/ Databricks
Databricks that reaches production
ACI Infotech is a Databricks consulting partner with certified architects across data engineering, machine learning, and platform administration. We design lakehouses on Delta Lake, govern them with Unity Catalog, and tune Spark until the bill makes sense. Then we run the platform, around the clock, under SLAs.
- Databricks consulting partner
- 40+ lakehouse implementations
- Certified architects
- ISO 27001
40+ lakehouse implementations
Certified on data engineering, ML, and platform admin.
Built for day two
A lakehouse designed for
day two.
Design for production from the first workspace: costs profiled, files compacted, governance in place before the first workload ships. We have taken enough lakehouses live to know where the walls are, and we route around them in the design.
30%Reduction in data latency
A governed lakehouse across 600+ retail locations, with zero downtime through the cutover.
Read the case study
/ What we do on Databricks
One lakehouse. Six disciplines.
01
Lakehouse architecture
Medallion layers on Delta Lake, designed around the workloads you actually run. One governed copy of the data serves BI, streaming, and ML, and the small-file problem gets solved in the design, not discovered in the bill.
Delta LakeUnity CatalogPhotonApache Iceberg
Delta Lake02
Unity Catalog governance
Access control, lineage, and discovery in one place, across every workspace and cloud. Who touched what becomes a query. Auditors notice the difference, and so do the engineers who stop guessing.
Unity CatalogAccess policyLineageAudit logging
03
MLflow and MLOps
Experiment tracking, model registry, feature stores, and retraining pipelines that keep models honest after launch. The GenAI work rides the same spine through Mosaic AI, so governance is built once.
MLflowFeature storesDatabricks Mosaic AIModel registry
MLflow04
Spark and cost optimization
Cluster policies, Photon, and query tuning that pull real money out of the compute line. Most estates we audit are paying for clusters nobody sized and jobs nobody profiled. We profile them.
SparkPhotonCluster policiesFinOps baseline
Spark05
Migrations to the lakehouse
Hadoop, Teradata, Oracle, and legacy warehouse estates moved in staged waves. Parallel runs prove parity against the old numbers before anything gets switched off. Nothing retires on faith.
HadoopTeradataOracleParallel runs
Hadoop
Teradata06
Managed Databricks operations
The platform stays fast, governed, and patched after go-live. 24/7 monitoring under SLAs, upgrade management, and a monthly cost report finance can read without a translator.
24/7 NOCSLAsUpgrade managementCost reporting
Is Databricks the right call?
Usually, when data has more than one job to do. The lakehouse earns its keep when BI, streaming, and machine learning need the same governed copy. When the work is simpler than that, a simpler stack wins, and we will say so before you buy anything. The recommendation follows your workloads, not our margins.
BI, streaming, and ML on one copy of the data
Open table formats: Delta Lake and Iceberg
Spark skills already in the building
GenAI on your own documents and data
A simpler stack
Pure SQL reporting for a small analyst team
A packaged app is 90 percent of the answer
When the second column wins, the honest answer is often Snowflake or a packaged product. The full comparison lives on our data engineering page.
/ Results
Lakehouses that left the lab.
Fortune 500 Retail Client
87%Reduction in Data Processing Time
Databricks Modernization & AI Enablement for a Leading Convenience Retail Chain
Read the case study
Fortune 500 Retail Client
73%Reduction in Data Processing Time
From Disparate Systems to Data-Driven Decisions: A Retail Transformation
Read the case study
Fortune 500 Manufacturing Client
$12.4MAnnual Cost Savings
ACI Streamlines Logistics for Chemical Manufacturer, Saves $10M
Read the case study/ How an engagement runs
Five phases. No mystery.
01
Assess
Weeks 1 to 2
Workspace audit, cost baseline, and the first production use case worth real money. If Databricks is the wrong tool, this is where we say so.
02
Architect
Weeks 2 to 4
Medallion design, Unity Catalog model, cluster policies, and a cost plan you can defend to finance.
03
Build
Weeks 4 to 12
Pipelines, quality gates, and CI for jobs and notebooks, shipped in increments instead of a big reveal.
04
Prove
Parallel run against the legacy numbers until the business signs off. The old platform stays on until parity is boring.
05
Run
Around-the-clock operations under SLA with our managed team, or a clean handover to yours with runbooks that work at 3am.
The platform is half the job.
Databricks pays for itself when the pipelines, governance, and operations around it are engineered with the same care as the platform choice. That is our data engineering practice, and the lakehouse is its home ground.
Data Engineering/ Why ACI
Why enterprises run Databricks with us
/ Partnership
Databricks consulting partner.
40+ lakehouse implementations, with certified architects on data engineering, ML, and platform administration.
/ Delivery
The architects who design your lakehouse are the ones who build it.
No handoff between the pitch deck and the delivery pod.
/ Scale
Founded 2006.
1,200+ engineers across 11 global delivery hubs. 500+ enterprise projects.
/ Operations
ISO 27001 certified, with 24/7 managed operations under published SLAs.
From the blog
Applied AI & ML
AI-Ready Data Architecture 2026: The Lakehouse Rebuild You Need
Build an AI-ready data architecture with lakehouse and medallion design to improve data trust, governance, and enterprise AI outcomes.
Read
MarTech & CDP
Unity Catalog Metrics: Deliver Trusted KPIs Across Your Enterprise
Build trusted KPIs with Databricks Unity Catalog Metrics Layer. Standardize definitions, improve governance, and ensure data consistency.
Read
Cloud Modernization
Multi-Agent AI for Cloud Cost Optimization
Avoid lakehouse migration mistakes. Learn why “cold data” breaks AI pipelines and how governance-first design ensures trusted analytics.
ReadExplore related capabilities
/ Databricks FAQ
Databricks questions,
answered straight.
When is Databricks the right call over a data warehouse?
When the same data has to serve BI, streaming, and ML without keeping three copies of it. The lakehouse holds one governed source for all three. If your work is purely SQL reporting, a warehouse may be simpler, and we will say so.
How long to stand up a production lakehouse?
A governed Bronze, Silver, and Gold platform with Unity Catalog usually reaches its first production workload in 8 to 14 weeks. We do not hand over a notebook and call it a platform.
What does Unity Catalog actually buy us?
One place for access control, lineage, and discovery across every workspace and cloud. Without it, governance drifts per team and you find out at audit time. With it, who-touched-what is a query, not an investigation.
How do you control Databricks cost?
Job clusters over all-purpose clusters, autoscaling with sane floors and ceilings, spot instances where it is safe, and Photon for the heavy SQL. We tag clusters so cost maps back to teams and you can see what each pipeline really costs to run.
Can you take models to production, not just pilots?
That is the point. MLflow for tracking and registry, evaluation gates before anything ships, and monitoring after. A model nobody can retrain or roll back is a liability, not a feature.