/ Data Engineering

Data engineering that feeds AI and analytics

ACI Infotech builds enterprise data platforms: Databricks lakehouses, Snowflake warehouses, and real-time pipelines that run around the clock with SLAs. We take data scattered across dozens of systems and turn it into one governed foundation your analysts, your applications, and your AI models can actually use.

40+ lakehouse implementations
Databricks and Snowflake partner
24/7 run teams
ISO 27001

Talk to a data architect See the case studies

Snowflake

40+ lakehouse implementations

Certified on both platforms. Run in production.

The case for one platform

Data works best
living in one place.

Most enterprises grew into ten to fifty source systems: CRM in one cloud, ERP in another, operational data on overnight batches. Bring them onto one engineered platform with ownership, lineage, and a pipeline SLA, and reports assemble themselves, AI pilots find clean data on day one, and the three-team report request becomes a single query.

87%Reduction in processing time

Lakehouse modernization across 600+ locations

“They flawlessly delivered top-tier digital data on a milestone that mattered to us. Their dedication and expertise made them a genuine partner, not a vendor.”

Director of Data and MarTech · A national convenience retailer

Read the case study

/ What we build

One governed platform. Six ways in.

Lakehouse architecture

Databricks lakehouses on Delta Lake with Unity Catalog governance. One copy of the data serves BI, streaming, and machine learning, with lineage and access controls from day one.

DatabricksDelta LakeUnity CatalogApache Iceberg

Cloud data warehouses

Snowflake warehouses sized and tuned so compute spend follows real demand. Resource monitors and auto-suspend go in on day one, because most Snowflake overspend is warehouses nobody turned off.

SnowflakeSnowparkdbt

Real-time pipelines

Kafka and Spark streaming for decisions that cannot wait for the nightly batch: inventory, fraud, pricing, alerts. Millions of records per second, with observability wired through Dynatrace.

KafkaSpark StreamingKinesisEvent HubsDynatrace

Data governance

Catalogs, quality scores, lineage, and access policy written as code. When an auditor asks where a number came from, the answer is a query, not a meeting.

Unity CatalogCollibraAlationGreat Expectations

Migration and modernization

Teradata, Oracle, Netezza, and Hadoop estates moved in staged waves. Parallel runs prove parity before anything old gets switched off.

TeradataOracleNetezzaHadoop

AI-ready data products

Feature stores, vector search, and curated datasets your models can train on without a six-week data hunt. This is the layer most stalled AI programs are missing.

Feature storesVector searchMLflow

Snowflake or Databricks?

Both, honestly, depending on the job. Snowflake wins when the center of gravity is SQL analytics, sharing, and a predictable warehouse. Databricks wins when the same data has to serve BI, streaming, and machine learning without keeping three copies of it. Plenty of enterprises run both under one governance layer. We hold certifications on each, so the recommendation follows your workloads, not our margins.

Snowflake

SQL analytics and BI on a predictable warehouse

Governed data sharing with partners and clean rooms

Warehouse costs finance can budget a year out

Enterprise governance across every workload

Both platforms

Open table formats: Delta Lake and Iceberg

Both platforms

BI, streaming, and ML on one copy of the data

Real-time pipelines and event-driven ingestion

Model training, MLflow, and GenAI workloads

Enterprise governance across every workload

Both platforms

Open table formats: Delta Lake and Iceberg

Both platforms

A card under both logos means both platforms carry it, under one governance layer.

/ Results

Built for enterprises. Measured in production.

Fortune 500 Hospitality Client

67%

Reduction in Implementation Timeline

Global Food & Facilities Leader Unified Data Intelligence

Read the case study

Leading CPG & F&B leader in the USA

75%Reduction in Analytics Request Volume

How a Global CPG Leader Empowered Brand Managers with Self-Service Intelligence

Read the case study

Fortune 500 Financial Services Client

67%Reduction in Allocation Processing Time

Modernizing Finance & Reporting with SAP Transformation

Read the case study

/ How an engagement runs

Five phases. No mystery.

Discover

Weeks 1 to 4

Inventory the sources, meet the teams, and agree on the first use case that is worth real money.

Architect

Weeks 3 to 6

Platform choice, governance model, and a cost plan you can defend to finance.

Build

Weeks 6 to 16

Pipelines, lakehouse, and quality gates shipped in increments, not saved up for a big reveal.

Prove

Parallel run against the old numbers until the business signs off. Nothing legacy is turned off on faith.

Run

Around-the-clock operations under SLA with our managed team, or a clean handover to yours with runbooks that work at 3am.

Go-live is the halfway point.

A data platform earns its keep in year two, when the pipelines still run, the costs still hold, and the quality scores still mean something. Our managed operations team keeps platforms up around the clock, with P1 response in fifteen minutes and misses reported monthly with root cause.

Managed Operations

/ Why ACI

Why enterprises pick us for data

/ Delivery

The architects who scope your platform are the ones who build it.

No switch between the pitch team and the delivery pod.

/ Partnerships

Databricks and Snowflake partners, with certified architects on both and 40+ lakehouse implementations behind them.

/ 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.

/ Questions

Data questions,
answered straight.

The questions we hear most before a data engagement. Anything else belongs in a conversation.

What is enterprise data engineering?

Building the pipelines, storage, and governance that turn scattered source data into something analytics and AI can use. It covers ingestion, the lakehouse or warehouse itself, quality, lineage, and serving data to the people and systems that need it.

How long does a data platform take?

A first governed use case ships in 8 to 12 weeks. Full platform migrations run 6 to 18 months depending on the estate. We ship in increments either way, so value lands long before the final cutover.

Snowflake or Databricks: which one do we need?

Snowflake for SQL-centered analytics and sharing, Databricks when BI, streaming, and ML must share one copy of the data. Many enterprises run both. We are certified on each and will tell you which fits, workload by workload.

What is the ROI of a modern data platform?

Typical results from our engagements: 15 to 20% lower storage costs, insights delivered 22% faster, and roughly double the productivity per analyst. The bigger number is usually the AI projects that stop stalling.

Can you work with our existing team and stack?

Yes. We build alongside your engineers and operate the tools you already run. The goal is a platform your team owns, not a dependency on ours.

How do you handle governance and compliance?

Lineage, access control, and audit trails are designed in from the start, not patched on before an audit. Every dataset gets an owner, a quality score, and a documented path back to its source.

Do you run the platform after go-live?

Both options. Our managed operations team runs it 24/7 under SLA, or we hand over to your team with runbooks and stay on call during the transition.

Is our data ready for AI?

Probably not yet, and that is normal. AI-ready means governed, documented, and fresh enough for the use case. We assess that gap in the first four weeks and build the missing layer instead of writing a report about it.