Continuous data integration in BigQuery

This document describes principles and techniques for implementing a repeatable workflow that will help you integrate data changes into your BigQuery-based data warehouse (DWH). These changes might include new datasets, new data sources, or updates and changes to existing datasets. The document also describes a reference architecture for this task.

The audience for this document is software and data architects and data engineers who use BigQuery as a DWH. The document assumes that you're familiar with basic concepts of CI/CD or similar application lifecycle management practices.


Continuous integration and continuous delivery (CI/CD) has become an essential technique in the software development lifecycle. Adopting the principles of CI/CD lets teams deliver better software with fewer issues than by integrating features and deploying them manually. CI/CD can also be part of a strategy for data management when you modernize your data warehousing.

However, when you're working with a DWH like BigQuery, there are differences in how you implement CI/CD compared to implementing CI/CD in source code. These differences are in part because data warehousing is an inherently stateful system for managing the underlying data.

This document provides the following information:

  • Techniques for implementing a continuous integration (CI) strategy in BigQuery.
  • Guidance and methods that help you avoid pitfalls.
  • Suggestions for BigQuery features that help with CI in BigQuery.

This document focuses on CI, because integration has more data-specific considerations for a data warehousing team than continuous delivery (CD) does.

When to use CI for a BigQuery DWH

In this document, data integration is a task that's usually performed by the DWH team, which includes incorporating new data into the DWH. This task might involve incorporating a new data source into the DWH, or changing the structure of a table that's already inside the DWH.

Integrating new data into the DWH is a task similar to integrating a new feature into existing software. It might introduce bugs and negatively affect the end-user experience. When you integrate data into BigQuery, downstream consumers of the data (for example, applications, BI dashboards, and individual users) might experience issues due to schema mismatches. Or the consumers might use incorrect data that doesn't reflect the data from the original data source.

CI for DWH is useful when you want to do the following:

  • Describe key points in CI for a DWH system.
  • Design and implement a CI strategy for your BigQuery environment.
  • Learn how to use BigQuery features for implementing CI.

This guide doesn't describe how to manage CI for non-DWH products, including data products like Dataflow and Bigtable.

Example scenario

Example Company is a large retail company that maintains its DWH in BigQuery. In the upcoming year, the company wants to integrate new data sources into its DWH from companies that were recently acquired by Example Company. The new data sources to be integrated have different schemas. However, the DWH must keep its existing schema and must provide strong data consistency and data completeness so that the downstream consumers of data aren't negatively affected.

Currently, the Example Company DWH team performs data integration. The integration relies on having the existing data sources in a predefined schema. This workflow involves legacy data import processes, acquired databases, and application services.

To update their data integration processes to accommodate the new data sources, the DWH team must redesign their approach to data integration to comply with the requirements that were noted earlier, such as strong data consistency. The team must implement the changes in an isolated fashion so that the data changes can be tested and measured before the data is made available to downstream consumers.

After the DWH team adopts the new workflow, the team has a repeatable process. Each developer can create an isolated development environment that's based on production data. Using these isolated environments, developers can then make changes, test them, have them reviewed, and deliver the required changes to the production environment. If the changes cause bugs or unforeseen issues, the changes can be easily rolled back.

What continuous integration means for a DWH

Continuous integration (CI) is a set of practices that lets development teams shorten development cycles and find issues in the code faster than with manual systems. The main benefit of adopting a CI approach is the ability to develop rapidly, reducing the risks of interference between developers. This goal is achieved by making sure that the process is repeatable, while allowing each developer to work in isolation from other developers.

These principles also apply when an organization must integrate data into a DWH, with a few differences. In the context of typical software development, CI isolates changes to source code, which is stateless. In the context of CI in data, CI integrates data into a DWH system. However, data is stateful by definition. This difference has implications for how CI applies to DWH scenarios, as described in this document.

Additional scenarios that aren't covered in this document

Although this document focuses on isolating development changes from the production environment, the document doesn't cover the following aspects of data integration:

  • Data testing: Are you able to verify that the data you have conforms to business requirements? Is the data reliable to serve as the source of truth? To increase your confidence level in the data that you're serving from your DWH, it's important to test the data. To test, you can run a set of queries, asserting that the data isn't missing values or asserting that it contains "bad" values.
  • Data lineage: Are you able to see any table in its context? For example, can you see where the data was gathered from, and which datasets were pre-computed in order to generate the table? In modern DWH architectures, data is split into many systems that use different, specialized data structures. These include relational databases, NoSQL databases, and external data sources. To fully understand the data that you have, you must keep track of that data. You must also understand how the data was generated and from where it was generated.

