Your teams don’t need another abstract cloud reference diagram. They need a production-ready Databricks Lakehouse that is scalable and ready for real workloads across analytics and business intelligence, with room to grow into more advanced machine learning. A well-designed Databricks architecture can turn your data platform from being just another tool into the backbone of your modern data estate. 

Tenjumps helps you design and implement a Databricks Lakehouse that runs on AWS or Azure and fits cleanly into your existing cloud storage strategy. We align control plane and compute plane choices with how your organization actually works, so the environment is easier to govern and evolve over time.

We focus on the practical details—how workspaces are structured and how Delta Lake tables are modeled—so data teams can move quickly. Unity Catalog configuration is built into that work, giving security and compliance teams a foundation they can trust.

• Design a medallion-style Lakehouse on Delta Lake that supports batch and real-time workloads for BI and data science.

• Implement governance using Unity Catalog on day one so that access control and lineage are part of the architecture, not bolted on later.

• Apply Databricks well-architected principles for workspaces and networking to avoid painful rework as adoption grows.

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Download the Databricks Lakehouse Architecture Checklist

When a Lakehouse Architecture partner makes sense

Some organizations come to Databricks seeking a greenfield platform. Others already have a Databricks workspace or two running, but the environment grew organically and now feels messy—there may be duplicated datasets and inconsistent schema design, with unclear ownership and no single view of who has data access to what. In either case, a deliberate Lakehouse architecture is the difference between simply having Databricks and actually having a modern data platform that can scale.

You’re a good fit for a Databricks Lakehouse architecture engagement if:

• You are standing up Azure Databricks or Databricks on AWS for the first time and want a clean landing zone for analytics and AI.

• You already use the Databricks platform, but clusters and workspaces have evolved into a patchwork of data pipelines that is hard to govern or optimize.

• You are planning major AI or data science initiatives, or you need real-time analytics, and suspect your current data lake and data warehouse mix can’t reliably support them.

• You are focused on foundational architecture that multiple teams will reuse, not one-off implementations.

A Lakehouse architecture differs from traditional data warehousing in a few critical ways. It combines data lake flexibility with warehouse-style reliability and uses open-source formats via Delta Lake so you can support end-to-end workloads from data ingestion through dashboards. It’s also designed so that the same governed foundation can serve downstream applications instead of forcing you to maintain separate stacks for each need.

Without a clear reference architecture, teams quickly run into issues from duplicate tables and conflicting metrics to overlapping ETL jobs and unpredictable pricing for compute resources.

How Tenjumps designs Databricks Lakehouse architectures

Tenjumps treats Databricks architecture as an engineering discipline. We start with your use cases and constraints—regulatory requirements, existing data warehouse or data lake technologies, target SLAs—and design a Databricks Lakehouse that can serve those needs over the full life cycle of your data products.

From vision to reference architecture

The engagement begins with structured discovery across your current data estate and top-priority use cases. We inventory current platforms, datasets, and data pipelines across cloud storage and databases, with a clear view of key legacy systems that still matter day to day. We then identify the BI reports and machine learning models that will rely on the Databricks workspace, along with any critical web application backends that depend on consistent data. Finally, we clarify compliance and data governance expectations across business units, including how data management responsibilities are split today, so the future model doesn’t fight your org structure.

From there, we define a target Databricks architecture. We decide how many workspaces you actually need and how they relate to each other, outline how Unity Catalog will be organized, and shape the medallion architecture layers. We also map how data flows from ingestion into analytics so that teams know exactly where each workload should live and how it should move through the Lakehouse.

Factory-Style architecture blueprinting

Rather than designing each environment as a one-off, Tenjumps uses a set of reusable patterns and accelerators.

These include standardized workspace and metastore layouts aligned to departments or domains, so ownership and boundaries are clear. They also cover table, schema, and naming conventions for Delta Lake, which makes datasets easier to govern and reuse over time. In addition, we rely on repeatable patterns for connecting to AWS or Azure cloud storage and configuring networking, with a clear approach to securing the control plane–compute plane links.

