In this blog, we take a deep dive into three best-of-breed platforms within a modern data stack—BigQuery, dbt, and Looker—and explain what benefits this data stack can bring to your organization, and why you should consider making the switch.

As a data professional, it can be daunting—exhausting even—to keep tabs on every tool that currently exists in what is commonly referred to as the modern data stack. With new tools on the market every day, it can be a full-time job just to keep up to date with functionality, changes to platforms, new methods, best practices, and everything in between.

At the end of the day, most organizations are looking for the simplest solutions that will solve their current and future challenges with data. Fortunately, there is a best-in-breed modern data stack that rises to this challenge: BigQuery, dbt, and Looker.

Illustration representing an example of a modern data stack implementation with BigQuery, dbt, and Looker.

Example of a modern data stack implementation with BigQuery, dbt, and Looker.

BigQuery, dbt, and Looker are impressive on their own, but their real power is unleashed when combined. Before going into the benefits of a combined data stack, let’s look at what each platform brings to the table.

The Strengths and Benefits of BigQuery as a Data Warehouse 

BigQuery sets itself apart as a data warehouse in three ways: effortless serverless infrastructure, affordability, and machine learning capabilities.

  • Serverless Infrastructure: BigQuery brings a state-of-the-art platform that is a truly serverless offering. Unlike many other solutions, resource provisioning is handled automatically behind the scenes. There is no need to configure computational scalability; Google manages all resources for you and bills you on how many bytes are scanned during queries. You have access to unlimited resources with petabyte scalability—you never have to worry about meeting demand at scale on BigQuery.
Learn more about the “Difference Between Managed Services and Serverless Technologies For Data and Analytics
Illustration of Fivetran’s comparison of data platform architectures.

Fivetran’s comparison of data platform architectures. Source: Fivetran

  • Affordability: BigQuery takes significant steps to ensure their product is optimized for affordability. No matter how you load data into BigQuery, you are not charged any computational expense for loading the data. Other cloud platforms will charge you for this activity, which adds up in additional costs over time. Another feature of BigQuery is proactive storage cost optimization. If there is data in BigQuery that hasn’t been altered in over 90 days, they move it into a long-term storage tier that is half the price of regular storage. On other cloud platforms, you would need to build this archival process yourself to realize the same savings.

    Lastly, BigQuery caches query results for 24 hours after running a query. This means that BigQuery stores the results of a query as a temp table, and if the underlying data has not changed, you will not be charged for running the same query twice in that timeframe.

  • Machine Learning Capabilities: BigQuery is the market-leading cloud data warehouse platform when it comes to machine learning capabilities. BigQuery ML offers an unprecedented experience of creating machine learning models using standard SQL queries directly in BigQuery. It supports everything from basic regression models to advanced models like Boosted Tree Classifiers and Neural Networks. It also has excellent integrations with other Google Cloud Platform (GCP) tools like Vertex AI. BigQuery is feature-rich and affordable, while managing all the storage and compute resources you need to power your analytics.
Learn more about “The Different Types of Machine Learning You Can Deploy and How to Use Them

The Strengths and Benefits of dbt as a Data Transformation Tool

dbt provides an advanced development framework to transform data on top of cloud platforms. dbt’s transformations are centered around SQL which has tightly integrated many data teams. dbt Cloud comes with many features including version control integrations, an intuitive development IDE, a centralized documentation hub, orchestration, and advanced testing capabilities.

  • Version Control Integrations: dbt Cloud provides an integration with version control tools like GitHub, GitLab, and Azure DevOps to ensure that your development process remains stable while teams work in tandem.
Screen shot of dbt Cloud’s guided Git interface which provides structure to commit changes, pull down changes, open pull requests, and make new branches.

dbt Cloud’s guided Git interface provides structure to commit changes, pull down changes, open pull requests, and make new branches.

