The Benefits of Building Your Transformation Layer in Your Data Warehouse

Avoid inefficiency and higher costs by transforming your data in one place

Table of Contents

    Business intelligence (BI) plays a critical role in how organizations capture and take action on insights from their data. Without BI solutions like Sigma, ThoughtSpot, Tableau, and others, organizations would be hard pressed to provide democratized analytics and insights for decision-makers.

    Just having a BI tool, however, does not guarantee successful activation of your business data. It is important that you have a solid data foundation to build on. This means having a transformation layer at the data warehouse level, allowing you to build, manage, and scale your data transformations to support any business use case or user.

    In reality, for many organizations data transformation happens in different places throughout the data stack, creating a slew of problems including inconsistent data models, varying definitions of business metrics, and inefficient data processing.

    “At Sigma, we provide a modeling layer designed to support exploration and last mile analytical modeling, but we are firm believers that a well-architected data platform starts with enterprise-grade transformational modeling in a platform like Coalesce,” says David Porter, Head of Partner Engineering at Sigma. “Modeling upstream in the warehouse to provide clean, trusted, well-modeled, and reusable data is critical to enable analytics that are approachable for the entire organization.”

    Coalesce solves this problem by allowing data teams to manage the data transformation process and data pipelines of any size as efficiently as possible. By building your data foundation in your data warehouse where it’s best suited, you can sidestep the challenges of trying to perform data transformations, modeling, and management in a BI tool.

    Watch on-demand webinar:
    How Paytronix uses Coalesce to eliminate data delays, inefficiencies, and the high costs of performing data transformations in Looker.

    Let’s explore the advantages of moving the data transformation layer to your warehouse, and how you can manage this process end to end with Coalesce.


    Standardized data transformation

    One of the most beneficial aspects of moving your data transformations out of BI tools and into your data warehouse is data transformation standardization. Standardization allows for the consistent application of all business-related data support. Different questions can be answered with the same foundational data model, and answers to questions from different dashboards return the same result.

    Most BI tools provide users the ability to model data. While this is helpful functionality for light data cleaning or joining a table(s), it is not an effective solution for full-fledged data transformation. Multiple issues can arise when trying to implement and scale heavy data transformation within a BI solution.

    By attempting to perform enterprise-level data transformations within your BI solution, finding a way to sustainably manage these transformations becomes a challenge. Many data professionals have experienced the massive sprawl that comes from dozens, hundreds, or even thousands of data sources and models that exist within a BI tool. At this scale, work is often duplicated, or worse, different results are coming from the same data sources because they have been modeled or transformed slightly differently. Managing all of the use cases and models that results from transforming data within a BI tool becomes increasingly difficult as the demands for data grow.

    Additionally, when managing data transformations within a BI solution, auditing models and results can be a challenge. Not only does the volume of data models and sources to audit become difficult to manage, but you also lose the context of the data you are auditing. This is because it is much harder to see how upstream data is impacting your current model, or how the impact of a column somewhere else in your data pipeline will ultimately affect your model within your BI solution.


    Centralized data assets

    You may think that by having a disciplined framework, you could manage a large, standardized data model within your BI tool. This may be true in theory, but implementing this at scale is a different story.

    BI tools are built first and foremost for reporting. They may include capabilities for cleaning and preparation of data, but ultimately the reporting and dashboarding capabilities are where these solutions shine. Trying to build a standardized enterprise data model in a BI solution is less than ideal due to the functionality needed to properly build, manage, and scale enterprise data pipelines.

    By defining your data model within your BI tool, you are isolating your data model to your BI solution. This means that other tools or users will not have the visibility and accessibility to the data model that they would if the transformations were taking place in a centralized data warehouse. This becomes increasingly risky if you were to migrate to a new BI solution, or buy a second one.

    If your data transformations live in a warehouse, your BI solution(s) could quickly and easily point to your data model within your data warehouse and be up and running. If the data model is built within a BI tool, you now have to determine how to migrate that model into a new tool, along with all of the other assets that need migrating—creating significantly more work for your team and impacting your time to value in a new solution.

