposed to just in this case, anytime that likelihood to rain shows up in this particular stream, it's going to map it to the field that I chose, which is this custom score likelihood to rain. So I'm going to create, oh yeah, go ahead. One comment here real quick. If this was like a untrusted source, where this data was coming from, you probably would want to put an expression in there that would validate that it's a number in the range of one to a hundred. But since this is a source that we generated that we know is clean the whole way through, we can just take the value straight from the CSV. Yep. And we'll pull up the docs and show some of like the conditions and expressions and whatnot, just so you know where it is. There's a whole bunch of things that you can do ultimately from a logic perspective, but try to keep it as simple as possible for this example. So I am going to create that mapping for that field. #### Subtitles (WebVTT) ```webvtt WEBVTT 1 00:00:00.000 --> 00:00:19.160 The main thing that I want to cover in this session is how the fields and the mappings 2 00:00:19.160 --> 00:00:21.060 work and how their relationship is to stream. 3 00:00:21.060 --> 00:00:25.920 So a perfect example of that is we have a common schema, a bunch of the heavy lifting 4 00:00:25.920 --> 00:00:29.600 has already been done, we'll connect MailChimp in a little while and it automatically gets 5 00:00:29.600 --> 00:00:34.100 the fields and mappings, but it is inevitable that you're going to want to push custom data 6 00:00:34.100 --> 00:00:35.100 into Linux. 7 00:00:35.100 --> 00:00:41.320 So I built a very simple, also Game of Thrones themed CSV, where we're pulling in things 8 00:00:41.320 --> 00:00:44.760 like email and first name and last name, and we use the attribute field, which comes kind 9 00:00:44.760 --> 00:00:45.900 of pre-mapped. 10 00:00:45.900 --> 00:00:49.640 But then you run into this field where it's likelihood to rain, which is, you know, akin 11 00:00:49.640 --> 00:00:53.520 to say like a custom score that's coming out of your warehouse or whatever it may be. 12 00:00:53.520 --> 00:00:57.300 So this is a thing that isn't in the system, it's totally custom. 13 00:00:57.300 --> 00:01:01.980 We need to make sure that when we upload this data, whichever stream we upload it to, 14 00:01:01.980 --> 00:01:05.800 it's going to ultimately get mapped properly to that particular profile. 15 00:01:05.800 --> 00:01:11.020 So I wanted to walk through that example together anyway, so we can just start to do that. 16 00:01:11.020 --> 00:01:17.980 So if I were to upload this information right now with no mappings, you would see it in 17 00:01:17.980 --> 00:01:18.980 the streams. 18 00:01:18.980 --> 00:01:23.100 So I'm not going to do that, but you could ultimately upload this to whatever stream 19 00:01:23.100 --> 00:01:24.380 you would want. 20 00:01:24.420 --> 00:01:29.580 You could go over here and you would see the raw data in the stream that actually was received, 21 00:01:29.580 --> 00:01:31.140 but it's not going to have anywhere to go with it. 22 00:01:31.140 --> 00:01:33.860 So it's going to sit in the stream as sort of a metric. 23 00:01:33.860 --> 00:01:35.780 It's never going to get mapped to the profile. 24 00:01:35.780 --> 00:01:37.380 It's just going to sort of confuse you. 25 00:01:37.380 --> 00:01:41.700 So it's really, really important to know that streams to Eric's point are kind of just a 26 00:01:41.700 --> 00:01:48.020 construct to help you separate the logic for mapping that data to a particular field. 27 00:01:48.020 --> 00:01:53.120 So in our case, we want to add the field likelihood to rain. 28 00:01:53.120 --> 00:01:55.800 The way that I would start to do this, and there's a couple of different approaches, 29 00:01:55.800 --> 00:02:00.840 but because we know that this field is net new, you're going to first need to go create 30 00:02:00.840 --> 00:02:01.840 a field, right? 