Databricks development and consulting services
Databricks development and consulting: lakehouse implementation on Delta Lake and Unity Catalog, Lakeflow pipelines and Spark jobs, migrations from Hadoop, legacy warehouses and the Hive metastore, Databricks SQL and AI/BI dashboards, machine learning and AI agents on Databricks, CI/CD and cost optimisation, with dedicated Databricks engineers for your team or a Databricks project delivered end to end.
What we build with Databricks
Lakehouse implementation on Databricks
Workspaces on Azure Databricks, AWS or Google Cloud set up in Terraform with Unity Catalog, raw, cleaned and business-ready layers in Delta Lake or Apache Iceberg tables, and development, staging and production separated by catalog and workspace.
Lakeflow pipelines and Spark jobs
Batch and streaming pipelines in Lakeflow pipelines, built on Spark Declarative Pipelines (formerly Delta Live Tables), with data quality expectations, ingestion through Lakeflow Connect and Auto Loader, and PySpark jobs scheduled in Lakeflow Jobs.
Unity Catalog and access control
Catalogs, schemas and grants managed as code, row filters, column masks and attribute-based policies for personal data, lineage and audit logs read from system tables, and Delta Sharing to give partners governed access without copying data.
Migrations to Databricks
Hadoop and Hive clusters, SQL Server, Oracle and Teradata warehouses and Azure Synapse workloads moved in stages, Hive metastore tables upgraded to Unity Catalog, and every table reconciled by row counts and totals before the old system is switched off.
Databricks SQL and AI/BI
SQL warehouses serving Power BI and Tableau, AI/BI dashboards, metric views that define each KPI once, and Genie, where business users ask questions in plain language over data and definitions the data team has curated.
Machine learning on Databricks
Feature engineering, training tracked in MLflow 3, models registered in Unity Catalog and served from Model Serving endpoints, for forecasting, scoring and recommendation models trained next to the data they use.
AI agents and retrieval on Databricks
Agents built with Agent Bricks or in code, retrieval over your documents with Databricks AI Search (formerly Vector Search), tracing and evaluation in MLflow, and applications on Databricks Apps with Lakebase, the managed Postgres database, holding their transactional data.
Databricks cost control
Spend broken down by job, warehouse and team from the billing system tables, then reduced with serverless or right-sized compute, cluster policies, auto-termination, Photon where it pays for itself, and automatic liquid clustering on large tables.
CI/CD with Declarative Automation Bundles
Jobs, pipelines and dashboards deployed from Git through Declarative Automation Bundles (formerly Databricks Asset Bundles) with GitHub Actions or Azure DevOps, PySpark code covered by unit tests, and service principals with federated credentials instead of personal access tokens.
Hire Databricks engineers
Dedicated Databricks engineers
Databricks engineers who join your team full time, work in your repositories, tracker and meetings, and report to your lead. You interview them; we carry the Ukrainian contract, payroll, invoicing and leave.
Databricks project delivery
A team that takes the Databricks project from scope to release: estimate, build, tests and deployment, with a technical lead on our side who owns the plan and the quality.
Databricks support and take-overs
An existing Databricks system taken over from another team or kept running: a read-only review and a written list of risks first, then fixes and new features in order of impact.
The dedicated team page explains how specialists join your team, and the outsourcing page covers project delivery, take-overs and how we charge.
Who works on your Databricks project
Databricks engineers
Lakeflow, Spark, Unity Catalog and platform set-up
Data engineers
Ingestion, pipelines and data quality
Analytics engineers and BI developers
Databricks SQL, dbt, metric views and dashboards
Machine learning engineers
MLflow, Model Serving and agents
Cloud and DevOps engineers
Terraform, networking and CI/CD
A lakehouse run like software
Workspaces, catalogs, grants, jobs and pipelines are all defined in code: Terraform for the platform, bundles for jobs and pipelines, and pull requests with tests for every change, so production is never edited by hand in a notebook and any environment can be rebuilt.
Cost and access are designed in from the first week: compute tagged per team with budgets and alerts, cluster policies that cap what can be started, personal data masked through Unity Catalog, and workspaces reached over private networking where your security policy requires it.
