For those not in the know, dbt (short for \u201cdata build tool\u201d) is the platform that\u2019s quickly becoming the darling of the data world. Much like Apple\u2019s GarageBand enables musicians to produce professional-sounding recordings without the aid of studio engineers, dbt helps data analysts disseminate organizational knowledge in a visually digestible format without software engineers. In both instances, these platforms channel raw information into a finished product once thought unattainable without assistance\u2014not to mention at a radically reduced cost.<\/p>\r\n
Since dbt was first developed in 2016, many of its practitioners began to call themselves analytics engineers, <\/em>a hybrid role that bridges the gap between data analysis and software engineering.<\/p>\r\n The platform\u2019s functionality boils down to this: If you can write SQL SELECT statements, you can use dbt to build models, write tests, and schedule jobs to produce reliable, actionable datasets for analytics and build production-grade data pipelines. Put simply, it frees your data analysts to do more with less, granting them the power of software engineering practices that typically develop over the course of a career.<\/p>\r\n Before delving further into dbt, let\u2019s look at the way data is traditionally handled. Data engineers are responsible for building and maintaining scalable systems as well as landing data in data lakes. Analytics engineers then take that source data and transform it into usable datasets. At that point, data analysts scour the results to generate insights. Sounds simple enough, doesn\u2019t it?<\/p>\r\n Anyone who works in data knows that the lines between these functions can become blurred, with all three roles overlapping at any given time. The real secret to a high-functioning data team is making sure each member is capable of collaborating with any other, regardless of individual skillset, job title, or background. That\u2019s the thought process behind dbt, one that makes data analysts feel comfortable. While scaling to meet the needs of data engineers, dbt is the one tool that all data practitioners can collaborate in to build their knowledge graphs.<\/p>\r\n Let\u2019s explore some of the reasons why your organization should depend on dbt to transform, test, and document\u00a0data\u00a0in the cloud\u00a0data\u00a0warehouse.<\/p>\r\n First and foremost, dbt is easier to use than many of its counterparts. It enables the transformation of data in warehouses by way of simple SQL queries. While it doesn\u2019t extract or load data, dbt is extremely good at transforming data that is already present. The tool acts as an orchestration layer on top of your data warehouse to improve and accelerate your data transformation and integration process.<\/p>\r\n It bears repeating: Anyone who knows how to write SQL SELECT statements has the power to shape raw data into almost any useful form imaginable. dbt natively understands the dependencies between all models, and as a result, it can do powerful things like run models in dependency order, parallelize model builds, and run arbitrary subgraphs defined in its\u00a0model-selection syntax. It also grants easy integration with existing systems such as Databricks, Snowflake, and Airflow. It\u2019s a very minimal learning curve for a tool with maximal potential.<\/p>\r\n dbt enables data transformation by introducing organizational logic into the data pipeline. This essential process is complemented by the platform\u2019s built-in\u00a0data quality test\u00a0functionality. If any data fails to operate in the testing environment, dbt flags the error so the underlying problem can be diagnosed.<\/p>\r\n From an organizational standpoint, this testing process helps put high-quality data in sharp relief for professionals with various analytical backgrounds and maturity, effectively blocking out noise.<\/p>\r\n Testing also ensures continuity when data analysts depart your company. Gone are the days of testing alone on an island without proper systems in place to document processes. With dbt testing in place, instructions can be handed over to anyone else who needs them as long as models are properly structured with proper test coverage. By making sure all testing is documented, the process of onboarding future employees suddenly becomes far less strenuous.<\/p>\r\n Data analysts love playing in their own sandboxes, and with dbt\u2019s robust environment management features, they have the freedom to do so. Users can choose the kind of environment they want to work in without altering production data models or disturbing someone else\u2019s schema. This functionality avoids some of the more common pitfalls of data management:<\/p>\r\n Environment management mitigates this trifecta of issues, all while ensuring that everyone is looking at the same raw data, regardless of a data analyst\u2019s chosen environment.<\/p>\r\n As a bonus, environment management can help develop younger analysts. We\u2019ve established that silos are undesirable during testing, but they can actually be helpful when it comes to nurturing young talent. dbt provides a means for them to save their changes and features no matter what environment they\u2019re in. This way, less experienced analysts don\u2019t have to build around only what they know and can experience the development cycle in full without slowing down production.<\/p>\r\n Data analysts intimately understand the businesses in which they work. They understand how to observe performance and diagnose any problems, all through the use of data. Many of them write code to some degree, but all too often, their reach exceeds their grasp, and experienced software engineers must be called in to turn data into more digestible formats.<\/p>\r\n As we\u2019ve seen, dbt puts the power of software engineering into the hands of data analysts. The platform is built from the ground up to enable data teams to take full control over the information they depend upon to do their jobs, authoring and deploying modular code inside cloud-native data platforms. Now, equipped with dbt, analytics engineers can organize and model essential information themselves to create clean data sets\u2014in other words, they can answer their questions. It\u2019s nothing short of a seismic shift to the analytics ecosystem, and we urge all data teams to get acquainted with dbt today.<\/p>\r\n Are you ready to join the community of more than 12,000 companies that use dbt in production? Wavicle<\/a> can help you streamline the process.<\/p>\r\n","protected":false},"excerpt":{"rendered":"What\u2019s the (data) point?<\/h2>\r\n
Ease of use<\/h2>\r\n
Quality test functionality<\/h2>\r\n
Choose your own environment<\/h2>\r\n
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The data tool we needed<\/h2>\r\n