If you’re encountering slow data onboarding, inconsistent analytics, and complex development environments in your expanding organization, it’s time to consider how dbt Cloud's data mesh enabling-architecture can streamline your development processes, ensure uniform analytics, and adapt to diverse technical skills — simplifying and enhancing your data management strategy.

Making the move to dbt Cloud’s data mesh model is a strategic move for organizations grappling with the complexities of large-scale data management. While achieving a data mesh goes beyond merely implementing technology, the right technology can indeed accelerate your organization’s transformation.

One such technology — dbt mesh — addresses key operational challenges such as decentralized data handling, varied skill sets in data teams, and the need for effective, scalable analytics. By recognizing specific signs in your organization’s data management practices, you can make informed decisions about adopting dbt Cloud’s data mesh model.

Here are five signs to help you determine if a data mesh model with dbt Cloud is the right fit for your organization:

  1. You’ve moved beyond a single data team, and your data practitioners are spread throughout the business. ↵
  2. Your investment in self-service analytics capabilities is growing. ↵
  3. You are finding that you have different skill sets developing on your platform. ↵
  4. The increasing volume of your data is making simple tasks challenging. ↵
  5. Shadow pipelines are emerging in your organization. ↵

#1: You’ve moved beyond a single data team, and now your data practitioners are spread throughout the business.

In larger organizations, operating a single, centralized data team often becomes impractical. This approach typically leads to slow data onboarding and delayed responses to analytical requests from downstream teams. We also find that data modeling becomes rigid — and most importantly — when those who deeply understand the data domains and generation processes are removed from data management, trust in your business’ data diminishes.

A data mesh remedies this by embedding data practitioners directly within each major business line, enhancing domain expertise for data engineers. However, this integration can result in a lack of awareness of the work done by others in the organization, potentially leading to repetitive work, increased costs through the creation of duplicate data objects, and additional complexity.

In version 1.5, dbt Core introduced features to address these challenges. Data contracts and model versioning enable teams to serve data as efficiently as software engineers serve microservices. This approach ensures that embedded data teams can rely on the accuracy and stability of the data they receive from others, avoiding disruptions in their pipelines. Contracts and model versioning enable a new multi-project architecture in dbt Cloud that allows teams to concentrate on their specific areas without the need to navigate the entire, expansive data lineage.

#2: Your investment in self-service analytics capabilities is growing.

As your organization grows, the people who need to answer questions from data will be increasingly distant from the data generation and modeling process.

This distance introduces significant risks in self-service analytics. How does a user know what the exact definitions of their key measures are? What if one team calculates revenue differently from another? These data governance problems can be burdensome and tedious to work through at best, and at worst, create mistrust in the data.

The dbt Cloud semantic layer effectively addresses these challenges. It enables teams to define metrics just once, allowing business users across the organization to access these standardized metrics from their preferred tools (including Google Sheets!). This solution streamlines the analytics process and ensures consistent and trustworthy data usage across the organization.

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#3: You are finding that you have different skill sets developing on your platform.

dbt empowers data analysts with tools for developing data pipelines, similar to their engineer counterparts, by simplifying DDL and orchestration. However, as you grow your data platform with dbt, you might notice a variety of skill sets emerging within the same project. While manageable at a small scale, this diversity can pose challenges as the project advances. Analysts might struggle with advanced dbt features aimed at enhancing performance, implementing custom CI checks, and using tools like pre-commit hooks to prevent bad commits.

Transitioning to a dbt mesh model allows teams with varying technical abilities to develop in their own dedicated projects. This shift reduces development process challenges by segregating reporting model development and semantic layer definitions into separate projects for analyst teams to manage, while engineers focus on foundational projects for enterprise data modeling.

This approach utilizes the Cloud IDE’s user-friendly features for analyst development and the Cloud CLI for engineering projects. The Cloud CLI ensures that all team members work within the dbt Cloud-managed environment, eliminating the need to manage local environments and providing security benefits through user authentication and access control. This alignment not only streamlines development but also ensures compliance with your company’s data development standards.

#4: The increasing volume of your data is making simple tasks challenging

When an organization has dozens of data practitioners and thousands of models, things that were once simple – like onboarding new users or seeing if someone has built a table already — can become daunting, impractical tasks. Engineers often find themselves sifting through extensive documentation, struggling to grasp its relevance, which leads to a lack of confidence in their actions.

To address this challenge, the new dbt Explorer provides a fast and visually engaging way to navigate your data projects. This tool makes it simple to trace the origins of data and understand how to leverage it in your work. With clearer insights and improved understanding, data engineers can focus on the innovative and impactful aspects of data work, moving past the frustration caused by ambiguity and uncertainty.

#5: Shadow pipelines are emerging in your organization.

As data volumes grow, so does the need for analysis of that data. If your organization doesn’t have a well-defined data strategy or is currently operating a centralized data platform that fails to keep pace with analytics demands — “shadow pipelines” will likely have started to appear.

Typically, these are processes set up by analysts on their local machines, using a mashup of tools to serve some reporting need. Such pipelines lack any form of data governance or visibility, leaving upstream teams unaware of how their data is being utilized. This lack of oversight can spread throughout the organization, creating systemic issues.

To combat this, it’s crucial to create and support a common toolchain that offers analysts a governed and visible method to transform data within their domain, along with the necessary computational resources.

Through dbt Cloud’s multi-project features, you can stand up this common toolchain while decentralizing the place in which this code is stored and executed. Teams can now import other analysts’ work into their own, and your centralized team can have visibility into how data is being used by analyst teams. This strategy not only enhances data governance but also promotes collaborative and efficient use of data.

Moving Forward with dbt Mesh

Adopting dbt mesh architecture streamlines data processes across organizations, allowing for better collaboration and governance. With the latest enhancements in dbt Cloud, teams can effectively manage and analyze data, ensuring more efficient and insightful use across all departments. This practical shift marks a significant step forward in simplifying and optimizing your organization’s data strategy.

Get In Touch With a Data Expert

Tyler Rouze Tyler Rouze is a senior consultant at Analytics8 who specializes in helping clients modernize their data management practices and tools. He is an open-source contributor to the dbt-core project, loves learning about new technology and thinking about how to improve the analytics development workflow. He has experience across the data lifecycle having held roles as a Data Analyst, Engineer, and Architect. Outside of work, Tyler enjoys running, spending time with friends and family, and baking an occasional sourdough loaf.
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