This factory-style approach leads to faster design cycles and more predictable implementations. It also creates a cleaner handoff to your internal data engineering and platform teams, who inherit an environment built on familiar, documented patterns.

Design principles we apply

Across cloud providers and industries, Tenjumps anchors Databricks architecture on a small set of practical principles. We align designs with Databricks’ well‑architected pillars so that reliability and performance are balanced with security and cost.

We also design for governed‑by‑default operation, using Unity Catalog and well‑defined permissions, so access decisions are straightforward. On top of that, we prioritize scalable patterns—such as serverless options for Databricks SQL and carefully tuned autoscaling clusters—that allow compute resources to grow or shrink with demand and without constant manual intervention.

Core building blocks of a Databricks Lakehouse

A successful Databricks Lakehouse should be a set of tightly connected components that together support end‑to‑end data processing, analytics, and AI.

Storage and Delta Lake foundation

At the storage layer, Databricks uses your cloud storage—such as Amazon S3 or Azure Data Lake Storage—as the source of truth for persistent data. Delta Lake sits on top of that cloud storage, providing ACID transactions and schema enforcement so that you can treat your data lake as a reliable data platform, with time travel available when you need to look back at previous table versions.

With a well-structured Delta Lake, you can:

• Store structured and unstructured data in a single Lakehouse.

• Run ETL and ELT pipelines with Apache Spark and Databricks SQL against the same Delta tables.

• Keep historical versions of datasets for debug, audit, and model retraining needs.

Medallion layers: Bronze, silver, gold

Databricks recommends organizing data into medallion architecture layers: Bronze for raw ingestion, Silver for cleaned and joined data, and Gold for business-ready tables. Tenjumps adapts these patterns to your domains so that each layer has a clear purpose and schema standard and data engineers know where each type of transformation belongs.

This structure supports well-understood data quality expectations at each layer. It also enables faster builds of dashboards, data science notebooks, and downstream APIs, since they can all pull from a consistent set of Gold datasets.

Workspaces, catalogs, and domains

On top of storage and tables, Databricks architecture hinges on how you structure workspaces and Unity Catalog. Tenjumps designs workspace and catalog layouts that map to your organization’s domains—for example, separating finance, operations, and customer analytics into different catalogs while sharing common reference data where it makes sense. This makes it easier to apply least‑privilege access control at the catalog and schema levels and to delegate workspace administration to the right teams without losing central oversight of the metastore.

Turning a blank Databricks workspace into a ready-to-build environment

Standing up a Databricks account and creating a Databricks workspace takes only a few clicks, especially on Azure Databricks. The hard part is turning that workspace into a governed, production‑ready environment that spans both development and testing, and then holds up under real production use. Tenjumps focuses on making that step repeatable so that you can apply the same approach every time a new environment is needed.

Environment and workspace design

We design environments so that:

• Each Databricks workspace has a clear role—such as development or staging—and connects to the right metastore and external data sources.

• Network and security settings limit how the control plane connects to the compute plane, often through private endpoints or VNet peering.

• Identity and group management integrates with your existing directory, allowing permissions to flow from established roles.

Delta Lake and table design standards

We establish table-level standards to keep data storage and access consistent over time:

• Naming conventions for catalogs, schemas, and tables that reflect domains and environments.

• Partitioning and file size guidelines so that Delta Lake tables stay performant and cost-effective as datasets grow.

• Schema patterns that support both reporting and machine learning—for example, designing fact tables and feature tables to share a common grain where it actually helps analysis.

Ingestion and transformation patterns

Data ingestion and processing are where architecture meets day-to-day work:

• For data ingestion, we use Auto Loader and streaming APIs to land data into Bronze Delta tables with explicit data quality checks, bringing in batch connectors where they make sense.

• For transformation, we rely on Apache Spark and Databricks Workflows to promote data from Bronze to Silver to Gold, following clear ELT patterns.

This results in a standardized approach to data pipelines that is easier to debug and extend as new use cases appear.

Unity catalog and governance-by-design

Unity Catalog is the cornerstone of data governance on the Databricks platform. It centralizes metadata, permissions, and lineage across all workspaces attached to a metastore. Tenjumps treats Unity Catalog as part of the core Databricks architecture.