  • Intuitive Development IDE: dbt has an extremely user-friendly development IDE so that individuals on your data team can add immediate value developing transformation logic.
Screen shot of dbt Cloud’s development IDE which makes it simple to preview, compile, and view lineage all in one spot.

dbt Cloud’s development IDE makes it simple to preview, compile, and view lineage all in one spot.

  • Centralized Documentation Hub: dbt is feature-rich in documentation capabilities. It automatically collects lineage on your projects and allows many customizations for defining business logic. All this information is compiled into one spot.
Gif illustrating example of lineage in the dbt documentation hub.

Example of lineage in the dbt documentation hub.

  • Orchestration: dbt Cloud’s jobs can be triggered from a variety of mechanisms to transform your data. This can either be used as a standalone feature or part of a larger orchestration pipeline if you are using a tool like Cloud Composer, Prefect, or Dagster.
Screen shot illustrating how dbt Cloud allows you to customize steps for your transformation workflows in a simple way.

dbt Cloud allows you to customize steps for your transformation workflows in a simple way.

  • Advanced Testing Capabilities: dbt comes out of the box with Generic, Singular, and Source freshness tests to ensure stability in your data pipeline.
Screen shot illustrating generic tests in dbt Cloud are defined using yaml files within the project.

Generic tests in dbt Cloud are defined using yaml files within the project.

Learn more about “What dbt is and What it Can Do for Your Data Pipeline
Graphic illustrating example architecture you could deploy using dbt.

Example architecture you could deploy using dbt.

The Strengths and Benefits of Looker as a Business Intelligence (BI) and Reporting Platform

Looker brings best-in-class data governance, API-driven development, and embedded analytics capabilities to enable reporting and dashboarding within the modern data stack—all this and more while maintaining unparalleled security models.



  • Data Governance: Looker allows developers to provide a top-down single source of governed and curated data, while still allowing end users to create, explore, analyze, and share real-time business analytics themselves. By using DRY coding principles and HUB and SPOKE methodologies, code can be created and then re-used, built upon, and modified downstream. If something changes upstream, it automatically flows downstream ensuring everyone is using the latest and greatest. No need to make changes throughout.
Learn more about how to “Jumpstart Your Data Governance Strategy with Looker
  • API-Driven Development: Looker allows end users to interact with their data and act on information gained through built-in and custom integrations to numerous third-party platforms. Once set up, users can start a Marketo campaign, send to Slack, kick off a business process, etc., all with the click of a button. Developers can automate coding changes, as well as perform administrative tasks, data tests, production changes, and so much more.
  • Embedded Analytics Capabilities: You can embed anything from visualizations, data, and entire dashboards within Looker. Users can fully interact with the data and dashboards, all without leaving your website or portal. Create custom themes, data events, custom end points, and much more with the Looker APIs and Embed SDK.

What Makes BigQuery, dbt, and Looker a Best-in-Breed Data Stack?

While each of these tools on their own provide significant benefits, the three work together in a multitude of ways to provide scalability, performance, reusability, and efficiency across the board.

The Integration Between BigQuery and dbt

Alone, BigQuery is an incredible tool thanks to its advanced features and petabyte scalability. However, as many teams grow, it becomes extremely difficult to manage transformations in BigQuery alone whether you use views, scheduled queries, or an alternative solution. Having an advanced transformation framework that accelerates your development, documentation, testing, and deployment processes should be a priority, and combining dbt with BigQuery fills this gap and unleashes synergies as they integrate flawlessly together.

In addition to the features available through dbt, you also can take advantage of unique open-source packages designed specifically for BigQuery and dbt. For example, “dbt_ml” is an open-source package that enables teams to automate training, auditing, and using BigQuery Models.

Since dbt micro focuses on transformations on top of BigQuery, it allows you to still choose the best tools to complete your data stack and address your specific needs. A large reason many of our customers love dbt is because they never have to choose a one-size fits all approach.

