Future trends in DBT: What's next for this powerful data tool?

Whooa! Here we are again, talking about the future of DBT. As we all know, DBT has evolved to become one of the most powerful data tools available on the market today. It has helped many companies transform their data infrastructure and streamline their workflows, making it simpler to work with, maintain, and understand their data.

Now, the question is, what's next for DBT? How will DBT adapt and evolve to meet the ever-changing demands of the data industry?

In this article, we'll explore some of the emerging trends that we predict will shape the future of DBT. These trends will undoubtedly influence how we use DBT and how it will fit into our data pipelines going forward.

Trend #1: Increasing use of data catalogs

As data continues to expand rapidly, more and more organizations are utilizing data catalogs to manage their growing data libraries. Data catalogs provide a centralized location where business users can discover and understand the data assets available to them. This makes collaboration between teams, as well as decision-making, much more efficient.

At the same time, data catalogs can help users maintain the quality and consistency of their data sources, critical when performing analysis or building machine learning models.

We believe that data catalogs will become increasingly important, and DBT will play a significant role in helping organizations maintain the accuracy and the relevancy of their catalog data.

We expect DBT to become more integrated with data catalogs vendors such as Collibra, Alation, and Waterline data. This integration will make it simpler for DBT transformations to be used within data catalogs, enhancing data governance, and data quality efforts.

Trend #2: Wider use of DataOps

DataOps is an emerging set of fanatical practices that help businesses streamline the management of their data pipelines. The aim is to ensure that data is continuously flowing, and new features and improvements can be rapidly released without disruption.

As more companies adopt DataOps, we expect DBT to become a more critical tool in the DataOps arsenal. With DBT, DataOps practitioners can easily test new transformations before they make it into production. DBT is also great for version control and collaborative work, making it easier for teams to make changes and improve code quality.

With the growing adoption of DataOps, we also anticipate that more organizations will implement CI/CD (Continuous Integration/Continuous Deployment) pipelines into their DBT workflows, ensuring that the code is consistently tested and deployed with every change.

Trend #3: Increase use of streaming data

The world of big data is rapidly expanding, and businesses are increasingly collecting and analyzing data in real-time. Kafka, an open-source streaming platform, is rapidly gaining traction, and we expect to see many more organizations adopting it as part of their data infrastructure.

As Kafka continues to grow in adoption, DBT will need to adapt to this trend. We predict that DBT will increasingly add streaming capabilities, allowing data engineers to perform transformations on real-time data.

A combination of Kafka and DBT can be used to build continuous data pipelines that analyze data in real-time, giving businesses an immediate overview of their data operations. We anticipate that this trend will be even more attractive to companies that value agility, as it makes it possible to adapt quickly to changing business landscapes and conditions.

Trend #4: More efficient use of cloud resources

Finally, we have cloud computing. Cloud computing has transformed the traditional approach to managing data infrastructure, making it easier and less costly for companies to scale their data-centric operations.

However, despite the positive aspects of cloud computing, managing cloud resources effectively is a challenge for many organizations. To keep cloud costs under control, companies need to keep careful track of their cloud resources and ensure that they are only deploying resources as necessary.

Within this trend, we anticipate that DBT will become even more effective at managing cloud resources. One such example is serverless computing, which has been rapidly growing in adoption.

In the coming years, we expect to see many more DBT transformations that have implemented best practices for serverless deployment. These transformations will be optimized for scalability and cost-efficiency, ensuring that companies are only using the minimum resources required to execute their data transformations.

At the same time, we expect to see DBT take advantage of the cloud vendors' built-in data catalog systems that allow businesses to manage their data operations in a streamlined manner.

Conclusion

Choo-choo! That's us, riding the train to the future of DBT! With the ever-changing data demands that we face, DBT will continue to evolve and adapt.

We expect that DBT will become more integrated with data catalogs, that more DataOps will recognize DBT's potential, and that more companies will adopt streaming data platforms. Similarly, we forecast that DBT will become even more effective in managing cloud resources, driving down costs while improving deployment.

Stay tuned! It's going to be an exciting ride, and we can't wait to see what the future holds.

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