target-bigquery

A Singer target that writes data to Google BigQuery.

target-bigquery works together with any other Singer Tap to move data from sources like Braintree, Freshdesk and Hubspot to Google BigQuery.

Contact

Email: analytics-help@adswerve.com

How to use it

Step 1: Enable Google BigQuery API

GCP web console -> API & Services -> Library

Search for BigQuery API -> click Enable

Step 2: Authenticate with a Service Account

It is recommended to use target-bigquery with a service account.

Create a service account credential:

API & Services -> Credentials -> Create Credentials -> Service account

Under Service account details, enter Service account name. Click Create

Under Grant this service account access to the project, select BigQuery Data Editor and BigQuery Job User as the minimal set of permissions. Click Done

BigQuery Data Editor permission allows the service account to access (and change) the data.
BigQuery Job User permission allows the service account to actually run a load or select job.

On the API & Services Credentials screen, select the service account you just created.

Click ADD KEY -> Create new key -> JSON key. Download the service account credential JSON file.

Name the file client_secrets.json. You can place the file where target-bigquery will be executed or provide a path to the service account json file.
Set a GOOGLE_APPLICATION_CREDENTIALS environment variable on the machine, where the value is the fully qualified path to client_secrets.json file:

Step 3: Configure

Target config file

Create a file called target-config.json in your working directory, following this sample target-config.json file (or see the example below).

Required parameters are the project name project_id and dataset_id.
Optional parameters are table_suffix, validate records, add_metadata_columns and location.
Default data location is "US" (if your location is not the US, you can indicate a different location in your target-config.json file).
The data will be written to the dataset specified in your target-config.json.
If you do not have the dataset with this name yet, it will be created.
The table will be created.

Sample target-config.json file:

{
    "project_id": "{your_GCP_project_id}",
    "dataset_id": "{your_dataset_id}",
    "table_suffix": "_sample_table_suffix",
    "validate_records": true,
    "add_metadata_columns": true,
    "location": "EU"
}

Tap config files

This is a little bit outside of the scope of this documentation, but let's quickly take a look at sample tap config files as well, to see how tap and target work together.

Sample tap-config.json file configures the data source:

{   "base": "USD",
    "start_date": "2021-01-01"
}

Sample state.json file is now just a empty JSON file {}, and it will be written or updated when the tap runs.
This is an optional file.
The tap will write the date into state.json file, indicating when the data loading stopped at.
Next time you run the tap, it'll continue from this date in the state file. If state.json file is provided, then it takes presedence over the "start_date" in the tap config file.

Learn more: https://github.com/singer-io/getting-started

Step 4: Install and Run

First, make sure Python 3 is installed on your system or follow these installation instructions for Mac or Ubuntu.
target-bigquery can be run with any Singer Tap, but we'll use tap-exchangeratesapi - which pulls currency exchange rate data from a public data set - as an example. (Learn more about Exchangeratesapi.io)
In the `sample_config/target-config. file, enter the id of your GCP (Google Cloud Platform Project) - you can find it on the Home page of your GCP web console.

Sample target-config.json file:

{
    "project_id": "{your project id}",
    "dataset_id": "exchangeratesapi"
}

These commands will install tap-exchangeratesapi and target-bigquery with pip and then run them together, piping the output of tap-exchangeratesapi to target-bigquery:

> cd "{your project root directory}"

› pip install tap-exchangeratesapi git+git://github.com/adswerve/target-bigquery

› tap-exchangeratesapi --config sample_config/tap-exchange-rate-api.json | ^
target-bigquery --config  sample_config/target-config.json > sample_config/state.json

"^" on a Windows machine indicates a new line. On a Mac, use "\".
If you're using a different Tap, substitute tap-exchangeratesapi in the final command above to the command used to run your Tap.

Step 5: Set up Partitioning and Clustering

Partitioning

A partitioned table is a special table that is divided into segments, called partitions, that make it easier to manage and query your data. By dividing a large table into smaller partitions, you can:

improve query performance,
control costs by reducing the number of bytes read by a query.

You can partition BigQuery tables by:

Ingestion time: Tables are partitioned based on the data's ingestion (load) time or arrival time.
Date/timestamp/datetime: Tables are partitioned based on a TIMESTAMP, DATE, or DATETIME column.
Integer range: Tables are partitioned based on an integer column.

Clustering

When you create a clustered table in BigQuery, the table data is automatically organized based on the contents of one or more columns in the table’s schema.
The columns you specify are used to colocate related data.
When you cluster a table using multiple columns, the order of columns you specify is important. The order of the specified columns determines the sort order of the data.
Clustering can improve the performance of certain types of queries such as queries that use filter clauses and queries that aggregate data.
You can cluster up to 4 columns in a table

Learn more about BigQuery partitioned and clustered tables:

https://cloud.google.com/bigquery/docs/partitioned-tables

https://cloud.google.com/bigquery/docs/clustered-tables

https://medium.com/google-cloud/bigquery-optimized-cluster-your-tables-65e2f684594b

https://medium.com/analytics-vidhya/bigquery-partitioning-clustering-9f84fc201e61

Setting up partitioning and clustering

Example 1: tap-recharge data

This is not a follow-along example. Additional tap configuration would be required to run it. This example is just for illustration purposes.

If we were to load tap-recharge charges table into BigQuery, we could partition it by date.

For clustering, we can selected:

foreign keys and
columns likely to appear in WHERE and GROUP BY statements

To configure partitioning and clustering in BigQuery destination tables, we create target-tables-config.json:

{
    "streams": {
      "charges": {
        "partition_field": "updated_at",
        "cluster_fields": ["type", "status", "customer_id", "transaction_id"]
      }
    }
}

We can verify in BigQuery web UI that partitioning and clustering worked:

Example 2: tap-exchangeratesapi data

You can follow along and try this example on your own. We will continue where we left off in Step 4: Install and Run above.

Take a look at our tap-exchangeratesapi data. We have:

dates
datetimes
floats which show exchange rates

In our tap-exchangeratesapi example, no columns are good candidates for clustering.

You can only set up partitioning.

Create your target-tables-config.json with partitioning configuration. Leave cluster fields blank:

{
    "streams": {
      "exchange_rate": {
        "partition_field": "date",
        "cluster_fields": []
      }
}}

Clear you state.json, so it's an empty JSON {}, because we want to load all data again. Skip this step, if you didn't previously load this data in Step 4 above.
Delete your BigQuery destination table exchangeratesapi, because we want to re-load it again from scratch. Skip this step, if you didn't previously load this data in Step 4 above.
Load data data into BigQuery, while also configuring target tables:

› tap-exchangeratesapi --config sample_config/tap-exchange-rate-api.json | ^
target-bigquery --config  sample_config/target-config.json ^
-t sample_config/target-tables-config.json > sample_config/state.json

"^" on a Windows machine indicates a new line. On a Mac, use "\".

Verify in BigQuery web UI that partitioning worked:

License

socketbox/target-bigquery

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