Transform your Databricks environment into a proactive intelligence hub
10+
years delivering data engineering AI ML
300+
senior engineers across data ai cloud
5x
faster partner onboarding, proven delivery
Our Services
Data in. Value Out.
From architectural strategy to production deployment, we cover the full spectrum of what it takes to make Databricks work at enterprise scale—so your teams can unlock the power of data and maximize your investment in the platform.
Proxet’s Solutions:
Delta Lake pipeline engineering
We design and deploy high-performance pipelines using Delta Lake to deliver reliable, scalable, and audit-enabled data foundations.
Unity catalog governance
We implement Unity Catalog to create a unified governance layer across all workspaces, standardizing access control, data lineage, and compliance in a trusted and transparent way.
Modular ingestion frameworks
We architect repeatable, modular ingestion patterns that standardize how new and unstructured data sources are onboarded into the Lakehouse, reducing integration time and technical debt.
Data quality & lineage tracking
We embed automated data quality checks and end-to-end lineage tracking directly into your pipelines, transforming raw data assets into audited commercial intelligence you can trust.
How we support ROI on your Databricks investment
Feature store buildout
We design and deploy Databricks Feature Store implementations that centralize feature engineering, eliminate duplication across teams, and accelerate model development cycles by giving data scientists reusable, production-quality inputs.
Real-time & streaming intelligence
We build structured streaming pipelines on Databricks that bring real-time signals into your models and dashboards, powering dynamic targeting logic, live anomaly detection, and sub-second decision systems at scale.
Cost optimization & platform tuning
We analyze your Databricks spend and compute utilization, identifying optimization opportunities and implementing cluster policies, autoscaling rules, and workflow restructuring that cut costs while sustaining performance.
OUR APPROACH
Expanding our Databricks platform expertise
Our senior engineers maintain active certifications and deep hands-on experience across every layer of the Databricks stack. We bring that expertise directly to your architecture decisions, ensuring you're building on proven patterns, not discovering pitfalls in production.
Evolve with the ever-changing data ecosystem
New data sources, regulatory requirements, and business priorities emerge constantly. Our team is skilled at integrating new inputs, adapting governance frameworks to compliance changes, and restructuring pipelines without disrupting the systems that already run your business.
Eliminating the gap between data and decisions
We architect Databricks environments with the end consumer in mind, building the pipelines, feature stores, and serving layers that put accurate, timely intelligence directly into the hands of the analysts, models, and applications that need it.
Accelerating your time to value
Our mission is to reduce the gap between your Databricks investment and measurable business outcomes. We move fast, cut through complexity, and deliver production-grade infrastructure that performs from day one—because we've solved these problems before and can anticipate the next curve.
Selected case study:
Powering predictive intelligence at scale through Databricks big data workflows for e-commerce data ecosystem
Challenge:
The client’s tangled legacy architecture and highly unreliable data signals were unraveling trust with enterprise-level clients, yielding frequent platform complaints. Data ingestion processes were slow with manual schema mapping and scattered pipelines, all of which capped the speed at which new data providers could be integrated.
There were also the unchecked infrastructure costs. Tracking millions of brands across years of time-series data caused storage bloat and computing costs that threatened to scale linearly with data volume. Internal engineering teams lacked the specialized big data and engineering expertise to perform the raw web scraping and production-grade data logistics at scale.
Solution (Proxet + Databricks):
- Core architectural overhaul: Proxet introduced Databricks to the organization for the first time, migrating processing logic out of unstable, unstructured pipelines into a unified environment.
- Deterministic language and auditability: Engineered a systematic ingestion framework that preserved original data at every step, creating an audited data ecosystem where historical tracking results could be rerun without data loss.
- Strategic cost optimization: Analyzed and tuned the costliest parts of the processing pipeline, ensuring that storage and compute stayed decoupled from expanding data volumes.
- Rapid data deployment: Developed specialized headless data-sharing pipelines designed to stream raw social media and marketplace analytics files directly to institutional customers.
Outcome – maximizing valuation, speed, and market differentiation.
- 5X faster partner onboarding: Standardizing ingestion workflows through Databricks accelerated the onboarding speed of external data providers fivefold, radically expanding product data coverage.
- Data trust at scale: Restored enterprise client trust by delivering an audited golden copy of data, successfully withstanding the client’s rigorous scrutiny.
- New stream of income through commercial monetization: Successfully turned raw social retail signals into a standalone intelligence product, rapidly positioning the new TikTok data pipeline as the firm’s fastest-growing source of revenue.
- Product evolution: Transformed a disorganized startup tool into a dominant, high-margin SaaS analytics market leader equipped with proprietary predictive scoring and advanced machine learning foresight.