Scope, cost and AI readiness, before any migration starts.
Applications mapped
Risk scored
Phased plan
Price fixed
Data integration
E-commerce data into Databricks or Snowflake.
Design, build and cost-tune the pipeline between your store and your analytics platform, with fault tolerance built in.
- Fixed-price assessment first
- Every change engineer-signed
- AceMQ 24/7 after go-live
Modernization assessment
Fixed priceProjected after migration
RuntimeWebLogic 12cTomcat 10
LanguageJava 8Java 21
FrameworkEJB 3Spring Boot 3
DatabaseOraclePostgreSQL
The short answer
Data analytics integration connects your e-commerce platform to Databricks or Snowflake so orders, customers, products and inventory land reliably for analysis. The design work is in incremental loads, schema changes, retries and cost, so the pipeline keeps up without reloading everything or silently dropping records.
Customers
Partners
Problem
Why e-commerce integration is harder than it looks
Platforms expose data through APIs with rate limits and paginated exports, and objects change after the fact: orders are edited, refunded and partially shipped. A pipeline that copies only new records misses those updates. One that reloads everything gets slow and expensive. Good design handles incremental changes, schema drift and retries from the start.
Design
From your store to your warehouse
| Stage | What happens |
|---|---|
| Source | Storefront such as BigCommerce, Shopify or custom, plus payments, shipping and marketing systems |
| Integration | Incremental extraction, landing in cloud storage, retries and checkpoints |
| Analytics | Loaded into Databricks or Snowflake on a schedule or as a stream |
Platform
Databricks or Snowflake
Both work well. Snowflake is often chosen for SQL-first analytics and BI teams. Databricks suits teams that also do data engineering and machine learning, such as demand forecasting. We integrate with either and design around the one you run.
How every engagement runs
AI drafts. Engineers decide.
Three phases, the same on every stack. Each one ends with something you can hold us to.
AI-assisted, engineer-verified, one capability at a time.
- import javax.persistence.Entity;+ import jakarta.persistence.Entity;- @Stateless+ @Service
AI draftEngineer approved
AceMQ engineers support the new stack from day one.
On call: the team that ran the migration
- Test before you touch
- AI drafts, engineers decide
- Strangle, don’t big-bang
- Every step is reversible
- Measure before and after
- Open targets, no new lock-in
- Fixed scope before fixed price
- Plan the run before the cutover
Operations
Cost and fault tolerance
Warehouse compute is billed by use, so pipeline shape drives the bill: how often it runs, how much it reprocesses, how queries are structured. Fault tolerance means a failed run resumes where it stopped and a source outage delays data instead of losing it. We cover both before building.
Modernization
Data in a modernization project
When an application is replatformed, its data feeds change shape. Reports and pipelines that read the old schema break quietly. We list those consumers during the modernization assessment and keep them working through the move. For warehouse-side tuning, see Greenplum.
Delivery
What you get
A working integration between your e-commerce system and Databricks or Snowflake, designed to be efficient without overspending, tuned for cost and fault tolerant so critical data stays available during failures.
We also document the load logic and the schema mapping, so your analysts know what each table means and how fresh it is. Undocumented pipelines are the most common reason analytics teams stop trusting their numbers.
Monitoring
Keeping the pipeline honest
A pipeline that fails loudly is easy. The costly failures are silent: a schema change that nulls a column, a retry that double-loads an order. We add row-count and freshness checks at each stage, so a bad load raises an alert instead of a wrong dashboard.
Proof
Delivered by the AceMQ engineering team.
IBM MQ → RabbitMQ
Phased migration with legacy code refactoring
Legacy messaging replaced in phases while the applications calling it were refactored. No big-bang cutover.
American NationalDelivered by the AceMQ engineering team
1,000+ apps
Spring and Java applications upgraded in 24 hours
A deterministic Spring upgrade process, run through Broadcom’s commercial Spring support, in a single 24-hour window.
Global financial institutionDelivered by the AceMQ engineering team
IBM MQ out
Proprietary messaging displaced
IBM MQ displaced with an open-source messaging stack, supported by AceMQ.
DellDelivered by the AceMQ engineering team
After go-live
Keep it running after cutover.
Databricks and Snowflake.
Design and architecture work comes before the build, and covers run frequency, reprocessing and query structure.
Runs resume from a checkpoint, so data is delayed rather than lost.
Extract from the APIs or exports, land in cloud storage, then load incrementally.
Snowflake for SQL-first BI, Databricks when data engineering and ML are also needed. Both work.
Start here
Know the cost before anything moves.
A fixed-price assessment gives you the inventory, the risk score and a phased plan. Scope and cost are fixed before the migration starts.
Get in touch
Talk to a modernization engineer
Tell us what you run today. An engineer, not a sales script, replies within one business day.