These topics are out of scope for this guide. However, it will benefit your data strategy to plan for these topics when you're designing a workflow for your team.

Typical setup of BigQuery as a DWH

The following diagram illustrates a typical DWH design for BigQuery. It shows how data from external sources is ingested into the DWH, and how consumers consume data from the DWH.

Three databases outside of Google Cloud are data sources. Their data
moves into storage in a staging area. The data then moves into
BigQuery tables, which are the source for
BigQuery views. Consumers like Looker, App Engine,
Vertex AI notebooks, and human users consume the data using the views.

The data starts at the data sources, where the data is in conventional transactional or low-latency databases such as OLTP SQL databases and NoSQL databases. A scheduled process copies the data into a staging area.

The data is stored temporarily in the staging area. If necessary, the data is transformed to fit an analytical system before it's loaded into the DWH tables. (In this diagram, the staging area is inside Google Cloud, but it doesn't have to be.) Transformations might include denormalization, enriching certain datasets, or handling malformed entries (for example, entries with missing values).

From the staging area, the new data is loaded into the DWH tables. The tables might be organized in different ways depending on the design of the DWH, and are usually referred to as core tables. Some examples of table design paradigms include the star schema paradigm, the denormalized paradigm, and multi-tier aggregates.

Regardless of the table design, these tables save data over time. The tables must adhere to the schema, and they're presumed to hold the source of truth for all analytical purposes. This role for the tables means that data in these tables must be complete, be consistent, and adhere to the predefined schemas.

These tables don't serve data directly to consumers. Instead, the data is served through an access layer, which is designed to encapsulate business logic that must be applied to the underlying data. Examples of this type of business logic are calculating a metric for each record, or filtering and grouping the data.

The consumers of the data can connect to and read data from the DWH access layer. These data consumers might include systems like the following:

  • Business intelligence (BI) dashboards
  • Data science notebooks
  • Operational systems that rely on data calculated in the DWH
  • Human users for ad-hoc queries

The data consumers rely heavily on the DWH for providing consistent schemas and on the business logic that the DWH encapsulates. These schemas and business logic can be considered as the service level agreements (SLAs) of the DWH platform. Any change to the business logic, to the schema, or to the completeness of data might have large implications downstream. Given the ever-changing nature of modern data platforms, the DWH team might be required to make those changes while nevertheless strictly adhering to the SLAs. In order for the team to meet these SLAs and also keep the DWH up to date, they need a workflow that allows data integration while minimizing the friction that these changes might create.

Assets for continuous integration in a DWH

As with any other development or IT team, the DWH team must maintain assets that are essential to their responsibilities. These assets can typically be divided into the following categories:

  • The codebase for data pipelines: These assets usually consist of source code in a high-level programming language like Python or Java. For those types of assets, the CI/CD processes are built by using tools like Git and Jenkins, or by using Google Cloud solutions like Cloud Source Repositories and Cloud Build.

  • SQL scripts: These assets describe the structure and the business logic that's encapsulated inside the DWH. Within this category, the assets can be further divided into the following subcategories:

    • Data definition language (DDL): These assets are used for defining the schema of tables and views.
    • Data manipulation language (DML): These assets are used for manipulating data inside a table. DML commands are also used to create new tables based on existing tables.
    • Data control language (DCL): These assets are used for controlling permissions and access to tables. Within BigQuery, you can control access by using SQL and the bq command-line tool or by using the BigQuery REST API. However, we recommend that you use IAM.

These assets, and others like Terraform scripts that are used to build components, are maintained inside code repositories. Tools like Dataform can help you construct a CI/CD pipeline that validates your SQL scripts and checks predefined validation rules on tables that are created by DDL scripts. These tools let you apply compilation and testing processes for SQL, which in most contexts doesn't have a natural testing environment.

In addition to the assets that are associated with tools and processes, the main asset for a DWH team is the data. Data isn't trackable by using asset-tracking systems like Git, which is designed to track source code. This document addresses the issues that are associated with tracking data.

