Data plays an instrumental role in decision making for organizations. As the volume of data increases, so does the need to make it accessible to everyone within your organization to use. However, because there is a shortage of data engineers in the marketplace, for most organizations there isn’t enough time or resources available to curate data and make data analytics ready.
Disjointed sources, data quality issues, and inconsistent definitions for metrics and business attributes lead to confusion, redundant efforts, and poor information being distributed for decision making. Transforming your data allows you to integrate, clean, de-duplicate, restructure, filter, aggregate, and join your data—enabling your organization to develop valuable, trustworthy insights through analytics and reporting. There are many tools on the market to help you do this, but one in particular—dbt (data build tool)—simplifies and speeds up the process of transforming data and building data pipelines.
In this blog, we cover:
According to dbt, the tool is a development framework that combines modular SQL with software engineering best practices to make data transformation reliable, fast, and fun.
dbt (data build tool) makes data engineering activities accessible to people with data analyst skills to transform the data in the warehouse using simple select statements, effectively creating your entire transformation process with code. You can write custom business logic using SQL, automate data quality testing, deploy the code, and deliver trusted data with data documentation side-by-side with the code. This is more important today than ever due to the shortage of data engineering professionals in the marketplace. Anyone who knows SQL can now build production-grade data pipelines, reducing the barrier to entry that previously limited staffing capabilities for legacy technologies.
In short, dbt (data build tool) turns your data analysts into engineers and allows them to own the entire analytics engineering workflow.
With dbt, anyone who knows how to write SQL SELECT statements has the power to build models, write tests, and schedule jobs to produce reliable, actionable datasets for analytics. The tool acts as an orchestration layer on top of your data warehouse to improve and accelerate your data transformation and integration process. dbt works by pushing down your code—doing all the calculations at the database level—making the entire transformation process faster, more secure, and easier to maintain.
dbt (data build tool) is easy to use for anyone who knows SQL—you don’t need to have a high-powered data engineering skillset to build data pipelines anymore.
dbt’s ELT methodology brings increased agility and speed to iFit’s data pipeline. What would have taken months with traditional ETL tools, now takes weeks or days.
dbt (data build tool) has two core workflows: building data models and testing data models. It fits nicely into the modern data stack and is cloud agnostic—meaning it works within each of the major cloud ecosystems: Azure, GCP, and AWS.
With dbt, data analysts take ownership of the entire analytics engineering workflow from writing data transformation code all the way through to deployment and documentation—as well as to becoming better able to promote a data-driven culture within the organization. They can:
dbt enables data analysts to custom-write transformations through SQL SELECT statements. There is no need to write boilerplate code. This makes data transformation accessible for analysts that don’t have extensive experience in other programming languages.
The dbt Cloud UI offers an attractive interface for individuals of all ranges of experience to comfortably develop in.
Continuous integration means less time testing and quicker time to development, especially with dbt Cloud. You don’t need to push an entire repository when there are necessary changes to deploy, but rather just the components that change. You can test all the changes that have been made before deploying your code into production. dbt Cloud also has integration with GitHub for automation of your continuous integration pipelines, so you won’t need to manage your own orchestration, which simplifies the process.
While configuring a continuous integration job in the dbt Cloud UI, you can take advantage of dbt’s sleek slim UI feature and even use webhooks to run jobs automatically when a pull request is open.
dbt (data build tool) allows you to establish macros and integrate other functions outside of SQL’s capabilities for advanced use cases. Macros in Jinja are pieces of code that can be used multiple times. Instead of starting at the raw data with every analysis, analysts instead build up reusable data models that can be referenced in subsequent work.
Instead of repeating code to create a hashed surrogate key, create a dynamic macro with Jinja and SQL to consolidate the logic in one spot using dbt.
Data documentation is accessible, easily updated, and allows you to deliver trusted data across the organization. dbt (data build tool) automatically generates documentation around descriptions, models dependencies, model SQL, sources, and tests. dbt creates lineage graphs of the data pipeline, providing transparency and visibility into what the data is describing, how it was produced, as well as how it maps to business logic.
Lineage is automatically generated for all your models in dbt. This has saved teams numerous hours in manual documentation time.
There is no need to host an orchestration tool when using dbt Cloud. It includes a feature that provides full autonomy with scheduling production refreshes at whatever cadence the business wants.
Scheduling is simplified in the dbt Cloud UI. Just give it directions on what time you want a production job to run, and it will take it from there.
dbt (data build tool) comes prebuilt with unique, not null, referential integrity, and accepted value testing. Additionally, you can write your own custom tests using a combination of Jinja and SQL. To apply any test on a given column, you simply reference it under the same YAML file used for documentation for a given table or schema. This makes testing data integrity an almost effortless process.
Talk to an expert about your dbt needs.Before learning dbt (data build tool), there are three pre-requisites that we recommend:
There are many ways you can dive in and learn how to use dbt (data build tool). Here are three tips on the best places to start:
dbt (data build tool) simplifies and speeds up the process of transforming data and building data pipelines. Now is the time to dive in and learn how to use it to help your organization curate its data for better decision making.
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