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Now CDAP provides the interface for users to handle their datasets in BigQuery.
Use-case
Users want to integrate CDAP with their already stored dataset in Google BigQuery.
User Stories
- As a User, I would like to run arbitrary queries synchronously against my datasets in BigQuery and pull those records into a hydrator pipeline.
- As a User, I would like to store data from a Hydrator pipeline into a table (dataset) in BigQuery. If the table doesn't exist, it should be created.
Requirements
1. User is able to query their datasets stored in Google BigQuery.
2. User should specify the limit time for the querying.
3. User is able to specify the limit size of the dataset to query.
4. User is able to poll for the result.
5. User can list the query result history for a duration of time.
6. The schema is automatically pulled from the table.
7. User can pull the field names from the query.
Example
Following is a simple example showing how BigQuery Source would work.
A dataset already exist in Google BigQuery:121
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project Id:
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vernal-
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seasdf-123456
dataset name: baby_names
name | count |
---|---|
Emma | 100 |
Oscar | 334 |
Peter | 223 |
Jay | 1123 |
Nicolas | 764 |
User pull the schema of the dataset:
Inputs | Value |
---|---|
project Id | vernal-seasdf-123456 |
dataset name | baby_names |
output schema:
name | String | ||
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count | Integer |
Design
CDAP provides two type of operations on the dataset stored in BigQuery: Query and Poll Results.
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