50+
Databricks-certified professionals
Databricks
Turn scattered data into governed analytics and AI on Databricks
Data spread across warehouses, applications, and files slows every analytics and AI initiative, and consolidating it on Databricks pays off only when access rules, quality checks, and cost controls come with it. As a Databricks partner, Simform builds on Lakeflow, Unity Catalog, and Agent Bricks so every workload stays reliable, governed, and traceable to an owner.
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AI-driven IP and solution accelerators
Certified Databricks expertise behind every engagement
As a Bronze tier partner in the Databricks partner network, Simform brings Databricks training, platform access, and partner technical support into every engagement. Our teams build on the platform as Databricks ships it and adopt new capabilities such as Lakeflow and Agent Bricks once they have proven out in practice. Your architecture keeps pace with the platform without your production environment becoming a testing ground.
Databricks engineering across every layer of the Data + AI Platform
The Databricks Data + AI Platform puts ingestion, governance, analytics, and AI on one foundation. Each product still needs engineering decisions before it can carry production work: how catalogs map to the business, which quality rules stop a bad load, how compute is sized, and how an agent is evaluated before release. Simform makes those decisions with your team and takes each layer into production, starting with the workloads the business is waiting on.
Lakehouse Migration with Lakebridge
Retire legacy warehouses, Hadoop clusters, and ETL estates without breaking the reports that depend on them. Simform uses Lakebridge to convert the code that translates cleanly and to validate migrated data against the source. We then rewrite the logic that would otherwise carry old workarounds onto the new platform. Lakehouse Federation keeps legacy sources queryable while workloads move in waves, so business teams work from the same reports throughout. The legacy warehouse is then retired on a schedule finance can plan around.
Estate assessment and wave plan sequenced by business dependency
Lakebridge-led code conversion and data validation, with targeted rewrites
Federated access to legacy sources and reconciliation before each cutover
Data Engineering with Lakeflow
Give analytics and AI teams data that arrives complete, on schedule, and already validated. Simform uses Lakeflow Connect to bring change data from enterprise databases and applications into governed bronze, silver, and gold layers. Transformations run on Spark Declarative Pipelines, with quality expectations written into each step. We orchestrate with Lakeflow Jobs under version control and CI/CD, and use Genie ZeroOps to surface schema drift and failures. A bad load is stopped before it reaches a dashboard or a model.
Change data capture from enterprise databases and applications through Lakeflow Connect
Declarative pipelines with quality expectations and recovery behavior built in
Orchestration, CI/CD, and pipeline monitoring for production operations
Real-Time Streaming on Databricks
Act on a stock-out, a suspicious payment, or a failing sensor while a response can still change the outcome. Simform uses Real-Time Mode in Spark Declarative Pipelines to process events in the same pipelines and Unity Catalog tables as your batch data, so live and historical views agree. Where a decision needs instant reads, we serve results through Lakehouse//RT instead of standing up a separate serving store. Each stream gets a latency target set by the decision it feeds, so always-on compute is reserved for the cases that pay for it.
Streaming and batch unified in one pipeline and one set of governed tables
Latency targets and compute sizing tied to the decision each stream feeds
Low-latency serving for operational dashboards and applications
Unity Catalog Governance for Data and AI
Know who can see which data, where it came from, and which agent or model used it, across every workload on the platform. Simform uses Unity Catalog to structure catalogs and ownership around how your business is organized. Tag-based policies then keep sensitive data protected as new tables and columns arrive. We bring existing workspaces under Unity Catalog in sequence, and use Unity Gateway to put agents and models under the same permissions and spend controls as your data. Lineage and audit trails build compliance evidence through normal work.
Catalog, schema, and ownership design mapped to business domains
Tag-based access policies, classification, and lineage configured from the first workload
Workspace migration to Unity Catalog and governance of AI assets through Unity Gateway
Analytics and BI on Databricks Lakehouse
Give business teams one trusted figure for every metric, and answers without waiting in an analyst’s queue. Simform defines each metric once in Unity Catalog Business Semantics and builds AI/BI dashboards and Genie spaces on top of it. A dashboard, a plain-language question, and a Power BI or Tableau report all return the same figure. We size and tag serverless SQL warehouses by team, so analytics spend stays traceable as adoption grows.
