Metadata-Driven Ingestion Pipelines and Why Your Lakehouse Needs It

Data Ingestion and various design patterns have already been introduced in one of the previous blogs. Additionally, we have discussed Incremental Data Ingestion where we have touched upon key aspects to minimize costs in production. In the Incremental Data Ingestion blog, we briefly mentioned metadata, but never explored what it means, how it adds value, and why your data lakehouse needs it.

Why Metadata Matters in Data Ingestion?

Metadata simply translates to “data about data”. Consider an example where a company runs a handful of data pipelines. Such a scenario, although rare, means that one can manage and handle these handful of pipelines easily. But in a real enterprise environment with hundreds or thousands of ingestion pipelines coming from various heterogeneous systems like ERP, WMS, CRM, APIs, IoT Devices, Sensors, Files, Database etc., manual tracking becomes impractical. This is where metadata plays a crucial role. Metadata acts as an instruction manual and answers some very important questions like:

• Where is the data coming from?

• What connections does it require?

• When was the last time the data was ingested (aka checkpoint)?

• How much data was ingested?

• Where is the target location?

• What schema, if existing, does the ingestion follow?

Instead of hardcoding this knowledge into pipelines, metadata allows ingestion to scale seamlessly with:

• Scalability → Onboard new sources without modifying code. Simply add this new info in metadata.

• Governance → Centralized visibility and lineage.

• Adaptability → Handles schema changes.

• Flexibility → Batch, CDC or streaming supported by config updates.

• Maintainability → Fewer pipeline rewrites.

Example:

If a company needs to ingest data from two new ERP tables, a metadata-driven approach would only require updating entries in metadata tables, i.e., connection details, object details, and ingestion rules. No pipeline rewriting, redeployment, or orchestration change is needed.

Practical Tip:

Store the metadata in a version controlled code repository when possible. This accelerates rollback, auditing, reproducibility and gives clean configs for deployment to higher environments.

Control Tables – The Backbone of Metadata-Driven Ingestion

We can think of metadata as a user manual for ingestion pipelines. The actual instructions within that manual are stored in control tables. These control tables are independent relational stores (like SQL Server or Azure SQL) that hold core ingestion information.

Some common control table types include:

1. Connection Table

Stores source system connection information:

• server

• username

• connection type (JDBC, API, Blob)

• authentication properties

  
  

These allow the ingestion to dynamically establish connections without static credentials in code.

Example:

Updating authentication details (e.g., rotating a secret) can be done centrally and no need for pipeline redeployment.

Practical Tip:

Use secure credential stores like Azure Key Vault or Databricks Secret

2. Object Details Table

Defines on an object level, what to ingest:

• source table or file path

• destination or target zone

• source system

• load type

• primary keys (if present)

  
  

This table acts as a single source of truth for each ingestible object.

Example:

If a new WMS table needs to be ingested daily instead of hourly, modifying the frequency in this table for that object would trigger changes automatically.

3. Object Attributes Table

Defines how the ingestion should run for an object:

• batch size

• CDC column identifiers

• filter conditions

• ingestion query

• schema rules

This table controls the ingestion logic per object.

Example:

If certain records have to be filtered (soft deletes), adding a filter in the attribute table for that object would prevent unnecessary ingestion volumes because of those soft deletes.

4. Checkpoint Table

Stores auditing information:

• last ingested timestamp

• last processed timestamp

• last updated timestamp

• change tracking tokens

  
  

This information helps in reliability and recovery.

Example:

If a network interruption stops ingestion mid-run, checkpoint table allows pipelines to resume from the last checkpoint value rather than triggering a full restart/refresh.

Practical Impact – Why This Matters in a Real Enterprise Environment

Let’s directly look into a real case scenario. Your team needs to ingest:

• 500+ operational tables

• 10 new sources per quarter

• both batch and CDC patterns

Now, without the metadata-driven ingestion approach:

• pipelines multiply and get harder to manage

• maintenance cost increases

• code changes would apply for each new source

• onboarding time for new data engineers would increase

This is where metadata-driven ingestion comes to the rescue:

• a single/multiple generic pipelines that can serve single/multiple sources.

• onboarding a new source system or table is simply adding this information in metadata

• CDC logic is externalized, for e.g., in the attributes control table

• business teams gain visibility into ingestion status

In short, metadata transforms ingestion from manual craftsmanship into scalable automation.

Practical Tip:

Maintain a dashboard (Power BI / Databricks SQL / Grafana) reading from control tables for health monitoring. This would also help in diagnosis of any issues that may arise.

Extending Metadata-Driven Ingestion Beyond Tables – Images and Vision Data

Until now, we have talked mostly about data present in the form of tables (CRM, ERP, WMS). The same metadata-driven approach becomes even more valuable when one considers unstructured assets like images, sensor data.

Some of the useful metadata information for images:

• Camera/source ID

• Intrinsic camera parameters (e.g., resolution)

• timestamp

• batch size for processing

• preprocessing rules (compress, resize)

• storage path

• associated structured data (product ID, defect label)

  
  

This enables pipelines where these images can be further annotated (if required) or used for training/evaluation. The general process in such cases as well would be as shown in the image below. In general, we can expect that the metadata controls batch size and preprocessing. The unstructured nature of the images can also be used with some structured data to then help downstream analytics.

Practical Example:

When onboarding a new camera feed, only metadata entries would be updated (new connection path, storage containers). The ingestion framework would remain untouched.

Conclusion

Metadata-driven ingestion shifts pipelines from code bound restrictions to being completely configuration driven. It reduces operational overhead, increases auditability, and accelerates onboarding new systems and engineers. More importantly, it lays the foundation for scalable and resilient architectures, across both structured, semi-structured and unstructured data.

If your data engineering ecosystem still relies on hardcoded ingestion logic, metadata driven design is a transformative step that your organization needs.

Further References

• Microsoft Fabric blog - Playbook for metadata driven lakehouse implementation in Microsoft Fabric

• Databricks Technical Blog - Metadata driven ETL Frameworks in Databricks (Part - I)

• Medium article - Building a scalable metadata-driven data ingestion framework

• MathCo article - Reducing pipeline development: A metadata-driven approach to data ingestion