31 00:02:01.840 --> 00:02:05.720 So you need to put a place on the profile where this information can actually live. 32 00:02:05.720 --> 00:02:09.920 We don't want to use any of the common scheme of fields, so I'm going to go to create new. 33 00:02:09.920 --> 00:02:11.320 You first select an ID. 34 00:02:11.320 --> 00:02:16.360 This can be any sort of sluggified input, so I'm just going to paste likelihood to rain. 35 00:02:16.360 --> 00:02:18.400 We'll just call it that for now. 36 00:02:18.400 --> 00:02:29.080 We'll say, you know, custom score on like rain, which is really just surfaced in the 37 00:02:29.080 --> 00:02:30.840 information in the UI. 38 00:02:30.840 --> 00:02:32.400 We'll choose the data type. 39 00:02:32.400 --> 00:02:35.000 So in our case, it looks like it's an integer. 40 00:02:35.000 --> 00:02:40.120 We want to be able to do things like greater than less than in the segmentation engine. 41 00:02:40.120 --> 00:02:43.720 That's one really important point when you're choosing the data type. 42 00:02:43.720 --> 00:02:48.560 It impacts the way that you can actually leverage that particular field in segmentation. 43 00:02:48.560 --> 00:02:52.440 So for instance, if I were to make this a string, it would work, but when I go to build 44 00:02:52.440 --> 00:02:58.320 a segment, if I wanted to find anybody that was like greater than 50 on likelihood terrain, 45 00:02:58.320 --> 00:02:59.320 it's not going to work. 46 00:02:59.320 --> 00:03:02.640 It's not going to allow you to do that because it can't do a sort of numeric operation on 47 00:03:02.640 --> 00:03:04.000 a string. 48 00:03:04.000 --> 00:03:10.520 So those kind of things are really important to keep in mind as you're doing custom mapping. 49 00:03:10.520 --> 00:03:14.760 Obviously there's lots of different data types that you could use. 50 00:03:14.760 --> 00:03:19.000 There's arrays, there's string arrays, there's time arrays, there's maps, et cetera, et cetera. 51 00:03:19.000 --> 00:03:21.960 We're just going to choose an integer in this case. 52 00:03:21.960 --> 00:03:23.200 You can add an optional description. 53 00:03:23.200 --> 00:03:26.140 This is just purely for the UI, so we won't do that here. 54 00:03:26.140 --> 00:03:28.200 You could choose it as an identity key. 55 00:03:28.200 --> 00:03:30.120 If it's a string, you can't do that with an integer. 56 00:03:30.120 --> 00:03:33.120 We don't want to do that with likelihood terrain. 57 00:03:33.120 --> 00:03:38.920 If I were to accidentally do that, the net result would essentially be that anybody that 58 00:03:38.920 --> 00:03:45.120 had, say, like a 95 score of likelihood terrain would be merged into one super profile, which 59 00:03:45.120 --> 00:03:47.160 is definitely not what you would want to do. 60 00:03:47.160 --> 00:03:51.720 So that's why only clicking the identity key when it's absolutely necessary and you want 61 00:03:51.720 --> 00:03:54.720 to use it to merge data together is very, very important. 62 00:03:54.720 --> 00:03:59.080 For a merge operator, because it's an integer, you'll see that there's a number of different 63 00:03:59.080 --> 00:04:01.720 ways that you can ultimately handle this data. 64 00:04:01.720 --> 00:04:05.880 Maybe I want the total purchase amount over time. 65 00:04:05.880 --> 00:04:07.280 You could do the integer as a sum. 66 00:04:07.280 --> 00:04:10.120 As new events come in, it's going to continue to add those up so that you have this one 67 00:04:10.120 --> 00:04:14.000 value that represents total purchase over time. 68 00:04:14.000 --> 00:04:15.400 You could just count the events. 69 00:04:15.400 --> 00:04:19.280 So instead of having an understanding of, like, the specific number, it's just how many 70 00:04:19.280 --> 00:04:22.280 times I've seen this sort of field change. 71 00:04:22.280 --> 00:04:24.280 The max number, the min number, the latest, the oldest. 72 00:04:24.280 --> 00:04:27.880 In our case, we want it to just represent the actual score value, and we want it to 73 00:04:27.880 --> 00:04:32.240 be the latest value so that if a new one comes in tomorrow when we push this information, 74 00:04:32.240 --> 00:04:33.520 it gets updated and overridden. 75 00:04:33.520 --> 00:04:35.680 So we're going to do latest. 76 00:04:35.680 --> 00:04:39.200 And then we'll leave all of the other fields. 