How a medallion architecture pipeline unified four data domains into a single analytics-ready lakehouse. And what separates Databricks partners who deliver from those who demo.
If you are evaluating Databricks implementation partners, you already know the platform is powerful. The question is not whether Databricks can handle your data engineering needs. It almost certainly can. The question is whether the partner you hire has actually built production pipelines with it, or whether they have a certification and a slide deck.
At Celerik, we are a Microsoft Solutions Partner for Data & AI. We built our own internal QMS Data Engineering Pipeline on Databricks Delta Live Tables before deploying the same architecture for clients. Here is what a real Databricks implementation looks like, and the questions you should ask any partner before signing.
Many companies evaluating Databricks implementation partners are not yet clear on what Databricks does versus what other tools in their stack already handle. Here is a plain-language breakdown.
Databricks is a unified data platform built on Apache Spark. It brings together data ingestion, transformation, governance, and analytics in a single environment. Instead of stitching together separate tools for each stage of your data pipeline, you build, run, and monitor everything in one place.
The components that matter most for implementation work:
Delta Lake. The storage layer. Delta Lake sits on top of cloud storage (Azure, AWS, or Google Cloud) and adds ACID transactions, schema enforcement, and time travel. You can query data as it existed at any point in the past. For audits, compliance, and debugging, this matters enormously.
Delta Live Tables (DLT). The pipeline framework. You define your tables declaratively and DLT handles orchestration, dependency resolution, error handling, and data quality enforcement automatically. This is what makes medallion architecture practical at scale.
Unity Catalog. The governance layer. Unity Catalog manages access controls, data lineage, and auditing across all data assets. Every column, table, and pipeline is tracked. You always know where a number came from.
Auto Loader. Incremental file ingestion. Auto Loader monitors cloud storage for new files and processes them automatically with schema evolution and checkpoint management. No manual ingestion triggers.
How Databricks compares to simpler tools:
A basic ETL script handles one source but breaks when schemas change or volumes grow. Power BI connects to existing data but does not clean or transform it at scale. Databricks handles the hard part: getting data from multiple messy source systems into a clean, validated, governed state that your BI tools can consume. For companies with data across more than two or three systems, Databricks is built for exactly this complexity.
Medallion architecture is the standard pattern for organizing data in a Databricks lakehouse. It structures data into three layers, each with a specific purpose:
Bronze. Raw data as it arrives from source systems. No transformations. The goal is to land data quickly and preserve the original state for reprocessing if needed. Bronze tables use Databricks Auto Loader for streaming ingestion from cloud storage volumes.
Silver. Cleansed, validated, deduplicated data with business logic applied. This is where your data engineering partner earns their fee. Silver transformations handle type casting, date normalization, deduplication by business keys, effort calculations, status derivations, and data quality enforcement. Rows that fail quality checks get dropped or flagged depending on criticality.
Gold. Analytics-ready materialized views for consumption by BI tools. Gold tables are optimized for the queries your stakeholders actually run. They pull from Silver and present data in the format your dashboards need.
The key insight of medallion architecture: raw data and clean data are separated by design. If something goes wrong at the Silver layer, you reprocess from Bronze. You never lose the original data. You never have to re-extract from source systems.
We built this for ourselves. The Celerik QMS Data Engineering Pipeline is an internal quality management system that consolidates engineering and delivery data from four operational domains into a single Databricks lakehouse.
The challenge:
Celerik tracks quality metrics across pull requests, code commits, issue tracking, deployments, and work items. Each domain lives in a different system with a different data schema. Data was scattered, manually assembled, and inconsistent. Leadership could not answer cross-domain questions like "how do deployment failure rates correlate with sprint overload?" or "which issue types take the longest to close?" without significant manual effort. The data existed. The infrastructure to connect it did not.
What we built:
A full medallion architecture pipeline using Databricks Delta Live Tables across four data domains.