Why unity catalog matters

With Unity Catalog in place, organizations gain a single control point for governance. They can manage data access with fine-grained privileges at the catalog, schema, table, view, and column levels and see lineage across data pipelines and jobs, so audits and incident investigations move faster. Policies for sensitive data, such as PII or financial datasets, are standardized in one central metastore, which makes it easier to enforce consistent controls across teams and workspaces.

Tenjumps’ unity catalog blueprint

We help you enable Unity Catalog within your Databricks account, attach workspaces to the right metastore, and configure managed storage locations so that the basics are set up correctly from day one. From there, we define roles for account admins, metastore admins, and workspace admins, ensuring that access control responsibilities are clear. We also design catalogs and schemas that mirror your organizational structures and data domains, which makes day-to-day permissions easier to reason about and less dependent on tribal knowledge. By embedding Unity Catalog into your Lakehouse architecture in this way, you get governance that can scale as the number of users, workspaces, and workloads grows.

Architecture best practices for scale, performance, and cost

A Databricks Lakehouse is only as effective as its day‑two operation. Tenjumps designs Databricks architecture with performance and resilience in mind from the start, baking in cost optimization so that the environment stays sustainable as usage grows.

Performance- and Cost-Aware Design

Key practices include choosing appropriate compute resources based on how your workloads actually behave, whether that means leaning on serverless options or using a mix of job and all-purpose clusters alongside right-sized Databricks SQL warehouses. It also means isolating noisy workloads so that one mis-sized ETL job does not starve interactive data science notebooks or production dashboards that business users rely on. On the storage side, Delta Lake tables are optimized with techniques such as Z-ordering, compaction, and careful partitioning so that query performance stays predictable as data volumes grow.

Supporting multiple workload types

A well-architected Databricks environment can host a wide range of workloads without fragmenting your stack. It supports business intelligence reporting through Databricks SQL and external tools such as Power BI while giving data teams the control they need over performance and cost.

The same platform can run data science and machine learning workloads that depend on reliable feature tables and consistent experiment tracking. It also handles real-time and near-real‑time data processing for streaming analytics and event-driven applications, which means operational use cases can live alongside traditional reporting. Rather than building distinct stacks for each of these needs, Tenjumps designs for reuse, letting curated Gold tables and shared features power multiple use cases without duplication.

Operational resilience

Finally, we embed operational safeguards directly into the design. Monitoring and alerting are set up on jobs, clusters, and data pipelines, tuned to your SLAs. Recovery strategies then lean on Delta Lake’s capabilities—using features like time travel, versioned tables, and idempotent processing—to handle failures gracefully.

From architecture sprint to end-to-end delivery

Tenjumps typically structures Databricks architecture engagements in a few clear phases.

Phase 1: Architecture assessment and target blueprint
You begin with an architecture assessment and target blueprint, a focused sprint that produces a concrete Databricks architecture design and roadmap tailored to your environment.

Phase 2: Pilot implementation and hardening
From there, you move into pilot implementation and hardening, applying the patterns to a well-chosen slice of workloads so that benefits show up quickly in day-to-day operations and teams can see how the new model behaves in practice.

Phase 3: Scale-out across domains
Once that is stable, the same patterns are scaled out across additional datasets and applications, then extended to more teams, turning the initial design into a platform-wide standard.

Throughout, Tenjumps works alongside your in-house data engineering, analytics, and platform teams so that the architecture is understandable and maintainable and stays aligned with your internal standards.

Request a Databricks architecture consultation

If you are evaluating Databricks for a modern data platform or already have a Databricks workspace that isn’t delivering the value you expected, an architecture consultation is the fastest way to get clarity.

In this session, Tenjumps will review your current data and analytics architecture through a Databricks lens, then outline a target Lakehouse architecture with clear roles for storage, Delta Lake, Unity Catalog, and workspaces. You’ll also leave with a prioritized roadmap that shows what to build first and what to refactor, along with a practical view of how and when to phase in more advanced use cases such as real-time analytics and AI.