Governed metric definitions shared across AI/BI, Genie, and existing BI tools
Self-service dashboards and Genie spaces set up for business teams
SQL warehouse sizing, tagging, and cost reporting by team
AI Agents and Models with Agent Bricks
Put agents and models into production on the data your business already trusts for reporting. Simform uses Agent Bricks to build agents grounded in governed lakehouse data, choosing the model for each task from OpenAI, Anthropic, Google, or open source to balance cost and quality. Every agent is evaluated with LLM-as-judge scoring and human review before release and traced in MLflow once live. We deploy through Databricks Apps under Unity Catalog permissions, so each agent goes live with an owner, enforced access limits, and an audit trail.
Agents built in code or with Knowledge Assistant and Supervisor Agent, connected to your systems through MCP
Pre-release evaluation with LLM-as-judge, custom metrics, and human feedback
Deployment through Databricks Apps and Model Serving, with Lakebase for agent memory
What enterprises build on Databricks with Simform
Databricks depth across your data estate, backed by enterprise engineering scale
Simform pairs a certified Databricks team with engineering breadth across cloud, data, and applications, so each platform decision fits the systems around it and holds up once your own teams take over.
Multi-platform architecture
We integrate Databricks with Snowflake, Microsoft Fabric, cloud services, operational systems, and the BI tools already in use, with Snowflake Select Tier and Fabric Featured Partner status behind that work, so each workload runs on the platform that suits it.
Azure Databricks inside your Microsoft estate
For organizations standardized on Azure, Simform delivers Azure Databricks within the identity, networking, and security already in place. The platform work is backed by Azure Expert MSP status and Microsoft Solutions Partner designations across Data & AI, Infrastructure, Security, and Digital & App Innovation.
Co-engineering delivery model
Our co-engineering model puts your data and platform teams in every architecture decision, so the Databricks platform you take over is one your engineers helped design and can extend on their own.
AI engineering on governed data
Experience building machine learning models, retrieval pipelines, and AI agents directly on lakehouse data lets your teams move from analytics to AI without a second platform, or connect Databricks to the wider enterprise AI stack where a workload calls for it.
Production ownership discipline
We hold data projects to product-team standards, with CI/CD, automated testing, observability, and post-launch ownership in every engagement, so the platform keeps performing long after the first workloads go live.
Accelerators from Simform’s Innovation Lab
Simform’s Innovation Lab builds reusable assets that delivery teams bring into Databricks engagements, including pipeline templates, governance patterns, and AI starter kits. Projects start from proven building blocks instead of a blank workspace.
Trusted by the world's leading companies
“After researching and qualifying several software development service options for the development of a cross-platform application, we fortunately selected Simform as our partner. This turned out to be a very good decision overall, as their performance has been outstanding. The complete engagement from the Simform staff is remarkable. Their team diligently assesses and executes in a very competent manner.”
Sonny Cutwright, Operations Manager
Bon Appetit
“Simform rearchitected and modernized our subscription management & billing platform as part of the multi-phase development initiative. The team quickly understood our business requirements and delivered a modern, modular architecture aligned with our scaling needs. Without disrupting our operational workflows, Simform handled complex data migration with precision and supported our legacy modernization journey with seamless ERP integrations.”
Robel Yemane, Head of Engineering
Privilee
“Simform was an invaluable partner in our system modernization, moving beyond analysis to deliver true product engineering advisory. They analyzed complex legacy workflows into clear, validated, developer-ready user stories. The clarity and strategic consistency they introduced was foundational, directly influencing our cloud architecture and product design. Their work effectively accelerated our discovery phase and time to market.”