Other data and AI services we provide
AI and machine learning
AI and machine learning development services: forecasting, fraud detection, recommendation systems, computer vision, NLP and speech recognition, and MLOps, with dedicated machine learning engineers or project delivery.
Generative AI development
Generative AI development services: RAG over your documents, AI agents, chatbots, LLM features, document processing, fine-tuning, evaluation and voice agents, with dedicated AI developers or project delivery.
Data engineering
Data engineering services: ETL pipelines, Spark, dbt and streaming, warehouses and lakehouses on Snowflake, Databricks, BigQuery, Redshift and Microsoft Fabric, with dedicated data engineers or project delivery.
Data analytics and Power BI
Data analytics and Power BI services: dashboards in Power BI, Tableau and Looker, product analytics, A/B testing, financial and marketing reporting, with dedicated data analysts and BI developers or project delivery.
Database development
Database development and administration services: schema design, query tuning, migrations and backups on SQL Server, PostgreSQL, MySQL, Oracle and MongoDB, with dedicated database developers and DBAs or project delivery.
Computer vision development
Computer vision development services: object detection, visual inspection, OCR, video analytics, medical imaging and models on edge devices, with dedicated computer vision engineers or project delivery.
Data annotation
Data annotation and labelling services for AI: image, video, text, audio and LiDAR labelling, RLHF and evaluation data, with quality control on every batch, from dedicated data annotators or project delivery.
MLOps
MLOps services and consulting: ML platforms, CI/CD for models, model registries, serving and autoscaling, GPU infrastructure, drift monitoring and LLMOps, with dedicated MLOps engineers or project delivery.
Snowflake development
Snowflake development and consulting: warehouse design, Snowpipe and Openflow ingestion, dbt, migrations, Cortex AI, security and cost control, with dedicated Snowflake developers or project delivery.
Kafka and event streaming
Apache Kafka development and consulting: event-driven architecture, Kafka Connect and Debezium, Flink and Kafka Streams, Confluent Cloud, MSK and Event Hubs, with dedicated Kafka engineers or project delivery.
Workflow automation
Workflow and AI automation services: n8n, Make, Zapier and Power Automate flows, AI agents in workflows, CRM, finance and document automations, with dedicated automation developers or project delivery.
Questions about Databricks development
Which industries do your Databricks engineers work in?
Fintech, banking and insurance, for risk, fraud and regulatory data; e-commerce and retail, for customer, order and demand data; AI and data products built on the lakehouse; healthcare and life sciences, for clinical and research data under strict access rules; advertising and marketing technology, for high-volume event data; manufacturing and energy, for sensor and telemetry data; and enterprise SaaS, for product analytics.
Do you work with Azure Databricks and Databricks on AWS and Google Cloud?
Yes, all three. Unity Catalog, Lakeflow and the SQL warehouses work the same way on each; what changes is the networking, the identity set-up and the storage underneath, and those are written in Terraform for the cloud you use.
Can you move us from the Hive metastore to Unity Catalog?
Yes. An inventory of tables, jobs and permissions comes first, then tables are upgraded in groups, jobs are pointed at the new names and tested, and the old metastore stays read-only until every consumer has moved.
How quickly can Databricks engineers start?
When the right Databricks engineer is available, the start is gated only by your interview and the NDA and IP assignment. Otherwise we run a search, which typically produces candidate profiles within two to three weeks, and nobody starts until you have said yes.
How do we hire Databricks engineers through BigTree108?
Tell us the work, the seniority and the hours you need. We propose one or two people with their profiles, you interview them the way you would interview your own hire, and you sign one agreement with BIG TREE 108 LLC and receive one invoice a month.
Who owns the work they produce?
You do. Every specialist has a signed contract with BigTree108 that assigns all work product to the company, and our agreement with you assigns it onward. Code, designs and documents are delivered into your own repositories and tools, not kept where only we can change them.
Planning or fixing a Databricks platform?
Tell us your cloud, your workloads and what the platform has to deliver. You get an answer within one business day: a plan, a cost and governance review, or candidate profiles.