Business event processing

Business event processing uses the capabilities of the business event ingest pipeline to refine your data.

Within the business event ingest pipeline, you can define how your data will be processed further.

You can create a processing rule that transforms incoming events.
You can also specify the retention time for your events by assigning them to buckets.

If you create multiple rules, the rules are performed in the order in which you have defined them.

Configure business event processing

To configurebusiness event processing

Go to Settings > Business Analytics > Ingest Pipeline > Processing.
Select Add rule and name your rule.
Add a Matcher to your rule by pasting your matcher-specific DQL query.

Matching based on previous rules is not supported
The Matcher operates on the initial data set before applying any processing rules. Matching records modified by preceding rules is not supported. For example, the modified value of an attribute in rule 1 cannot be used as a matching value in rule 2.
Select Add item to choose the fields you will transform via the processor definition. Determine the data types and names for your fields. You can choose if the rule is Optional, Is Array, or Read-only.
- Optional: If a transformation field is marked as optional, the transformation can happen even if the field is not provided. But if a transformation field is marked as mandatory (not optional) , the transformation occures only if the field is present in the incoming data.
- Is Array: Needs to be selected when a field contains multiple values, and you intend to work with specific values.
- Read-Only: Cannot be changed during transformation.
Add a Processor definition. Processor definitions are instructions that tell Dynatrace how to transform the data you’ve selected in the matcher DQL query. It is created using processing commands, functions and pattern matching (Dynatrace Pattern Language) that allows you to add, modify or remove incoming records.
In the Rule testing section, paste an event sample and run the test.

Your incoming data example needs to be provided in the JSON format.
Select Save changes.

Business events processing examples

In the below examples, you can see how ingest pipeline processing can be used to meet your technical and administrative requirements.

Example 1. Add a new calculated field to the pipeline.
As a user, you need to add a calculated dollar trading volume value to the pipeline.
```
{
 "action":"buy",
 "accountId":6,
 "amount":10,
 "instrumentId":1,
 "price":157.025
}
```
- Rule name: Add field
- Matcher: matchesValue(action, "buy")
- Transformation fields:
Name
Type
Optional
is Array
Read-only
amount
double
false
false
true
price
double
false
false
true
- Processor definition: FIELDS_ADD(trading_volume: price*amount)
Example 2. Mask your data.
You need to hide the CVV field on your credit card in an incoming JSON payload.
```
{
 "action": "payment",
 "creditCardNumber":"5570001112223344",
 "valid":"12/27",
 "cvv":"001"
}
```
- Rule name: Mask field
- Matcher: matchesValue(action, "payment")
- Transformation fields:
Name
Type
Optional
is Array
Read-only
cvv
string
false
false
false
- Processor definition: FIELDS_ADD(cvv: SHA1(cvv))
Example 3. Drop event attribute.
You need to drop the birthdate field in an incoming JSON payload.
```
{
 "action": "newUser",
 "firstName":"Frank",
 "lastName": "Underwud",
 "birthDate": "10.01.1967"
}
```
- Rule name: Drop field
- Matcher: matchesValue(action, "newUser")
- Transformation fields:
Name
Type
Optional
is Array
Read-only
birthDate
string
false
false
false
- Processor definition: FIELDS_REMOVE(birthDate)
Example 4. Parse nested JSON.
You need to parse attributes from a nested JSON in order to have them as top-level attributes in Grail.
```
{
  "action":"sell",
  "details":{
     "accountId":6,
     "amount":10,
     "instrumentId":1,
     "price":157.025
   }
 }
```
- Rule name: Parse field
- Matcher: matchesValue(action, "sell")
- Transformation fields:
Name
Type
Optional
is Array
Read-only
details
string
false
false
false
- Processor definition: PARSE(details,"JSON{INTEGER:accountId,INTEGER:amount,INTEGER:instrumentId,DOUBLE:price}(flat=true)") | FIELDS_REMOVE(details)
Example 5. Parse error message.

In this example, you need to remove the voucher code from the error message field below to have the ability to count how often the same error message appears. The solution below leverages the Dynatrace Pattern language for parsing.
```
{
"timestamp":"2023-01-18T10:50:23.777000000Z",
"cartId":"58583939",
"error.message":"The voucher [XY-892940] is not valid!",
"error.messageKey":"error.voucher "
}
```
- Rule name: Parse error message
- Matcher: matchesValue(error.messageKey, "error.voucher ")
- Transformation fields:
Name
Type;
Optional
is Array
Read-only
error.message
string
no
no
no
- Processor definition: FIELDS_ADD(final:REPLACE_PATTERN(error.message, "LD:p1 '[' LD:to_be_masked ']' LD:p2 ", "${p1}${p2}"))
- Result: The voucher is not valid!.