For many data projects, the initial requirement is simple: copy a few tables from a source system into your new analytics platform. The natural first step is to build a few dedicated data pipelines—one for each table. It’s fast and it works. But what happens when five tables become fifty? That straightforward solution quickly becomes a brittle, time-consuming web of individual processes that are difficult to manage, modify, and scale.

This exact scenario inspired us to find a better way. While working with a $1B property management company, we saw firsthand how a process built on individual pipelines became unwieldy as their data needs grew. That experience became the catalyst for designing the reusable, metadata-driven ingestion framework we use today—a strategic approach that trades short-term convenience for a robust, scalable solution.

An Architecture for Growth

Our framework is designed to be both powerful and flexible, using a combination of Fabric’s core components to create an elegant, automated workflow.

Here’s a high-level look at the pattern:

• The Control Center: At the heart of the system is a Fabric SQL Database that holds all the metadata. This is where we define what data to move—the source and target schemas, table names, and groupings of tables called "jobs".
• Orchestration with Data Factory: A master Fabric Data Pipeline acts as the conductor. It kicks off by reading from the metadata database to get the list of tables for a specific job. Then, it loops through each table and executes the copy process.
• Landing and Ingesting: The process is broken into two distinct stages. First, the pipeline lands the raw data from the source (in our case, Snowflake) as Parquet files into a dedicated "landing" area in a Fabric Lakehouse. Once a file is landed, a JSON "manifest file" is created alongside it, confirming the load and logging key details like the record count and timestamp.
• Processing with Spark: A Spark Notebook, using Python, takes over for the final step. It reads the newly landed Parquet files, adds its own auditing columns (like ingested_on), and formally ingests the data into clean, efficient Delta tables, ready for analytics.

Lessons from the Field: Flexibility and Cost-Efficiency

Building this framework in a real-world environment revealed some of Fabric’s key strengths and important considerations for any implementation.

One of Fabric’s best features is the flexibility it offers. You can orchestrate high-level activities in a pipeline and then dive into low-level Python code in a notebook to handle complex logic, like creating our manifest file. This gives developers the power to choose the right tool for the job.

We also learned critical lessons in cost management. We found that the default Spark clusters setting are quick to be provisioned but can be inadequate for a specific workload. By configuring a smaller, custom Spark pool you can save a significant number of CUs over time; alternative a larger cluster can reduce time generate valuable insights when larger datasets need to be processed. This kind of practical, cost-conscious tuning is essential for a successful Fabric deployment.

Should You Adopt a Metadata-Driven Framework?

When does it make sense to invest in this type of framework versus creating individual pipelines?

The answer depends on scale and strategy. If you only have a handful of tables and don't anticipate that changing, a simpler approach may be sufficient. However, if you are building an enterprise-level platform where you expect the data footprint to expand, a framework is invaluable. It makes the process of adding 100 new tables almost as easy as adding one.

You also have to consider the hand-off. A sophisticated framework requires a team that can manage and maintain it. The components are designed to be modular, so you can start simple and add complexity—like incremental load logic—over time, but it’s a strategic choice that should align with the client’s long-term capabilities.

Ultimately, moving beyond copy-paste pipelines is about building for the future. By leveraging a metadata-driven approach, you create a system that is not only efficient and scalable but also easier to maintain and adapt as your business evolves.