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Transformations

Combining Multiple Data Sources

Use these when you have data from two separate queries and want to bring them together.

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Join — DF_JOIN

Match rows from two datasets by a shared key column, similar to looking up a value in a reference table.

Use this when: you have two related tables — for example, sensor readings in one and device details (name, location, type) in another — and you want to combine them into one.

Join type	Keeps rows from…
inner	Only rows that match in both datasets
left	All rows from the first dataset; fills gaps with empty if no match
outer	All rows from both datasets

First dataset (sensor readings)

Code
[
  { "device_id": "AC-01", "kwh": 1.2 },
  { "device_id": "AC-02", "kwh": 1.7 },
  { "device_id": "AC-03", "kwh": 0.9 }
]

Second dataset (device details)

Code
[
  { "device_id": "AC-01", "name": "Meeting Room A", "floor": 1 },
  { "device_id": "AC-02", "name": "Reception",      "floor": 1 },
  { "device_id": "AC-04", "name": "Server Room",    "floor": 2 }
]

After — inner join on device_id

Code
[
  { "device_id": "AC-01", "kwh": 1.2, "name": "Meeting Room A", "floor": 1 },
  { "device_id": "AC-02", "kwh": 1.7, "name": "Reception",      "floor": 1 }
]

Code
{
  "type": "DF_JOIN",
  "config": {
    "joinType": "inner",
    "joinField": "device_id",
    "secondaryFrameIndex": 0
  }
}

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Merge — DF_MERGE

Combine columns from two datasets that share the same index column, adding new columns side-by-side.

Use this when: both datasets have the same rows (aligned on a shared key, like timestamp) and you want to add new columns from the second one — rather than matching and filtering rows like Join does.

First dataset

Code
[
  { "timestamp": "08:00", "temp": 24 },
  { "timestamp": "09:00", "temp": 26 },
  { "timestamp": "10:00", "temp": 25 }
]

Second dataset

Code
[
  { "timestamp": "08:00", "humidity": 60 },
  { "timestamp": "09:00", "humidity": 58 },
  { "timestamp": "10:00", "humidity": 62 }
]

After — merged on timestamp

Code
[
  { "timestamp": "08:00", "temp": 24, "humidity": 60 },
  { "timestamp": "09:00", "temp": 26, "humidity": 58 },
  { "timestamp": "10:00", "temp": 25, "humidity": 62 }
]

Code
{
  "type": "DF_MERGE",
  "config": {
    "mergeField": "timestamp",
    "secondaryFrameIndex": 0
  }
}

---

Concatenate — DF_CONCATENATE

Stack rows from multiple datasets on top of each other.

Use this when: you have the same kind of data from different sources or time periods, and you want to treat them as one combined dataset — for example, January readings and February readings.

First dataset (January)

Code
[
  { "device": "AC-01", "kwh": 1.2, "month": "Jan" },
  { "device": "AC-02", "kwh": 1.7, "month": "Jan" }
]

Second dataset (February)

Code
[
  { "device": "AC-01", "kwh": 1.5, "month": "Feb" },
  { "device": "AC-02", "kwh": 2.0, "month": "Feb" }
]

After — stacked together

Code
[
  { "device": "AC-01", "kwh": 1.2, "month": "Jan" },
  { "device": "AC-02", "kwh": 1.7, "month": "Jan" },
  { "device": "AC-01", "kwh": 1.5, "month": "Feb" },
  { "device": "AC-02", "kwh": 2.0, "month": "Feb" }
]

Code
{
  "type": "DF_CONCATENATE",
  "config": {
    "frameIndices": [0, 1],
    "addQueryIdField": false
  }
}

Set "addQueryIdField": true to add a column tracking which dataset each row came from.