Data integration vs. data transformation: What’s the difference?

Businesses are becoming increasingly data-driven. That makes producing high-quality data a make-or-break proposition, no matter your field.

Unfortunately, high-quality data doesn’t come for free. Raw data has to be collected from numerous sources and turned into a format suitable for driving day-to-day decision-making.

Data integration and data transformation are two interrelated but different concepts that are integral to turning raw data into business insights. In this article, we’ll discuss what they are, how they’re related, and how to manage them at scale across your enterprise.

What is data transformation?

Data transformation converts raw data from its original format into one or more readily usable by business decision-makers. This includes normalizing, cleaning, and validating the data to ensure it's ready for analysis.

Data transformation happens as part of a data pipeline, an automated process that syncs new data from its source, transforms it, and stores it in a new destination. Data pipelines are often run on a set schedule, importing new data from their sources on a regular basis. Analysts and decision-makers can then use languages such as SQL and Python to query the data and generate reports.

Typically, data transformation occurs inside of a process using one of two methodologies:

• ETL (Extract, Transform, Load): Data is taken from its source(s), changed into its new format, and then loaded into a new destination.
• ELT (Extract, Load, Transform): A similar process except that data is transformed after it’s been loaded into its destination.

Over the years, ETL has been replaced with ELT, as cloud computing has made it easier and more cost-efficient to load data prior to transformation. Data is loaded (typically into a data warehouse) and then transformed and stored in a separate location. This approach makes the raw data available immediately to everyone with data warehouse access. It also allows teams with different needs to transform the raw data however they see fit.

Benefits of data transformation

No business that relies on data can afford to lose out on the benefits of data transformation, a process that:

Increases data quality. Raw data can almost never be used as is. It’s full of malformatted or missing values, redundancies, inconsistencies, and sometimes outright incorrect information.

This poor-quality data can cost an organization up to 30% of its yearly revenue. Besides poor decision-making based on incorrect values, unexpected values in new data (e.g., a different date format, a malformed customer ID number) can cause data pipelines to break, making reports and data-driven applications unavailable until they’re fixed. Bad data can also result in filing incorrect regulatory reports, which could result in fines.

Produces organized, easy-to-use data. Without data transformation, analysts and decision-makers would have to reinvent the wheel whenever they wanted to create a new report, fixing data issues anew each time. Data transformation provides a clean data set in an accessible location, making it easier to generate new reports and data-driven apps.

Paves the way for machine learning and AI workloads. Analytics is a deterministic approach to data. The volume of data doesn’t matter as much as its accuracy and performance for querying. By contrast, machine learning and AI take a probabilistic approach to data, using neural networks and statistical inference to generate new outputs. This requires a large volume of high-quality data - whether you’re training your own Large Language Models (LLMs) or using data for context with techniques such as retrieval-augmented generation (RAG).

What is data integration?

Data integration is a type of data transformation. As part of the data transformation process, a data pipeline may bring data in from multiple sources. It then combines this data to provide a single, unified view of a data set across the enterprise.

For example, data about customers in a retail business may be split across multiple systems - e-commerce sales, marketing emails, website analytics, advertising campaigns, web search, etc. Bringing this data together and presenting it as a unified customer record can help businesses answer questions such as where they acquire their highest-spend customers, which types of users are most likely to refer their friends, and more.

Benefits of data integration

Promotes better decision-making. Having a 360-degree view of the business makes it easier for decision-makers to connect the dots and see trends that otherwise might have been obscured by keeping data in separate systems.

Can improve performance. Performing joins across data residing in multiple external systems can result in slow query performance. Unifying the data into a single location eliminates the need for cross-system joins, meaning ad-hoc queries return results orders of magnitude faster.

Increases data discoverability. According to a 2022 Matillion and IDG survey, most large companies draw data from over 400 sources. 20 percent are drawing data from over 1,000 sources. It can be hard for employees to find precisely what they need in these vast data oceans.

Data integration brings critical data together into a single location, which helps eliminate data silos—islands of data that don’t produce business value because data consumers can’t find them.

Encourages data democratization. All teams need data. However, not every team can afford to hire a small crew of data engineers or analytics engineers to wrangle it from across the enterprise into a usable format. Consolidating such data into a single location makes it easier for data consumers who are conversant in SQL to find and make use of data.

Types of data integration

There are several ways to perform data integration:

Data warehousing. The traditional approach in which engineers physically move data into a single location and a minimal number of tables. Data warehousing uses different data modeling techniques than relational database systems that enable faster querying for BI use cases.

Virtualized integration. Virtualized integration provides access to numerous data sources from within a single location. This approach misses out on some of the performance benefits provided by data warehousing (though it may utilize techniques such as caching to speed up subsequent access). The upside is that business users can access data where it currently lives ‌without waiting for engineers to import it into the warehouse.

Data mesh. Bringing data into a data warehouse typically depends on relying on a centralized data engineering team to create new data pipelines. This can create a bottleneck as the team’s queue fills up with new requests. A data mesh architecture solves this by modeling data as a set of interconnected domains. A mesh architecture provides a self-service data platform that makes it easier for teams to create their data pipelines and for business users to find and use the data sets they produce.

The challenges with data transformation and data integration

Data integration, in short, is one data transformation technique you'll use among many others in a data pipeline to ensure that data is reliable, accurate, and performant. Other data transformation techniques include cleaning, aggregating, generalization, validation, normalization, and enrichment.

The nature of enterprise data makes managing both data transformations and data integration at scale a challenge. Your company, like many others, is probably managing data across hundreds of sources, including data warehouses, analytics tools, marketing platforms, relational databases, NoSQL data stores, data lakes and lakehouses, etc.

Lacking a single approach to manage transformation and integration across such disparate systems, engineering teams often end up implementing data pipelines in an ad hoc manner, using technologies and languages that lock them into vendor-specific solutions. This creates numerous inefficiencies:

• Engineers reuse little code, instead solving the same problem redundantly over and over in different languages.
• As an organization, you have little visibility into what data assets you have and what data pipelines might currently be running. This makes it impossible to ensure data quality and consistency across data stores. It almost makes it impossible to manage data pipeline spend and control costs.
• There’s no consistency in how data transformation and data integration code is tested before it’s put in front of users. This can result in injecting bad data into production data sets.
• There’s also a lack of consistency and how changes are rolled out to production. While software engineering has focused on techniques such as Continuous Integration and Continuous Deployment (CI/CD) to verify changes before release, most analytics code is still deployed in an ad hoc, one-off fashion.

dbt Cloud: A data control plane for your data pipelines

Managing data transformation and data integrations across your enterprise requires a data control plane—a single toolset that enables you to manage all data transformations across the enterprise.

dbt Cloud is a data control plane for managing your data pipelines that enables everyone in your enterprise to work with data no matter where it lives. Using dbt models, data engineers, analytics engineers, and even business users can model data transformations uniformly using SQL or Python code, avoiding vendor lock-in. This enables data engineers to monitor, manage, and fine-tune data transformation workloads across the enterprise.

dbt Cloud provides out-of-the-box features that support shipping and using high-quality data sets, including:

• Version control and peer reviews
• Support for creating DRY code that can be reused across projects
• Testing and documentation, including automatically generated data lineage
• CI/CD deployment and automated testing of changes in pre-production environments before release
• Data discovery and management via dbt Explorer, which enables data consumers to find and learn about data sets while giving data producers insight into usage and performance
• AI-assisted support for generating new data models, tests, and docs using dbt Copilot, which embeds context-aware AI into every phase of your analytics workflow

Learn more about how dbt Cloud can manage data transformations at scale across your enterprise—ask us for a demo today.