By: Srikanth Singh Bidhaniya, Associate Principal - Data Engineering, LTIMindtree

In this article, we will understand how the data platforms have evolved, identify key challenges most organizations face today in managing these platforms, and explore how dbt can address these challenges.

Evolution of data platforms

Data platforms have been used for some time to consolidate data by extracting it from a wide array of source systems and integrating them by cleaning, enriching, and storing the data to make it easily accessible to different teams/users.

In the early days, organizations used Extract, Transform, Load (ETL) tools like Informatica, IBM DataStage, SSIS, and others for on-premises data processing. These tools were primarily batch-oriented, focused on extracting data from various sources, transforming it to fit a specific schema, and then loading it into a data warehouse.

As organizations started dealing with massive amounts of data, these tools faced scalability and cost challenges, leading to Hadoop’s emergence. Further, technologies like Apache Pig and Apache Spark emerged to provide ETL capabilities in the Hadoop ecosystem.

With the need for faster insights, data integration tools evolved to support real-time and near-real-time ingestion and processing capabilities like Apache Kafka.

With the proliferation of cloud computing, cloud data warehouses like Redshift, Big Query, Snowflake gained popularity. Their impressive performance and scalability, combined with a clear separation of computing and storage, allowed the possibility of managing the transformations within the database effectively.

Challenges of a data platform

The primary challenge that data platforms have faced over the years is improving the scalability and performance of data processing due to the increased volume, variety, and velocity of data used for analytics. As companies grow, they must integrate new data sources with agility and speed to ensure data quality.

While most platforms have been able to largely achieve this, consistently delivering high-quality, low-latency analytics is still a challenge for most data platforms.

Furthermore, in large teams, engineers often work in isolation, resulting in knowledge being siloed. This results in code duplication or different calculations of the same KPI by different teams.

dbt framework for modern data platform

The way to solve these problems is by adopting DataOps principles and using tools that natively support them. One such tool that has gained a lot of attention in the data community is dbt.

Simply put, dbt is a data transformation tool that uses the traditional Structured Query Language (SQL) to define the transformation rules. The only elements of the dbt code that don’t use SQL are the ones using jinja (templating language) macros. The combination of templating language like jinja with SQL makes it more powerful to define the transformation rules. One of the natural outcomes of this, is that dbt natively understands the dependencies between all the data objects and this awareness helps solve the day-to-day challenges of building and managing data workflows such as:

1. Adding new data objects without worrying about the orchestration and defining the execution order.
2. Executing tests through dependent objects and catching errors early in the pipeline.
3. Improving collaboration due to transparency of relationships between objects and their ownership.
4. Simplifying the Continuous Integration (CI)/Continuous Development (CD) workflow without the need to define the execution order of the objects.
5. Investigating the data issues without inspecting the full pipeline by narrowing the analysis using a lineage graph.

Although the primary purpose of DBT is facilitating data transformation, it can also be viewed as a framework that can address the challenges of modern data platforms.

In addition, dbt can also act as a centralized hub for defining the upstream sources and downstream consumers of the data warehouse.

1. Sources: Allows you to name and describe the objects loaded into the warehouse. Additionally, it can be extended to manage the metadata for the ingestion framework, too.
2. Exposures: Enables you to name and describe the downstream use of the objects in the warehouse.
3. Metrics: Helps you define metrics, enabling downstream users to query the definitions across Business Intelligence (BI) tools.

By defining Sources, Exposures and Metrics along with the transformations, we can ensure that data is well-understood and efficiently used across the organization.

Let us review how dbt as a framework supports key DataOps principles.

1. Collaboration: dbt allows writing data transformations using SQL and providing documentation within the models to generate a clear documentation making it easier for the data teams to understand and collaborate on data transformations.
2. Automation: dbt allows writing data transformations and test cases as code with clear dependencies, enabling running both scheduled data pipelines as well as CI/CD jobs automatically in the correct order.
3. Version Control: dbt seamlessly integrates with Git, enabling tracking changes of models and allowing teams to work on different versions of models facilitating parallel development and testing.
4. Continuous Integration and Delivery (CI/CD): dbt allows businesses to automate deployment processes by identifying changes and running impacted models and their dependencies along with the test cases ensuring quality and integrity of data transformations.
5. Monitoring and Observability: dbt allows to run data freshness and quality checks to identify potential issues and trigger alerts,
6. Modularity and Reusability: dbt encourages breaking down transformations into smaller, reusable models. It also allows sharing models as packages, facilitating code reuse across projects.

In conclusion

As data platforms become more complex, managing them becomes difficult, and embracing the DataOps principle is the way to address these challenges. In this blog, we looked at dbt as a solution that supports the DataOps principles, allowing us to build scalable, agile, and well-documented data platforms. Lastly, dbt allows businesses to define the upstream sources and downstream consumers of the data warehouse besides defining the metrics and using them consistently across the organization.

If you are looking to build a data platform using dbt, check out Auto DBTizer, which accelerates and automates the conversion of Snowflake objects to a functional dbt project.