Issues with integrating data

Because of the potential complexity of table relationships inside a DWH (for example, in one of the table design paradigms mentioned earlier), keeping the state of production data isolated from a testing environment is a challenge. Standard practices in software development can't be applied to the data integration scenario.

The following table summarizes the differences between the practices for integrating code and the practices for integrating data.

  Integrating code Integrating data
Local development Source code is easily cloneable due to its relatively small size. Generally the code fits most end-user machines (excluding cases of monorepos, which have other solutions). Most tables in a DWH cannot fit on a development machine due to their size.
Central testing Different states of the source code are cloned into a central system (a CI server) to undergo automated testing. Having different states of the code lets you compare results between a stable version and a development version. Creating different states of the data in an isolated environment isn't straightforward. Moving data outside the DWH is a resource-intensive and time-consuming operation. It isn't practical to do as frequently as needed for testing.
Past versions During the process of releasing new versions of software, you can track past versions. If you detect a problem in a new release, you can roll back to a safe version. Taking backups of tables inside the DWH is a standard practice in case you must roll back. However, you must make sure that all affected tables are rolled back to the same point in time. That way, related tables are consistent with one another.

Integrate data into BigQuery tables

BigQuery has two features that can help you design a workflow for data integration: table snapshots and table clones. You can combine these features to achieve a workflow that gives you a cost-effective development environment. Developers can manipulate data and its structure in isolation from the production environment, and they can roll back a change if necessary.

A BigQuery table snapshot is a read-only representation of a table (called the base table) at a given moment in time. Similarly, A BigQuery table clone is a read-write representation of a table at a given moment in time. In both cases, storage costs are minimized because only the differences from the base table are stored. Table clones start to incur costs when the base table changes or when the table clones change. Because table snapshots are read-only, they incur costs only when the base table changes.

For more information about the pricing of table snapshots and table clones, see Introduction to table snapshots and Introduction to table clones.

You can take table snapshots and table clones using the BigQuery time travel feature (up to seven days in the past). This feature lets you capture snapshots and clones of multiple tables at the same point in time, which makes your working environment and snapshots consistent with one another. Using this feature can be helpful for tables that are updated frequently.

How to use table clones and table snapshots to allow isolation

To illustrate the integration workflow for CI in a DWH, imagine the following scenario. You're given a task of integrating a new dataset into the DWH. The task might be to create new DWH tables, to update existing tables, to change the structure of tables, or any combination of these tasks. The workflow might look like the following sequence:

  1. You identify the tables that might be affected by the changes and additional tables that you might want to check.
  2. You create a new BigQuery dataset to contain the assets for this change. This dataset helps isolate the changes and separates this task from other tasks that other team members work on. The dataset must be in the same region as the source dataset. However, the project can be separated from the production project to help with your organization's security and billing requirements.
  3. For each of the tables, you create both a clone and a snapshot in the new dataset, potentially for the same point in time. This approach offers the following benefits:

    • The table clone can act as a working copy where you can make changes freely without affecting the production table. You can create multiple table clones of the same base table in order to test different integration paths at the same time, with minimal overhead.
    • The snapshot can act as a restore and reference point, a point where the data is known to have worked before any change took place. Having this snapshot lets you perform a rollback in case an issue is detected later in the process.
  4. You use the table clones to implement the changes that are required for the tables. This action results in an updated version of the table clones, which you can test in an isolated dataset.

  5. Optionally, at the end of the implementation phase, you can present a dataset that can be used for the following tasks:

    • Unit testing with a validation tool like Dataform. Unit tests are self-contained, which means that the asset is tested in isolation. In this case, the asset is the table in BigQuery. Unit tests can check for null values, can verify that all strings meet length requirements, and can make sure that certain aggregates produce useful results. Unit tests can include any confidence test that makes sure that the table maintains the organization's business rules.
    • Integration testing with downstream consumers.
    • Peer review.

    This workflow lets you test with production data, without affecting the downstream consumers.

  6. Before you merge the new data into BigQuery, you can create another snapshot. This snapshot is useful as another rollback option in case the data in the base table has changed.

