Category: bigquery

In today’s data-driven landscape, organizations often have multiple teams comprising domain experts who create their own unique products. However, enabling other teams to leverage these products efficiently requires a structured approach. Enter data mesh—a paradigm that decentralizes data ownership and processing. In this guide, we’ll explore how to implement data mesh using Google Cloud Platform (GCP) BigQuery, empowering teams to manage their data products seamlessly.

Setting up Domain-Specific GCP Projects

Begin by assigning a dedicated GCP project to each domain or business team. This ensures that teams have the autonomy to develop and manage their data products within their respective environments. By segregating projects based on domains, teams can focus on their specific requirements without interfering with others’ workflows.

Development and Promotion Workflow

Within their assigned GCP projects, domain teams develop their data products tailored to their expertise. These products undergo rigorous testing and refinement in the development environment. However, it’s crucial to avoid publishing directly from the development environment to prevent potential disruptions for subscribers. Frequent changes in the development phase can lead to compatibility issues and operational challenges for downstream users.

Promotion to Higher Environments

Once a data product is deemed ready for consumption, it’s promoted to higher environments, typically housed in different GCP projects. This transition ensures that only validated and stable versions of products are made available to subscribers. By segregating development and production environments, organizations can maintain data integrity and stability while minimizing disruptions to subscriber workflows.

Publishing Data Products

When promoting a data product to a higher environment, a team lead assumes the responsibility of publishing it. This involves orchestrating a seamless transition and ensuring that subscribers can access the updated version without interruptions.

Make sure Analytics Hub API is enabled for this.

Follow these steps to publish your product:

1. Navigate to GCP BigQuery and access Analytics Hub. Click on “Create Exchange.”
2. Depending on the nature of your product, provide the necessary details and proceed by clicking “Create Exchange.”
   
3. At this stage, you’ll have the option to configure permissions for administration, publishing, subscription, and viewing of the listing. You can either set these permissions now or configure them later.
   
4. Once the exchange is created, proceed to create a listing for it. A listing involves selecting the dataset you wish to publish. Currently, GCP BigQuery only allows choosing datasets only. And depends on what location you choose for your exchange you should be able to select datasets.
   
5. Provide the required Listing Details and Listing Contact Information corresponding to your data product. Once completed, you’ll be able to publish the dataset through the listing.
   
   

Subscribing to the Data Products (listings)

• Once the Data Product is published other users can search and subscribe to this listing. User would need access to create a new Dataset in the project they want to subscribe this new data products.

What Are Partition Columns?

In BigQuery, tables can be partitioned based on particular column. Partitioning involves dividing a large table into smaller, more manageable pieces or partitions. Each partition contains a subset of the data and is stored separately. The key idea behind partitioning is to allow BigQuery to scan and process only the partitions that are relevant to a query, rather than scanning the entire table. This can lead to dramatic improvements in query performance and cost savings.

Why Are Partition Columns Important?

1. Query Performance

Partitioning a table based on a meaningful column, such as a timestamp or date, can significantly speed up query execution. When you run a query that includes a filter condition on the partitioning column, BigQuery can prune irrelevant partitions, scanning only the data that meets the criteria. This reduces query execution time, especially when dealing with large datasets.

For example, if you have a daily time-series dataset and partition it by date, querying data for a specific date range becomes much faster because BigQuery only needs to scan the partitions corresponding to that range.

2. Cost Efficiency

Improved query performance isn’t the only benefit of partition columns. It also translates into cost savings. BigQuery charges you based on the amount of data processed during a query. By scanning fewer partitions, you reduce the amount of data processed, leading to lower query costs. This cost reduction can be substantial for organizations with large datasets and frequent queries.

Query to Find partitioned column for all GCP bigquery tables

And so, I was looking for a way to find partitioned column for each table in our project. So we can improve our query performance and cost.

Following query will help show you partitioned column for each Bigquery table in your GCP Project

SELECT
  table_catalog AS project_id,
  table_schema AS dataset,
  table_name,
  partition_column
FROM (
  SELECT
    table_catalog,
    table_schema,
    table_name,
    CASE
      WHEN is_partitioning_column = "YES" THEN column_name
    ELSE
    NULL
  END
    AS partition_column,
    ROW_NUMBER() OVER(PARTITION BY table_catalog, table_schema, table_name ORDER BY is_partitioning_column DESC) AS rnk
  FROM
    <your_project>.<your_region>.INFORMATION_SCHEMA.COLUMNS
  )a
WHERE
  a.rnk = 1