Request Your Databricks Architecture Consultation

FAQ

How is a Databricks Lakehouse different from my current data warehouse or data lake?
A Databricks Lakehouse combines the flexibility of a data lake with warehouse-style reliability, which means you store data once in low-cost cloud storage and use Delta Lake for ACID transactions and schema enforcement. It also lets you serve everything from BI dashboards and ad hoc analytics to machine learning and real-time workloads off the same governed foundation, so data engineering and analytics teams can share curated datasets.

We already have Databricks. Do we still need an architecture engagement?
Yes, if the environment grew organically. Common signals include multiple workspaces with overlapping tables and unclear ownership, and Unity Catalog only partially adopted, forcing teams to duplicate ETL logic to get their work done. An architecture engagement focuses on reshaping what you already have—workspaces, catalogs, Delta tables, and pipelines—into a consistent Lakehouse architecture.

Do we have to rebuild everything to adopt medallion architecture?
Not necessarily. Many teams transition into medallion architecture in phases, keeping key source systems and existing pipelines in place. You introduce clearer Bronze, Silver, and Gold layers around high-value domains, then move more workloads to the new pattern over time.

How does Unity Catalog fit into all of this?
Unity Catalog is the governance backbone. It centralizes metadata and permissions across workspaces so that you aren’t managing access control and catalogs separately per project. In practice, that means cleaner roles, fewer one-off grants, and a single place to understand who can see what—critical for regulated industries and any team dealing with sensitive data. Without a clear Unity Catalog design, organizations often end up recreating access control logic in pipelines, BI tools, and notebooks — increasing risk and slowing delivery.

What decisions do we need to make about cloud (AWS vs. Azure) for the Databricks architecture?
From an architecture perspective, the big differences are around identity and networking, plus the native storage services you use (for example, S3 vs. ADLS). The core patterns—Delta Lake, medallion layers, Unity Catalog, and workspaces—stay consistent. During the engagement, we align choices such as VPC or VNet layout, private endpoints, and directory integration with your standards on the chosen cloud.

How opinionated will you be about workspaces, catalogs, and domains?
We will be strongly opinionated but still adaptable. The goal is to avoid every team doing things differently, because that is how data sprawl and governance problems start. We’ll propose clear patterns for how many workspaces you need and how catalogs and schemas map to domains, then show how environments such as dev, test, and prod should be separated, tuning those patterns to your scale and org structure.

Can this architecture support both BI and advanced analytics without creating new silos?
Yes. A well-designed Databricks Lakehouse explicitly plans for BI and ad hoc analytics, as well as data science and real-time use cases, to share the same curated tables and features. That means designing reusable Gold tables and feature datasets, enforcing naming and schema standards, and avoiding separate data copies that are scoped only to BI or only to ML.

How do you think about cost and performance as part of architecture, not just tuning?
Cost and performance start at design time. We look at workload types and SLAs, along with expected concurrency, to decide when to use job clusters versus all-purpose clusters and when serverless makes sense. Table layout, partitioning, and caching strategies are treated as architectural choices, not something we patch later after costs spike.

What if our teams are new to Databricks and Apache Spark?
That’s common. Part of the engagement is making patterns understandable and repeatable through documented reference architectures and workspace templates, as well as ensuring your team can follow Delta Lake table standards. We also provide example pipelines that engineers can copy and adapt, and we can work in a joint-pod model initially so that your team learns the Databricks way of doing data engineering while we deliver.

Can we use our existing tools (dbt, BI, CI/CD) with the Databricks architecture?
In most cases, yes. Architecting the Databricks environment includes planning how your dbt and BI tools plug into workspaces and clusters and how CI/CD systems and observability fit around them. The aim is an end-to-end platform that integrates tools into a coherent Lakehouse architecture.

What does success look like 6–12 months after implementing this architecture?
You should see a small number of well-structured workspaces and a clear Unity Catalog hierarchy, with consistent medallion layers for key domains. High-value dashboards and ML use cases run on shared, trusted tables, and new projects onboard faster because patterns are established. Conversations about Databricks shift away from questions about plumbing and wiring toward prioritizing which data and AI use cases to tackle next.