The Integration Between BigQuery and Looker

BigQuery with Looker combines the scalability and flexibility of each tool to allow users to leverage all sizes and types of data—nested, flattened, big, small, and everything in between— your organization has now or will in the future. As your data use cases grow, so do these tools.

Additionally, you can use BigQuery’s cost estimator within Looker to monitor spend. As users bring in data from all over the known (and unknown) data-verse, use dbt and BigQuery to pull that data into a central location and let Looker write the SQL needed to analyze it—no need for end users to know how to account for any special type of data or write SQL. Looker sits on top of BigQuery taking full advantage of everything it has to offer as end users are analyzing their data in a cost-effective, managed, and curated environment.

The Integration Between Looker and dbt

Looker with dbt takes data governance and transformation to a whole new level. You can use dbt to model business definitions, aggregations, heavy computational metrics, and you can use LookML to put the finishing touches on your data, prototype data model changes, define metrics or definitions that require end user interaction, and implement joins and explores (how users interact with the data).

While these tools are more than enough to get you started, when you’re ready to take it to the next level, put a third-party tool (like Spectacles) on top of Looker and dbt to let you know how, where, and what will be affected in Looker each time you make a change in dbt. Now, instead of your end users sounding the alarm that things are broken (or worse, incorrect and nobody knows), developers know exactly what will happen when they implement. The point is that each of these tools are ready to take your implementation to the next level as your data needs mature.

Talk to an expert about your modern data stack needs.

Why You Should Consider a BigQuery, dbt, Looker Stack

Organizations want to make sure the modern data stack can integrate with their current data sources, is future proof, cost-effective, and can handle all their main and edge cases of data and analytics. Not only do all three of these tools integrate and play nicely with each other and the other tools in your data arsenal, but when used in concert, they allow you to control every aspect of your data including security, data governance, and integration with third-part tools downstream of even Looker.

And while all three of these tools might not be under the same flag, getting them to work together is not a huge undertaking or something that requires a ton of pre-work.

Futureproof data stack: All these tools were born in the cloud for the cloud:

  • There is no dependency on downloads or outdated architecture.
  • They were specifically built to grow and adapt to the ever-changing data ecosphere.
  • Every update brings new functionality, new possibilities, and enhanced security.
  • As your use cases grow, so does this data stack—period.

Cost-effective data stack: Every data stack has implementation costs, but there are other costs you need to consider, specifically around operating costs including:

  • Does this stack automatically cut costs on archived data?
  • Does this stack allow you to set query limits from a cost perspective?
  • Does this stack give you generous free tiers?
  • Does this stack allow you to bring more data in to answer questions without charging you?

Utilizing all the features in a BigQuery, dbt, and Looker stack allows you to set limits, use archiving features to automatically reduce cost, and let you pull in more data for free as use cases evolve—as well, by default—give you generous free tiers that don’t start charging until you reach them.

Data stack that can handle all main and edge cases of data and analytics: There isn’t much that these three tools can’t do together. If there is, odds are there is another tool in their collective ecosystem that can either quickly bolt on or can be custom-built using their development framework, including:

  • Ingestion
  • Streaming data
  • Embedding analytics
  • Dynamic coding
  • Custom integration

We’ve only scratched the surface as far as what this modern data stack can do, and how it can help your business grow. BigQuery, dbt, and Looker allow you and your organization to take advantage of best-in-breed technology without sacrificing ease of use and is already set up to grow as your organization does.

Josh Goldner Josh is Analytic8’s Google Practice Director and is also a certified LookML Developer. Josh implements modern analytics solutions to help his clients get more value from their data. Josh is an avid outdoorsman and balances his professional work with hunting and teaching his coworkers how to fish.
John Barcheski John is a Data Engineering Consultant based out of our Madison office. He leads Analytics8’s dbt Practice and is passionate about accelerating the productivity of data teams through using dbt. John specializes in data ingestion, modeling , and transformation solutions. In his free time, John enjoys spending time with family, traveling, and fishing.
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