77 00:04:39.200 --> 00:04:41.640 They're all grayed out, actually, in this case, because you can't set them. 78 00:04:41.640 --> 00:04:45.480 If we were to set or choose an array or a map, you'd have some more options to Eric's 79 00:04:45.480 --> 00:04:51.280 kind of earlier point on the size limit and how long some of that information hangs around. 80 00:04:51.280 --> 00:04:56.640 The other thing that's really important is you can flag your fields as if they contain 81 00:04:56.640 --> 00:04:58.240 PII or not. 82 00:04:58.240 --> 00:05:02.480 Because a CDP is collecting user information, a lot of that information can be personally 83 00:05:02.480 --> 00:05:04.420 identified, which becomes really, really important. 84 00:05:04.420 --> 00:05:09.860 We have lots of controls in the UI to hide or show that information for particular users. 85 00:05:09.860 --> 00:05:15.780 In a lot of cases, our customers will maybe encrypt or encode or Base64 encode an email 86 00:05:15.780 --> 00:05:17.220 address to never expose that. 87 00:05:17.220 --> 00:05:21.180 So there's lots of controls that you'll see around privacy. 88 00:05:21.180 --> 00:05:26.460 This just lets you know the field may contain PII so that throughout the UI, we can put 89 00:05:26.460 --> 00:05:27.940 the proper controls in place. 90 00:05:27.940 --> 00:05:32.140 And then there's some categorization options that are totally optional. 91 00:05:32.140 --> 00:05:36.500 They just show up in the actual profile kind of filtering when you're exploring. 92 00:05:36.500 --> 00:05:40.060 So we'll just say that that's behavior for now. 93 00:05:40.060 --> 00:05:43.220 So this is how we're going to configure our field. 94 00:05:43.220 --> 00:05:48.140 If I hit create field, this essentially just gives it a place to live on the profile. 95 00:05:48.140 --> 00:05:53.420 But there's still no logic on how do I take the raw data that's coming in and map it to 96 00:05:53.420 --> 00:05:54.900 that particular profile, right? 97 00:05:54.900 --> 00:05:56.780 There's still that kind of gap. 98 00:05:56.780 --> 00:05:58.480 So that's where mappings come in. 99 00:05:58.560 --> 00:06:04.240 So if I go back to my field, likelihood terrain, I'll go to current mappings and I see that 100 00:06:04.240 --> 00:06:06.280 there's no mappings. 101 00:06:06.280 --> 00:06:13.400 So we'll hit create new mapping and you'll see three new concepts that we have not touched 102 00:06:13.400 --> 00:06:14.400 on yet. 103 00:06:14.400 --> 00:06:16.840 So well, two of the three we haven't touched on yet. 104 00:06:16.840 --> 00:06:20.600 So stream is going to say, where do I want to create this mapping to? 105 00:06:20.600 --> 00:06:28.040 I can choose one of the streams that exist, or we'll just do demo custom CSV. 106 00:06:28.080 --> 00:06:34.000 I'm going to copy this and put it over here so I don't forget that. 107 00:06:34.000 --> 00:06:38.480 And then you have an opportunity to do an expression or a condition. 108 00:06:38.480 --> 00:06:42.600 So what the expression does is there's a few different functions that we have. 109 00:06:42.600 --> 00:06:45.960 So you saw it like on email, as an example, where it's going to take it, it's going to 110 00:06:45.960 --> 00:06:49.040 lowercase it, it's going to do some basic validation. 111 00:06:49.040 --> 00:06:50.840 There's things for phone number to normalize. 112 00:06:50.840 --> 00:06:54.280 There's ways that you can split, you can uppercase, you can lowercase. 113 00:06:54.280 --> 00:06:58.040 There's a whole list of the different expressions in our documentation that we can kind of come 114 00:06:58.040 --> 00:06:59.280 back to. 115 00:06:59.280 --> 00:07:04.320 But this is one of the, I wouldn't call it a cleansing layer by any means, but it's one 116 00:07:04.320 --> 00:07:08.200 of the ways that you can manipulate some of the endpoints to make them consistent. 117 00:07:08.200 --> 00:07:14.240 In our case, we're just going to do likelihood to rain. 118 00:07:14.240 --> 00:07:19.480 So we just want to like take a direct translation, oops, I pasted the wrong value. 