    The process of merging the changes depends on the process that your organization wants to adopt and on what changes are required. For example, for a change in the SQL scripts, the new dataset might be accompanied by a pull request to the standard codebase. If the change is limited to a change in the data within a given table, you could just copy data using standard methods of BigQuery.

You can use a script of stored procedures to encapsulate and automate the steps for creating a dataset and creating the clones and snapshots. Automating these tasks reduces risk of human error. For an example of a script that can help automate the processes, see the CI for Data in BigQuery CLI utility GitHub repository.

Benefits of using table clones and table snapshots

When you use the workflow described in the preceding section, your developers can work in isolation and in parallel, without interfering with their colleagues. Developers can test and review changes, and if there's an issue, roll back the changes. Because you're working with table snapshots and not with full tables, you can minimize costs and storage compared to working with full tables.

This section provides more detail about how table snapshots and table clones let developers achieve this workflow. The following diagram shows how table snapshots and table clones relate to the data in the production dataset.

A production dataset contains 9 tables. A second dataset named "Dev Dataset 1"
contains snapshots of tables 2 and 3 and clones of tables 2 and 3. A third
dataset named "Dev Dataset 2" contains snapshots of tables 3 and 4 and clones
of tables 3 and 4.

In the diagram, the production dataset contains all the tables that are being used in production. Every developer can create a dataset for their own development environment. The diagram shows two developer datasets, which are labeled Dev Dataset 1 and Dev Dataset 2. By using these developer datasets, developers can work simultaneously on the same tables without interfering with one another.

After developers have created a dataset, they can create clones and snapshots of the tables they are working on. The clones and snapshots represent the data at a particular point in time. At this point, developers are free to change the table clones, because changes aren't visible on the base table.

A developer can review the table clones, compare them to the snapshot, and test them for compatibility with downstream consumers. Other developers are able to work with other clones and snapshots, without interference, and without creating too many resource-consuming copies of the base table.

Developers can merge changes into the base table while keeping the snapshot safe to have as a rollback option, if needed. This process can also be repeated for different environments, like development, test, and production.

Alternatives to table clones and table snapshots

There are alternatives to using table clones and table snapshots that let you achieve a similar result. These alternative methods are typically used differently than clones and snapshots. It's important to understand the differences between these methods and where you might prefer one method over the other.

Copy entire tables into a different dataset

One alternative method is to use a different dataset and to copy the tables into that dataset. Using this method is similar to using table clones and snapshots, but you copy the entire set of tables. Depending on the sizes of the data being copied, the storage costs might be high. Some organizations used this method before table clones became available in BigQuery. However, this method doesn't present any advantages over using clones and snapshots.

Export and import tables to Cloud Storage

Another alternative method is to move the data through Cloud Storage. This method is also similar to using table clones and table snapshots. However, it includes the extra step of exporting the data to a Cloud Storage bucket. One advantage of this method is that it gives you an extra backup of your data. You might choose this method if you want a backup for disaster recovery or hybrid solutions.

Use Analytics Hub

Analytics Hub lets you share datasets both outside and inside the organization in a way that's designed to be secure. It offers many features that let you publish datasets to provide subscribers with controlled, read-only access to those datasets. However, even though you can use Analytics Hub to expose datasets in order to implement changes, a developer still must create table clones in order to work with the tables.

Summary of DWH continuous integration options

The following table summarizes the differences, advantages, and potential disadvantages between the options for DWH continuous integration. (Analytics Hub offers a different feature set, and is therefore not measurable using the parameters listed in the table.)

  Costs Rollbacks Risks
Table snapshots and table clones Minimal. You pay only for the difference between the snapshot or clone and the base table. The snapshot acts as a backup to roll back to if necessary. You control the amount of risk. Snapshots can be taken at a point in time for all tables, which reduces inconsistencies even if there is a rollback.
Table copy Higher costs than using table snapshots and table clones. The entirety of the data is duplicated. To support rollbacks, you need multiple copies of the same table. Possible, but requires two copies of the table—one copy to serve as backup and one copy to work with and make changes to. Cloning is harder to do for a point in time. If a rollback is necessary, not all tables are taken from the same point in time.
Export and import Higher costs than using table snapshots and table clones. The data is duplicated. To support rollback, you need multiple copies of the same table. The exported data serves as a backup. Exported data is not a point-in-time export for multiple tables.

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