/ Snowflake
Snowflake that stays fast and on budget
ACI Infotech is a Snowflake Partner with SnowPro certified architects. We design the warehouse, migrate the data by domain, and set resource monitors and auto-suspend before the first invoice lands. Then we run the platform, around the clock, under SLAs.
- Snowflake partner
- SnowPro certified architects
- 500+ enterprise projects
- ISO 27001
SnowflakeDatabricks and Snowflake partner
Certified architects on both. We implement what fits.
Fast and on budget
Keep Snowflake fast
and the invoice calm.
Resource monitors and auto-suspend from day one, queries profiled, credits attached to owners. We have tuned enough Snowflake estates to know where the money hides, and we wire the platform so it stays found.
22%Faster decisions
One governed data platform for 400,000 employees across 53 countries.
Read the case study
/ What we do on Snowflake
One warehouse. Six disciplines.
01
Warehouse and platform architecture
Warehouses sized for the workloads you actually run, with resource monitors and auto-suspend set on day one. Storage and compute scale apart, roles and masking are designed in, and month six on the platform looks like month one.
SnowflakeMulti-cluster warehousesZero-copy cloningResource monitors
Snowflake02
Migrations from legacy warehouses
Teradata, Oracle, Netezza, and homegrown estates moved by domain, heaviest cost first. Old and new run in parallel until the numbers match, and nothing cuts over until the business signs off on parity.
TeradataOracleNetezzaParallel runs
Teradata03
Snowpark and data apps
Pipelines, UDFs, and ML built in Python where the data already lives, plus Streamlit apps for teams who need more than a dashboard. One platform to govern instead of a sidecar stack nobody owns.
SnowparkPythonSnowpark MLStreamlit
Python04
Data sharing and clean rooms
Live shares with partners and customers, with no copies and no file drops to babysit. Clean rooms let two companies compute on joined data without seeing each other’s rows, and the governance model comes along for free.
Secure Data SharingData Clean RoomsSnowflake Marketplace
05
Performance and cost control
Snowflake bills by the second, so the bill follows the query plan. We profile the heavy queries, right-size the warehouses, and set clustering only where it pays. Then finance gets a report that explains the number.
Query profilingWarehouse sizingClusteringFinOps baseline
06
Managed Snowflake operations
Monitoring, security management, access reviews, and upgrade planning after go-live, under SLAs and around the clock. The platform stays fast and governed without pulling your engineers off their own roadmap.
24/7 NOCSLAsAccess reviewsCost reporting
Is Snowflake the right call?
Often, when SQL analytics is the center of gravity and the platform team is small. Snowflake removes most of the knobs other platforms make you turn, and data sharing is where it has no real rival. When heavy streaming and ML need the same copy of the data, a lakehouse can fit better, and we will say so before you buy anything. We hold certifications on both.
SnowflakeSQL analytics and BI as the center of gravity
Sharing governed data with partners and customers
A small platform team with no appetite for tuning clusters
Elastic concurrency without capacity planning
A different road
Heavy streaming and ML on one copy of the data
Deep Spark skills already in the building
When the second column wins, the honest answer is usually Databricks. The full comparison lives on our data engineering page.
/ Results
Data platforms that held up.
Global Food Services Operator
22%Faster decisions
One governed data platform for 400,000 employees across 53 countries, replacing dozens of regional reporting stacks.
Read the storyGlobal Financial Services Firm
90dFrom prototype to production
A governed cloud data foundation for analytics and machine learning, taken from first prototype to production in one quarter.
Read the lakehouse story
Fortune 500 Convenience Retail Chain
30%Reduction in data latency
A modern data platform across 600+ locations, with real-time inventory visibility and zero downtime through the cutover.
Read the retail story/ How an engagement runs
Five phases. No suspense.
01
Assess
Current spend, workload inventory, and the first domain worth moving. If Snowflake is the wrong tool for your workloads, this is where we say so.
02
Architect
Warehouse layout, role hierarchy, masking policies, and resource monitors, with a cost model you can defend to finance before a single credit burns.
03
Migrate
Schemas, pipelines, and reports moved by domain. Most enterprise migrations land in 3 to 6 months; a single-source warehouse swap can land in weeks.
04
Prove
Old and new run in parallel until the numbers match and the data owners sign off. Nothing gets switched off on faith.
05
Run
Around-the-clock operations under SLA with our managed team, or a clean handover to yours with runbooks that hold up at 3am.
The warehouse is half the job.
Snowflake pays off when the pipelines, models, and governance around it get the same engineering care as the platform choice. That is our data engineering practice, and the warehouse sits at the center of it.
Data Engineering/ Why ACI
Why enterprises run Snowflake with us
/ Partnership
Snowflake partner with SnowPro certified architects.
We are a Databricks partner too, so the platform recommendation follows your workloads, not our margins.
/ Delivery
The architects who design your warehouse are the ones who tune 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 and CMMI Level 3 appraised, with 24/7 managed operations under published SLAs.
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ReadExplore related capabilities
Data Engineering
The pipelines and governance behind the warehouse.
Databricks
When streaming and ML share the same data.
Retail & Consumer
Customer data and demand forecasting.
Financial Services
Governed, audit-ready analytics.
Snowflake Consulting Services
Scoped engagements with certified Snowflake engineers.
/ Snowflake FAQ
Snowflake questions,
answered straight.
How long does a Snowflake migration take?
Most enterprise migrations run 3 to 6 months from kickoff to cutover, and a single-source warehouse swap can land in weeks. The long pole is rarely Snowflake itself; it is untangling the pipelines and reports that grew around the old system. We run old and new in parallel until the numbers match, then cut over.
What actually drives the cost of running Snowflake?
Compute, not storage. Snowflake bills per second of warehouse runtime, so cost comes from how queries are written and how warehouses are sized and suspended. We set resource monitors, auto-suspend, and right-sized warehouses on day one, and most teams watch the bill drop once we tune the heavy queries.
Do we have to move everything at once?
No. We migrate by domain, usually starting with the workloads that cost the most to run or hurt the most today. Each domain gets validated against the source before anyone trusts it. Big-bang cutovers are how you end up reconciling numbers at 2am.
How do you keep data governed in Snowflake?
Role-based access, row and column security, masking policies for PII, and object tagging for lineage and cost attribution. We wire this in as we migrate, not as a cleanup project afterward.
What do you need from our team?
A data owner who can settle what the numbers should be, and access to the source systems. We bring the architects and engineers. The work that stalls migrations is usually decisions, not code.