119 00:07:19.480 --> 00:07:21.900 That's not what we want. 120 00:07:21.900 --> 00:07:26.300 So all I want to do is I want to take the key that'll ultimately come in as likelihood 121 00:07:26.300 --> 00:07:28.060 to rain in our CSV. 122 00:07:28.060 --> 00:07:31.780 And I want to say anytime that that value comes in, we're going to, as long as it's 123 00:07:31.780 --> 00:07:36.700 on the demo custom CSV stream, we're going to map it up to that profile. 124 00:07:36.700 --> 00:07:39.940 The conditions we'll come back to, and I'll show you a few different examples, but conditions 125 00:07:39.940 --> 00:07:46.820 give you a way to say, like when we're mapping conversion events or events in general, if 126 00:07:46.820 --> 00:07:49.740 it's a particular type, then I want to map it in this way. 127 00:07:49.740 --> 00:07:55.580 So you can add essentially if else equals type logic to a particular mapping so that 128 00:07:55.580 --> 00:07:59.980 there's conditional logic, whether it should map that field or not, as opposed to just 129 00:07:59.980 --> 00:08:05.260 in this case, anytime that likelihood to rain shows up in this particular stream, it's going 130 00:08:05.260 --> 00:08:11.200 to map it to the field that I chose, which is this custom score likelihood to rain. 131 00:08:11.200 --> 00:08:13.420 So I'm going to create, oh yeah, go ahead. 132 00:08:13.420 --> 00:08:14.980 One comment here real quick. 133 00:08:14.980 --> 00:08:21.260 If this was like a untrusted source, where this data was coming from, you probably would 134 00:08:21.260 --> 00:08:24.980 want to put an expression in there that would validate that it's a number in the range of 135 00:08:24.980 --> 00:08:27.120 one to a hundred. 136 00:08:27.120 --> 00:08:31.380 But since this is a source that we generated that we know is clean the whole way through, 137 00:08:31.380 --> 00:08:34.220 we can just take the value straight from the CSV. 138 00:08:34.220 --> 00:08:35.220 Yep. 139 00:08:35.220 --> 00:08:39.540 And we'll pull up the docs and show some of like the conditions and expressions and whatnot, 140 00:08:39.540 --> 00:08:40.780 just so you know where it is. 141 00:08:40.780 --> 00:08:45.940 There's a whole bunch of things that you can do ultimately from a logic perspective, but 142 00:08:45.940 --> 00:08:47.980 try to keep it as simple as possible for this example. 143 00:08:47.980 --> 00:08:51.460 So I am going to create that mapping for that field. ``` ```transcript [00:00] The main thing that I want to cover in this session is how the fields and the mappings [00:19] work and how their relationship is to stream. [00:21] So a perfect example of that is we have a common schema, a bunch of the heavy lifting [00:25] has already been done, we'll connect MailChimp in a little while and it automatically gets [00:29] the fields and mappings, but it is inevitable that you're going to want to push custom data [00:34] into Linux. [00:35] So I built a very simple, also Game of Thrones themed CSV, where we're pulling in things [00:41] like email and first name and last name, and we use the attribute field, which comes kind [00:44] of pre-mapped. [00:45] But then you run into this field where it's likelihood to rain, which is, you know, akin [00:49] to say like a custom score that's coming out of your warehouse or whatever it may be. [00:53] So this is a thing that isn't in the system, it's totally custom. [00:57] We need to make sure that when we upload this data, whichever stream we upload it to, [01:01] it's going to ultimately get mapped properly to that particular profile. [01:05] So I wanted to walk through that example together anyway, so we can just start to do that. [01:11] So if I were to upload this information right now with no mappings, you would see it in [01:17] the streams. [01:18] So I'm not going to do that, but you could ultimately upload this to whatever stream [01:23] you would want. [01:24] You could go over here and you would see the raw data in the stream that actually was received, [01:29] but it's not going to have anywhere to go with it. [01:31] So it's going to sit in the stream as sort of a metric. [01:33] It's never going to get mapped to the profile. [01:35] It's just going to sort of confuse you. [01:37] So it's really, really important to know that streams to Eric's point are kind of just a [01:41] construct to help you separate the