Five things to consider when choosing a data transformation tool

10 minute read

For small organizations, spreadsheets suffice for data storage and analysis. Low barriers to entry and simple workflows make them an attractive option.

For enterprise organizations, however, there comes a point where the size and power limits of spreadsheet tools become a roadblock. When that happens, data transformation tools become a necessity. Here’s what you need to know about data transformation tools and how to select one.

Data transformation tools: beyond spreadsheets

As data has become more accessible to collect and store, more companies are analyzing data from e-commerce, advertising, and customer support. However, manual analysis using spreadsheets is time-consuming and prone to errors. Worse, large datasets can completely overwhelm these tools, requiring complicated partitioning to be loaded at all.

There’s a better approach than downloading CSVs and wrangling them in a spreadsheet whenever new data comes in. You can use standard data query languages like SQL to turn raw data into usable tables.

However, SQL transformations aren’t automatic. Whenever new data comes in, you have to re-run your transformation pipelines. Things become even more complicated if you want to change the transformations themselves. If you have no way to track changes to your SQL code, you can’t be sure who made a change, if anyone reviewed the change, or what tests the change went through before publication.

That is where data transformation tools come in. With a data transformation tool, you can create a data pipeline that runs on autopilot—no need to manually rerun queries repeatedly. These tools can also handle larger data volumes than human spreadsheet software and CSVs ever could.

How a data transformation tool works

Data transformation tools fit into an “Extract, Load, and Transform” or ELT process. Data engineers extract data from a source and load it into the target database, where they transform it. Transformation is the most involved step, which includes:

  • Data exploration: Looking through the database for trends and meaningful aggregates and then using these insights to make plans for future development
  • Developing logic: Using a language like SQL or Python to develop a series of computations
  • Testing: Running and debugging the transformation logic as well as verifying that the computations are correctly constructed
  • Automation and scheduling: Building a pipeline that automatically ingests new data and recomputes the relevant transformations
  • Documentation: Describing the logic and pipeline you have developed to support usage outside of a single team

The top five things you need in a data transformation tool

A data transformation tool must support the transformation process, meaning it should, at minimum, support the following:

  • Data modeling and transformation
  • Testing and verification
  • Source control and automation
  • Documentation
  • Discoverability

Here’s how a good data transformation tool supports each of these processes.

1. Data modeling and transformations

Data rarely comes ready to use. You usually need to modify it—perhaps by normalizing values, discarding invalid entries, or changing the format. You may also have to combine data from different sources, joining distinct formats into a coherent dataset.

Data transformation tools should allow you to use SQL, python, or another language to transform your raw data into usable datasets. For example, dbt lets you create model objects that you can define in SQL or Python. You can then connect model objects to develop sophisticated data analytics.

2. Testing and verification

Once you’ve developed the transformation logic, you need to verify that data loads into your pipeline correctly. Data transformation tools should support data testing to confirm that your pipeline is in good working order. The tool should enable testing in stages - e.g., deploying to a staging/testing area against model data first instead of deploying directly to production.

3. Source control and automation

Data transformation tools need to keep up with new data from sources. As new data comes in, you want to feed it into your pipelines and incorporate it into your existing analytics. That’s why automation is a critical feature of data transformation tools.

An ideal data transformation tool takes this a step further, allowing for pipeline automation and the development process itself. In software development, Continuous Integration/Continuous Deployment (CI/CD) rebuilds an application whenever developers check-in and deploy code changes. With the right tools, CI/CD can also work for data product development.

CI/CD for data works similarly for software development – after approvals, all changes go through testing and then deployment in an automated or semi-automated process (e.g., an automated process with final manual inspection and approval). Such a system, however, involves verifying changes across multiple environments, so it can be complicated and expensive to build from scratch.

Finding a tool that already supports data CI/CD can provide a significant competitive advantage. For example, Residential solar company Sunrun was able to speed up its data product deployments by 75% after implementing CI/CD.

4. Documentation

Data transformation tools are more valuable when users across your organization can understand data for themselves without relying on a central team to field their questions. Documentation helps users self-service those answers.

Good documentation can provide information on data lineage - i.e., where data comes from, how it is transformed, and who is accessing it. It can also provide information on what data means, the specifications of its formatting, and any conditions or restrictions on the data. Without this information, data sets are difficult, if not impossible, to understand.

Good data transformation tools define data pipelines in code so the underlying data is readable and shareable. This transparency allows all stakeholders to evaluate and understand what’s happening with your data. A transformation tool should also automatically generate documentation where it can - for example, automating the generation of data lineage from the relationships you define between transformations.

5. Discoverability

Data teams’ effort to create meaningful tables don’t mean much if no one can find this data. A good data transformation tool needs to make data assets discoverable and visible. There are a wide array of approaches depending on the tool, including native data catalogs, knowledge-bases, and semantic layers.

With proper discoverability, a data transformation tool makes data assets accessible to the entire organization. Non-data teams can search for, understand, and access data through the tool, democratizing data development. When other teams can self-serve, data teams don’t have to field their requests, freeing up time for higher-value projects.

Factors to consider when selecting a data transformation tool

When selecting a data transformation tool, there are a few factors to consider:

  • Support: Does this tool support the source and target systems we work with? Does this tool support our company's development languages like SQL or Python?
  • Scaling: Can this tool scale to meet a rising demand for data and use cases?
  • Testing: How does this tool support data testing? Does it have data tests built in? Can tests be automated? Does testing require full builds, or can it be done in stages?
  • Version Control: Does the tool have version control features? Does it integrate with industry standard verson systems like git?
  • Automation: Is it straightforward to automate transformation pipelines? Can it support CI/CD workflows for data development? Can you have multiple environments (e.g., dev, test, production) to run proposed changes on pre-prod data before pushing them live?
  • Documentation: Does this tool generate documentation? Can it automatically compute data lineage?
  • Security: Does this tool help us protect our data and enable data governance?
  • Discoverability: How does the tool support data discovery? Does it promote data self-service?

dbt Cloud: The industry’s leading data transformation tool

dbt Cloud is a data transformation tool designed to make your transformations more scalable, discoverable, and secure. dbt Cloud provides automated testing and documentation, CI/CD support, security features, and data governance tools. dbt’s flexible development environment supports model development in SQL or Python for smooth adoption.

Start building with dbt Cloud

Streamline your data transformation process, reduce manual errors, and increase productivity with dbt Cloud. Sign up today and take your data transformation workflow to the next level.