Whether you are thinking about replacing your transformation tool or adding transformations to your data stack for the first time, unintended consequences can result. Adding a new transformation tool should help optimize and accelerate analytics, but any change to your modern data stack can create challenges. In this blog, we will look at common pitfalls to avoid as well as best practices to help you make the most out of your data transformations.
Pitfalls to avoid when transforming your data
The following practices can run your data integration efforts aground and create no shortage of trouble for you engineers, analysts, and decision-makers.
Don’t use ETL
The Extract, Transform, Load (ETL) workflow was designed for on-premise databases and is not optimized for the cloud. Massive Parallel Processing (MPP), compute power and economical storage of the cloud means you’ll benefit from an Extract, Load, Transform workflow. Extract data from the source, load it to a cloud data warehouse or data lake where it can be affordably stored, and then use the power of the cloud to run simple and complex queries to transform your raw data into analytics-ready data sets. This makes data transformation more efficient, performant and affordable. While it can be tedious to rewrite ETL queries to ELT queries, this upfront time investment will save query performance and costs in the long run.
Don’t develop in a black box
Engineers can often find themselves developing in a silo, creating a black box. Locally writing SQL makes the code inaccessible leaving it unchecked and not discoverable. Development shouldn’t happen in a black box. The lack of transparency and visibility can result in duplicate work or variations in calculations that impact downstream reporting resulting in data quality concerns and a loss of trust from data consumers. Tools like dbt Core and dbt Cloud (data build tool) help to promote collaboration and introduce engineering best practices like version control to ensure teams can work together and that data models can be easily discovered by authorized users.
GUIs are rarely as easy as they seem
Tools that use Graphical User Interfaces (GUIs) offer users pre-designed components to drag and drop onto a canvas to visually build out data flows. These look easy to use, but can often be rigid and unintuitive. Learning a new GUI can take weeks to months. The steep learning curve can delay the delivery of data analytics initiatives. Additionally, there may be actions you want to take that are not natively supported, making these applications limiting. SQL, on the other hand, is the universal language of analysts, making it widely accessible. Read more about GUIs vs. SQL here.
BI tools are not transformation tools
The value of BI and visualization tools is their ability to tell complex data stories at a glance. But before that data can be visualized, it has to be cleansed. Quality is often low in raw data because of duplicate, missing or incomplete records. Although BI tools support some lightweight transformations they are not built to handle raw data, especially as data volumes and complexity rise. Trying to transform data in a BI tool will create performance issues. Additionally, while these tools produce amazing visualizations that seem accessible, as non-technical users further explore these visualizations, they can quickly become overwhelmed by the underlying transformations. Instead, prepare your data with a tool specifically designed to handle transformation, and then your BI tool can read clean, high-quality data for accurate reporting.
Best practices to maximize the impact of your transformations
Your organization should cultivate the following habits to set your data integration efforts on a sustainable path to success.
Use prebuilt SQL to accelerate analytics
Transforming your data in SQL gives you maximum flexibility, but has a bad reputation of being slow to write, especially for beginners. Stop hand coding every job from scratch to save time and accelerate the delivery of new analytics! Find and adopt an off-the-shelf, pre-built solution that can be used and reused over and over, saving development resources while freeing up more time for analysis. Fivetran data models with pre-built SQL provide dbt users out-of-the-box reporting for popular data source connectors, with more being built. Learn more about how you can use Fivetran-developed and maintained data models to accelerate analytics.
Improve documentation across your organization
Data engineers, who are perpetually strapped for time, spend far too long responding to data questions that could easily be answered by well-maintained, up-to-date data lineage and documentation. Data lineage and documentation can provide data analysts with a comprehensive view of how the data has evolved from its raw form to its analytics-ready state, while improving data literacy across the organization. This enables data engineers to spend less time explaining their logic. Data analysts can easily understand the source and dependencies of the data they are working with.
Join a community of experts
We talk to data teams daily that are extremely lean. Whether you work in an enterprise or startup, you can find yourself and a few data professionals responsible for a mammoth project. We highly recommend finding a community of experts so you can lean on your peers for additional support and best practices. You can join the dbt Slack Community and join channels like #tools-fivetran to ask questions and share your best practices.
Interested in learning more about how Fivetran and dbt Core can fit in your modern data stack? Check out this webinar.