Director of Engineering
Datamark
“We partnered with Simform to centralize our preclinical research data. They understood technical data challenges and scientific workflows. They helped us build a platform for data ingestion, OCR-based extraction, standardization, analysis, and visualization. Our researchers can use natural language or a visual query builder to analyze complex datasets, while real-time charts and statistical outputs speed up discovery. We liked the ability of the team at Simform to combine strong data engineering capabilities with practical analytics experience.”
Data and Analytics Manager
Preclinical Research Analytics Platform
“The cloud migration was a great sucess. Very satisfactory, seamless and increased our productivity. The most impressive part about Simform is their dynamic and well-versed team. Anytime there was a concern, we were able to communicate and have it rectified immediately.”
President
Trusted Community Services Organization
“Simform led the discovery phase for our digital infrastructure overhaul, bringing a mix of deep technical expertise and a truly collaborative spirit. They took our complex requirements and turned them into a solid product blueprint and a clear, prioritized roadmap. Everything stayed on track thanks to their structured project management. The level of strategy and architecture they brought to the table gave us the certainty we needed to take the next steps.”
Department Manager
Eye Recommend
“Our overall experience with Simform was very positive. They worked as an end-to-end technology partner and integrated well with our engineering and operations teams. This was not just about building a mobile app; it also required careful thinking around data quality, workflows, and long-term system design. Simform brought a practical, product-minded approach and designed an Azure Data Factory pipeline to clean and organize data before it reached the app and admin dashboard.”
Marketing Manager
Patient-Facing Digital Application & Data Platform
“We’re very satisfied with Simform as our engineering partner. We wanted a platform to support the full lifecycle of our field inventory and ease operations for field reps, hospitals and analysts. They translated our complex requirements into an easy-to-use solution and handled development from UI/UX to backend and DevOps. The app works seamlessly across user groups and has a first-class, consistent and simple interface.”
Brad Spataro, Commercial Operations Manager
Evergen
Case studies
Discover the many ways in which our clients have embraced the benefits of the Simform way of engineering.
Vending operations provider cuts stockouts 30% with unified data platform on Databricks
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AI Training Services Provider Enhances Workforce Operations with Databricks
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Sports Brand Boosts ROI 40% with Customer Data Platform
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Automotive Improves Data Accuracy 80% with Data Platform
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Frequently Asked Questions
Databricks runs on AWS, Microsoft Azure, and Google Cloud. We design the lakehouse for the cloud where your data and applications already live, so adopting Databricks doesn’t force a second migration. The governance, pipeline, and cost practices described on this page carry across all three.
Yes. An existing platform that has grown expensive, slow, or hard to govern is a common starting point. We review how workloads are designed, how reliable the pipelines are, how access is controlled, and where compute is being spent, then re-engineer the areas holding the platform back. Where workspaces still sit outside Unity Catalog, bringing them under it is usually the first step.
Databricks bills for the compute each workload consumes, so most of the cost is set by design decisions about pipelines, clusters, and SQL warehouses. We size compute to each workload, tune queries and schedules, and apply tags and budgets from the start. Usage reporting then shows spend by team and use case, which keeps the bill explainable as adoption grows.
It depends on how many source systems are involved, how much transformation logic they carry, and how much of that logic should be redesigned along the way. Every engagement opens with an assessment that produces a sequenced roadmap with milestones specific to your estate. A first wave of workloads moves early, which proves the approach and the timeline before the larger ones follow.
Yes. Most enterprises already run more than one data platform, and Simform is also a Snowflake Select Tier Partner and a Microsoft Fabric Featured Partner. We work out which workloads belong on Databricks, how data moves between it and the platforms already in place, and where one governed copy can serve several tools.
Your team knows the business and its data better than any partner will. The gap is usually capacity and pattern experience: migrating without disrupting reporting, setting up governance that scales, and avoiding design choices that inflate compute costs later. Our co-engineering model keeps your engineers in every architecture decision while we bring certified Databricks specialists and patterns from earlier platform builds.
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Hiren Dhaduk
Creating a tech product roadmap and building scalable apps for your organization.
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