Databricks consulting, implementation, and managed services
BaySys Technology helps data teams move from fragile jobs, expensive clusters, and disconnected reporting into production Databricks platforms built for analytics, AI, and regulated operations.
BaySys Technology is positioned as an independent Databricks-focused consulting practice for teams modernizing analytics, engineering, governance, and AI-ready data platforms on Azure and AWS.
Service lines
Engage BaySys for a targeted assessment, a full implementation, or ongoing platform ownership across engineering, governance, reliability, and cost control.
Define the workspace, network, storage, catalog, and environment model before engineering work begins.
Build production Databricks workloads for batch, streaming, reporting, machine learning, and data products.
Move legacy warehouses, scripts, and reporting data marts into a governed lakehouse without losing operational continuity.
Configure Unity Catalog, access controls, auditability, and private networking for enterprise data teams.
Tune Spark, cluster policies, Photon, partitioning, storage layout, and job scheduling to reduce waste.
Keep your platform stable after launch with monitoring, incident triage, release support, and backlog delivery.
Azure and AWS
BaySys designs Databricks around the cloud you already operate. We account for identity, networking, object storage, DevOps, FinOps, and downstream analytics from the beginning.
Architected with Microsoft Entra ID, ADLS Gen2, Azure Key Vault, Azure DevOps or GitHub Actions, Private Link, and Power BI consumption patterns.
Architected with IAM, S3, KMS, VPC endpoints, Glue or Lake Formation integration where appropriate, CloudWatch, and QuickSight or warehouse consumption.
Delivery model
Review workloads, costs, data sources, security constraints, and current pain points.
Produce the target architecture, governance model, backlog, migration plan, and delivery estimate.
Build workspaces, data pipelines, catalogs, CI/CD, monitoring, and validated business outputs.
Support releases, performance tuning, incident response, and continuous optimization.
Planning tool
Use this lightweight estimator to frame a first conversation. The result is directional and should be validated against workload complexity, governance needs, and cloud configuration.
A focused architecture and implementation sprint for initial lakehouse setup, governance model, and first production pipelines.
Contact
Share a little context and we will follow up to discuss your Azure or AWS Databricks environment, migration goals, governance requirements, or managed services needs.