As businesses around the world consistently lean on data-driven insights, organizations are ramping up their arsenal of analytics tools. data build tool(dbt), an open-source platform that transforms raw data into ready-for-analysis datasets, has gained considerable traction for its robust capabilities. One of its standout features is the use of dbt packages, which offer reusable chunks of code, be it SQL queries, macros, or models, to streamline your data workflow.
With over 150 packages available on the dbt Hub, these pre-built modules add a layer of functionality that goes beyond just data modeling. They play a critical role in data testing, quality assurance, and much more. As data build tool continues to evolve, these packages are becoming increasingly sophisticated, serving as indispensable tools that optimize your workflow and save you a significant amount of time.
What Are dbt Packages?
The data build tool packages are pre-built sets of SQL macros and materializations designed to extend dbt's core capabilities. Unlike isolated SQL queries, these packages offer reusable solutions for commonly faced analytical problems. For example, they can standardize the format of AdWords or Snowplow data across your organization, or provide pre-written audit queries for tracking data lineage and quality.
Each data build tool package is an autonomous project with its own set of models and macros. Once a package is added to your data build tool project, its elements are fully integrated. That means you can execute the models within the package using the dbt run command, and you can also refer to these models or invoke macros from the package in your own data build tool project.
The data build tool package hub addresses a range of functionalities and can serve different purposes. They may include models to manage permissions in databases like Redshift, or macros for working with data loaded by specific tools such as Stitch.
Packages can also serve governance requirements, as they enable standardized data transformation and validation techniques. This ensures data consistency and quality, which are essential for compliance and auditing.
Top 3 dbt Packages
Here are the top 3 data build tool packages you should be aware of:
1. dbt_utils Package
dbt Utils is the all-in-one utility package for the data build tool. Often considered the first go-to after learning basic data build tool modeling, it provides an array of functionalities that simplify and enhance your workflow.
Why It's Crucial: This package is universally recognized for its robust set of features that address a wide range of common data transformation and testing needs.
Key Features: Simple tests that allow you to quickly test your data for recency, uniqueness, and more. It has functionalities like generating GROUP BY SQL statements, which can save you time on writing repetitive code. Offers Jinja macros for tasks such as formatting date strings.
Why You Should Care: If you're overseeing data governance, dbt utils package can significantly improve your efficiency by automating routine tasks and checks. It helps ensure data quality and consistency, which are cornerstones in any data governance strategy.
2. dbt_artifacts Package
The dbt Artifacts package is specifically designed for analyzing your data build tool operations. It generates pre-modeled data that allows you to dig into the details of your dbt project, from model run times to the number of snapshots you have.
Why It's Crucial: As you scale your dbt usage, you'll inevitably have questions about performance, efficiency, and resource allocation. dbt Artifacts offers a structured way to answer these questions, using data to inform your workflow optimization.
Key Features: This package generates fact tables for various dbt features like snapshot, test, seed, and model invocations. It also provides dimensions offering a comprehensive overview of all existing models, tests, and snapshots. Essentially, it gives you a meta-level view of your dbt activities.
Why You Should Care: If you're responsible for data governance and optimization, this package gives you the metrics needed for informed decision-making. It's particularly useful for those who aim to improve efficiency, allocate resources wisely, and ensure quality in their dbt projects.
3. dbt osmosis Package
dbt Osmosis is a tool that streamlines the management of your dbt models. It's essentially a graphical interface for interacting with your YAML configurations, compiling your Jinja code into SQL, and even comparing differences between your models.
Why It's a top dbt package?: Let's be honest, managing dbt models can become convoluted as your project scales. Osmosis eases this by providing a workbench where you can paste, compile, and test your model code. It automates naming conventions and allows you to copy model setups, removing a lot of manual grunt work.
Key Features: This dbt package include real-time SQL querying using Jinja code, defining and applying naming conventions across models, and providing SQL diffs for version comparison.
Why You Should Care: If you're tasked with overseeing data models and transformations, dbt Osmosis can be a time-saver and a complexity-reducer. It's like having an extra set of hands helping you keep everything in order.
Importance of dbt Packages
Efficiency: Right off the bat, dbt packages cut down your development time. Instead of coding each SQL macro or materialization from ground zero, these pre-built solutions give you a fast track.
Standardization: Uniformity is key in data governance. Dbt packages ensure that whether you're working on Project A or Project Z, the logic remains the same. This is crucial for maintaining data integrity across the board.
Quality & Consistency: Dbt packages often come battle-tested. They include built-in tests that uphold data quality, making your life a whole lot easier when it comes to ensuring consistent and reliable data.
Scalability: As your data needs grow, so does the complexity. Dbt packages offer a scalable solution, providing a modular framework that easily integrates with your expanding data pipeline.
Operational Insight: With packages like dbt Artifacts, you get a backstage pass to your dbt operations. This level of insight is gold for resource allocation and performance optimization.
Governance and Compliance: Dbt packages are your ally in meeting governance & compliance standards. They offer tried-and-true methods that align with industry best practices, streamlining the auditing process.
Versioning: Version control allows you to keep track of changes, making it simpler to roll back or update as needed. This is especially useful in larger teams where multiple people might be working on the same package.
Reusability: Once a package is developed, it can be reused across multiple projects. You don’t need to reinvent the wheel every time you have a similar requirement in a different project. This reusability not only saves time but also ensures that you’re applying consistent logic and standards across projects.
While the dbt ecosystem offers an extensive range of packages, the ones we've highlighted are foundational tools for anyone serious about data governance. These packages are proving to be essential building blocks for optimizing efficiency and robustness in your dbt projects.
And remember, the open-source nature of dbt means you're not limited to what's already out there—you can contribute to existing packages or even create your own to fill gaps. Whether you're a data engineer, analyst, or overseeing data governance, these dbt packages are key to leveraging your data assets to their fullest potential.
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“[I like] The easy to use interface and the speed of finding the relevant assets that you're looking for in your database. I also really enjoy the score given to each table, [which] lets you prioritize the results of your queries by how often certain data is used.” - Michal P., Head of Data