Extraction layer. Four Python notebooks pull data from the Celerik API Management platform: pull requests and commits, issues, deployments, and work items. Each notebook handles pagination, authentication, and retry logic. Data lands as JSON files in Unity Catalog volumes under the qms.qms_etl catalog.
Bronze layer. Auto Loader picks up new files from each volume automatically. Bronze tables use streaming ingestion with schema evolution and rescue columns for malformed records. No manual triggers. No missed files.
Silver layer. Delta Live Tables apply domain-specific business logic to each Bronze table:
Every Silver table enforces data quality expectations with DROP or WARN strategies based on criticality. Bad data is caught at the Silver layer before it reaches dashboards.
Gold layer. Materialized views serve analytics-ready data for each domain: PR analytics, commit analytics, issue analytics, deployment analytics, and work item analytics. Cross-domain views link PRs to issues and commits to PRs, enabling queries that span the full delivery lifecycle.
Tech stack: Databricks Delta Live Tables, Apache Spark, Delta Lake, Unity Catalog, Python, SQL, Power BI
The results:
This architecture now powers Celerik's internal delivery reporting. The same pattern deploys directly to client engagements.
Here is what separates partners who deliver from those who demo.
Anyone can spin up a Databricks notebook and run a demo. Ask for case studies with specific data volumes, domain complexity, and measurable outcomes. How many source systems? How many tables in production? What data quality issues did they encounter and how did they resolve them?
Databricks certifications show someone passed an exam. Production deployments show someone built something that runs every day.
The medallion pattern is standard, but implementation details matter enormously. Ask how they handle deduplication in Silver. How do they manage schema evolution in Bronze? What happens when a source API changes its response format? How do they enforce data quality without blocking the pipeline?
A partner who has built Bronze-Silver-Gold pipelines in production will have specific, concrete answers. A partner who has not will give you theory.
Data governance is not optional for enterprise implementations. Unity Catalog manages access controls, data lineage, and auditing. Your partner needs to understand how to configure it properly from day one, not retrofit it after the pipeline is built.
Ask: how do they control which teams see which tables? How do they document data lineage? How do they handle personally identifiable information or sensitive business data?
Databricks pipelines start with data extraction. Your data lives in APIs, databases, flat files, and SaaS platforms, each with different authentication, pagination, schema, and rate limiting behavior. Your partner needs real integration experience, not just Spark knowledge.
Ask about specific source systems they have integrated. How do they handle API pagination? What happens when a source system goes down mid-extraction? How do they manage API credentials securely?
Most real-world implementations span multiple data domains. Your partner needs to manage interdependencies between pipelines, handle cross-domain joins at the Gold layer, and keep four or more pipelines running reliably without one failure cascading into others.
Single-domain POC experience does not prepare a partner for this. Ask whether they have built multi-domain pipelines and how they manage pipeline dependencies.
Databricks pipelines often handle sensitive data. Your partner should build with governance built in:
Celerik is a Microsoft Solutions Partner for Data & AI. We have built Databricks pipelines in production, including the QMS system described above.
What we bring:
The real value of a Databricks implementation is not the pipeline. It is the questions it makes answerable.
Before the Celerik QMS pipeline existed, cross-domain questions were unanswerable without hours of manual work. After the pipeline: deployment failure rates correlated with sprint load. Issue resolution times broken down by type and team. Code churn tracked per PR and linked to downstream defect rates.
None of those questions required new data. They required the existing data to be unified, clean, and queryable.
That is what a properly implemented Databricks pipeline does. It turns data you already have into decisions you could not previously make.
A Databricks engagement does not have to start with a full multi-domain pipeline. Start with one domain, prove the architecture, then scale.
Here is how we typically approach it:
The goal is working data in production quickly, not a six-month architecture exercise.
Ready to build a data foundation that actually works?
Celerik is a Microsoft Solutions Partner for Data & AI specializing in Databricks implementation, data engineering, and custom software development. Based in Colombia with US-aligned operations, we help mid-market and enterprise companies build scalable data foundations that power better decisions.
