www.mongodb.com
Open in
urlscan Pro
2600:9000:2490:d800:7:7859:3840:93a1
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URL:
https://www.mongodb.com/
Submission: On July 17 via manual from IN — Scanned from DE
Submission: On July 17 via manual from IN — Scanned from DE
Form analysis 2 forms found in the DOM
GET https://www.mongodb.com/search
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<div class="css-87svlz">
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<li role="option" tabindex="0" class="css-11dtrvq">Realm Documentation</li>
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</form>Text Content
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{Blog} MongoDB to release new Vector Search and Stream Processing capabilities.
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Connecting to: mongodb+srv://cluster0.ab123.mongodb.net/myFirstDatabase
Using MongoDB: 6.0
# Confirm vector embeddings were added to movie data
# Return three documents in movieEmbeddings collection
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without having to ETL it to another system
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requests per day with no change in performance.
1-to-1
1-to-Many
Many-to-Many
0
# Option 1: Model one-to-many
1
# relationships with embedded documents
2
3
Order={
4
"_id": "2934f",
5
"salesDate": "2022-05-02",
6
"customer":{
7
"name": "Jack Beanstalk",
8
"gender": "M",
9
"rewardsMember": "True"
10
},
11
"items": [ {
12
"name": "Monstera",
13
"price":{
14
"$numberdecimal": "8.00"
15
},
16
"quantity":{
17
"$numberInt": "1"
18
}
19
},
20
{
21
"name": "Pothos",
22
"price":{
23
"$numberdecimal": "8.00"
24
},
25
"quantity":{
26
"$numberInt": "2"
27
}
28
} ]
29
}
30
31
# Option 2: Model one-to-many
32
# relationships with document references
33
34
Order={
35
"_id": "2934f",
36
"salesDate": "2022-05-02",
37
"customer": {
38
"name": "Jack Beanstalk",
39
"gender": "M",
40
"rewardsMember": "True"
41
},
42
"items": [ "12", "35", "86"]
43
}
44
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“The granular updates you can perform on nested @MongoDB data is pretty
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10M
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MongoDB for Insurance
40%
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4
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RETAIL
“Cost, speed, reliability and access to the data. From our perspective, we will
have realized a 40% cost saving by the time we've finished our migration. That's
huge.”“Cost, speed, reliability and access to the data. From our perspective, we
will have realized a 40% cost saving by the time we've finished our migration.
That's huge.”
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daily events processed
40%
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two billion per day and Atlas is well placed to handle that massive increase
without a problem.”
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MongoDB for Manufacturing
32
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2M+
customers supported
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us. It made it really easy for us to choose MongoDB because we didn’t have to
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could be flexible.”
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MongoDB for Financial Services
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