We build with Databricks
We use Databricks to build unified data engineering, analytics, and machine learning pipelines that support your business goals. From lakehouse architecture and notebook workflows to job orchestration and cost management, we create products designed to evolve with your needs.
DISCUSS YOUR PROJECTLakehouse Architecture
We combine lake storage with warehouse-style data management.
Collaborative Notebooks
Teams share Python, SQL, Scala, and R workflows together.
Managed Spark Clusters
We run production Spark jobs without infrastructure overhead.
BENEFITS OF DATABRICKS technology
We use Databricks to unify data engineering, analytics, and machine learning on one lakehouse platform.
BUILD
[01]- Design lakehouse schemas
- Build Delta Lake tables
- Configure Spark clusters
- Set up notebook workflows
ENGAGE
[02]- Process large-scale ETL
- Run shared notebooks
- Orchestrate data pipelines
- Track ML experiments
GROW
[03]- Scale Spark workloads
- Deploy production ML models
- Optimize storage costs
- Extend to new data sources
Our Databricks Technology Stack
We combine Databricks with Delta Lake, MLflow, and cloud storage like S3, ADLS, and GCS for lakehouse storage, experiment tracking, and BI integration with Power BI and Tableau.
Custom Databricks development company
With our Databricks development services, we build unified lakehouse platforms for large-scale ETL pipelines, collaborative data science, and production machine learning, tailored to your boldest business goals. Having years of experience with Databricks, our engineers harness its full potential to deliver reliable, scalable data platforms across industries and company sizes. Whether it's building a new lakehouse from scratch or modernizing legacy data warehouses and fragmented pipelines, we design architectures that keep data engineering, analytics, and ML teams working from the same platform. Our range of Databricks development services spans consulting, architecture design, pipeline development, and ongoing optimization. We work as your technical partner and ensure complete transparency and comfortable communication throughout the process, bringing deep expertise in Spark, Delta Lake, and MLflow to data modeling and problem-solving. All this to make sure your data platform stays performant and cost-efficient as usage grows, and moves your business forward.
OUR DATABRICKS SERVICES
We build, modernize, and support Databricks data pipelines around your product goals.
Your roadmap is growing faster than your team. Add senior engineering capacity and deliver more without sacrificing quality.
Large-scale ETL pipelines and Delta Lake architectures built around your data sources.
Shared notebooks and workflows that keep data science and analytics teams aligned.
Model development, experiment tracking, and production deployment pipelines built with MLflow.
Migrate legacy data warehouses to a unified lakehouse or optimize an existing Databricks setup.
Meet our Databricks experts
A curated selection of senior specialists currently available for new engagements.
We Turn Technology Into Results
Partner with a team that blends technical precision, creative design, and business insight. We’ll help you launch, scale, and dominate your digital niche.
Frequently Asked Questions
Common questions about how we use Databricks and what it can bring to your project. Have a specific requirement?
How does SoftDoes use Databricks?
We use Databricks to build unified lakehouse platforms for large-scale ETL pipelines, collaborative data science, and production machine learning. We design the architecture and cluster configuration around your data volume and access patterns.
What types of projects do you build with Databricks?
We build ETL pipelines, real-time analytics platforms, collaborative data science workspaces, and MLOps pipelines that take models from experimentation into production.
Can Databricks integrate with our existing cloud infrastructure?
Yes. Databricks runs on AWS, Azure, or GCP and connects natively with cloud storage like S3, ADLS, and GCS, as well as your existing identity and networking setup.
Do you use Delta Lake for data storage?
Yes, Delta Lake is our default storage layer on Databricks. It brings ACID transactions and schema enforcement to data lake storage, which keeps pipelines reliable at scale.
How do you handle machine learning workflows on Databricks?
We use MLflow for experiment tracking, model versioning, and deployment, so data science teams can move from notebooks to production models without switching platforms.
Can you migrate our existing data warehouse to Databricks?
Yes. We assess your current data warehouse and pipelines, then migrate them to a lakehouse architecture with minimal disruption to existing reports and dashboards.
How do you decide whether Databricks fits a project?
We look at your data volume, existing cloud provider, team skillset, and analytics and ML requirements, then confirm Databricks is the right fit before starting development.