logic for mapping that data to a particular field. [01:48] So in our case, we want to add the field likelihood to rain. [01:53] The way that I would start to do this, and there's a couple of different approaches, [01:55] but because we know that this field is net new, you're going to first need to go create [02:00] a field, right? [02:01] So you need to put a place on the profile where this information can actually live. [02:05] We don't want to use any of the common scheme of fields, so I'm going to go to create new. [02:09] You first select an ID. [02:11] This can be any sort of sluggified input, so I'm just going to paste likelihood to rain. [02:16] We'll just call it that for now. [02:18] We'll say, you know, custom score on like rain, which is really just surfaced in the [02:29] information in the UI. [02:30] We'll choose the data type. [02:32] So in our case, it looks like it's an integer. [02:35] We want to be able to do things like greater than less than in the segmentation engine. [02:40] That's one really important point when you're choosing the data type. [02:43] It impacts the way that you can actually leverage that particular field in segmentation. [02:48] So for instance, if I were to make this a string, it would work, but when I go to build [02:52] a segment, if I wanted to find anybody that was like greater than 50 on likelihood terrain, [02:58] it's not going to work. [02:59] It's not going to allow you to do that because it can't do a sort of numeric operation on [03:02] a string. [03:04] So those kind of things are really important to keep in mind as you're doing custom mapping. [03:10] Obviously there's lots of different data types that you could use. [03:14] There's arrays, there's string arrays, there's time arrays, there's maps, et cetera, et cetera. [03:19] We're just going to choose an integer in this case. [03:21] You can add an optional description. [03:23] This is just purely for the UI, so we won't do that here. [03:26] You could choose it as an identity key. [03:28] If it's a string, you can't do that with an integer. [03:30] We don't want to do that with likelihood terrain. [03:33] If I were to accidentally do that, the net result would essentially be that anybody that [03:38] had, say, like a 95 score of likelihood terrain would be merged into one super profile, which [03:45] is definitely not what you would want to do. [03:47] So that's why only clicking the identity key when it's absolutely necessary and you want ``` #### Key takeaways - Connect **Customizing Schema (fields & mappings)** back to your stack configuration before moving to the next module. - Capture one concrete artifact (screenshot, Postman call, or code snippet) that proves the step works in your environment. - Re-read the delivery versus management boundary for anything you changed in the entry model. ## Supplement for indexing ### Content summary Customizing Schema (fields & mappings). Customizing Schema (fields & mappings) in Data Ingestion & Profile Construction (data-insights-data-ingestion-profile-construction). ### Retrieval tags - Customizing - Schema - fields - mappings - data-insights-data-ingestion-profile-construction - lesson 04 - Customizing Schema (fields & mappings) - data-insights-data-ingestion-profile-construction lesson ### Indexing notes Index this lesson as a primary chunk tagged with lesson_id "04" and topics: [Customizing, Schema, fields, mappings]. Parent course slug: data-insights-data-ingestion-profile-construction. Use asset_references URLs as thumbnail hints in search results when present. Never surface LMS quiz content or assessment answers from this file. ### Asset references | Label | URL | | --- | --- | | Video thumbnail: Customizing Schema (fields & mappings) | `https://cdn.jwplayer.com/v2/media/fGpn7GIn/poster.jpg?width=720` | ### External links | Label | URL | | --- | --- | | Contentstack Academy home | `https://www.contentstack.com/academy/` | | Training instance setup | `https://www.contentstack.com/academy/training-instance` | | Academy playground (GitHub) | `https://github.com/contentstack/contentstack-academy-playground` | | Contentstack documentation | `https://www.contentstack.com/docs/` |