    By moving data transformations to a data platform like Snowflake, you gain multiple advantages over doing the same work in a BI tool. First, you can build a consistent, standardized data model that is then available to all of your downstream processes, such as BI tools, users, and AI/ML models. Additionally, you eliminate inefficient data modeling and querying. Instead of modeling the same tables and views in different ways for your BI needs, you can centralize the transformations, allowing the same table to serve multiple reports, users, dashboards, and so on.

    Next, by centralizing your data model, the entire data team has visibility into the logic and can collaboratively work on managing and maintaining these transformations to best serve the business’s data needs. Finally, by transforming data in one place, you ensure that users gain access to standardized, centralized data, increasing the quality of your reporting as analysts are less likely to need to redefine columns or tables.

    With this transformation layer in place, you can focus on building clean, sustainable, and manageable reporting assets within your BI solution. Analysts don’t have to focus on complex data modeling as the data models they access are standardized for them. The sprawl of inconsistent and varying data sets within your BI solution is significantly eliminated, and your data team can focus on building actionable insights on top of a standardized transformation layer that you can trust. This allows the reporting layer to focus on what it is best at—reporting!


    Lower cost, higher value

    We have discussed why a standardized data transformation process outside of your BI solution is important. But there are additional reasons why data transformation should take place in a data warehouse, specifically further upstream in your data pipeline.

    The further downstream a transformation is applied, the smaller the scope of value it is able to serve. As transformations happen further and further downstream, the use cases they are solving often become more and more specific, serving less and less value to other needs. This becomes painfully evident when working in a BI solutions where data transformations are managed. When you do not have a standardized data model, users are required to define and apply specific transformations for their specific use case.

    Additionally, by defining data transformations within a BI tool, you are creating significantly more work for your data warehouse, which often equates to higher cost. This is because it is inherently less costly to transform data once, when loading it into Snowflake, than it is to transform it for every report, dashboard, and user that accesses that same data. By having a standardized data transformation process, you can capitalize on these advantages while optimizing your Snowflake spend.


    Easier change management

    Now that we know we need a standardized data transformation process and that transforming data early on in that process is advantageous, we can discuss a final advantage in transforming your data within your data warehouse—change management.

    When you have a standardized data transformation process running in your data warehouse, managing inevitable changes or updates to your pipelines is easier and more scalable. For example, let’s say we want to apply a text transformation to a column within a table that’s used frequently in our BI solution. If we were to apply this transformation within our BI tool, we would need to find each data model or source where this table is used, and manually update the table with the transformation for every occurrence of that column.

    By having our data transformation layer at the data warehouse level, we can apply this transformation one time to the table and column, and immediately persist that change to the rest of our data pipeline. This allows us to define transformations one time, and allow all use cases of that transformation to immediately have access to a consistent and easily applied data model. In this case, you don’t have to worry about missing the transformation within one of the reports in your BI tool.

    By performing data transformations in this way, we are able to implement a source of truth that the rest of our data applications can query. This means that the management of data models with BI tools is alleviated by the data transformation layer in your data warehouse, allowing users to focus on solving business problems with data, instead of solving data problems.


    Building sustainable data transformations

    We’ve talked a lot about the value of building data transformation within your data warehouse and the value provided from this for your BI solution. But how do we actually do this?

    Using a data transformation solution like Coalesce, data teams can rapidly build data pipelines on Snowflake that can empower your entire BI strategy. As we’ve seen, having a data transformation layer built within your data warehouse lays the foundation for the success of your data and BI initiatives. Data teams need a way to quickly build enterprise data pipelines at scale, and then manage them in a way that’s consistent and sustainable so that they can capitalize on the advantages discussed above. With Coalesce, data teams gain granular visibility into their data, such as defining data tests and understanding column-level lineage, giving them complete confidence in evaluating the impact of ongoing changes.

    If you’re interested in accelerating and amplifying your BI initiatives by building the critical piece for successful BI adoption—sustainable and scalable data pipelines within Snowflake—take Coalesce for a test drive by creating a free trial account. If you’d like to chat with us about your data transformation needs in detail, request a demo with us today!

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