Scope, cost and AI readiness, before any migration starts.
Applications mapped
Risk scored
Phased plan
Price fixed
Greenplum
Greenplum consulting for analytics at scale.
Performance tuning, architecture, machine learning and 24/7 support for open-source Greenplum and VMware Tanzu Greenplum.
- 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
Greenplum is a massively parallel processing data warehouse built on PostgreSQL. It splits data across segment hosts and runs each query on all of them at once. When it slows down, something is usually breaking that parallelism. Our consulting finds the cause and covers open-source and Tanzu Greenplum.
Stacks we work in
- Greenplum
PostgreSQL
VMware
Java
Spring Boot
Kubernetes
Kafka
Docker
Terraform
GitHub Actions
Background
How Greenplum works
A coordinator receives each query and plans it. Segment hosts each store a slice of every table and run their part in parallel. Performance depends on how evenly data is spread across segments and how well the planner understands it.
Performance
Why Greenplum queries are slow
Each is found with query plans and segment-level metrics.
- Distribution keys that skew data onto a few segments.
- Stale statistics that lead the planner to bad plans.
- Table bloat that VACUUM has not reclaimed.
- Queries that spill to disk instead of running in memory.
- Resource group settings that let large jobs starve others.
Editions
Open-source Greenplum and Tanzu Greenplum
Open-source Greenplum provides the MPP engine. VMware Tanzu Greenplum, now part of Broadcom, adds federated data access, polymorphic storage, in-database analytics and commercial support. We consult on both, and on moves to managed or hybrid deployments.
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
Services
What we do
Emergency support
Rapid fixes, root cause analysis and crisis management during outages.
Architecture and tuning
Scalable design, workload management, replication and disaster recovery.
Machine learning
Apache MADlib models trained where the data lives, and PXF for external data.
Proactive support
24/7 monitoring, upgrades, patching and SLAs matched to your operations.
Modernization
Greenplum in a modernization project
Analytics platforms rarely move on their own schedule. When applications change, the ETL, UDFs and external tables around Greenplum change too. We check those dependencies during a modernization assessment, and plan any cloud or hybrid move alongside the application work in application modernization.
AceMQ supports Greenplum in production after the move.
Machine learning
Machine learning and federated queries
Apache MADlib brings machine learning into the database, so models train where the data lives instead of exporting large data sets to another system. PXF, the Platform Extension Framework, lets Greenplum query external data in object storage, Hadoop and other databases.
We build on both, with user-defined functions and GUC tuning where workloads need them. We do not promise a speed-up figure before reading your query plans.
Customers
Partners
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.
A massively parallel processing data warehouse built on PostgreSQL, designed for analytics on very large data sets.
PostgreSQL runs a database on one server. Greenplum runs PostgreSQL on every segment and adds a coordinator and a parallel planner, so one query uses the whole cluster.
Both, including managed and hybrid deployments.
Yes. Emergency support covers fixes, root cause analysis and crisis management.
Yes, using Apache MADlib and data science workflows on the cluster.
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.
- InventoryEvery application, its stack and what it depends on.
- Risk scoreWhere a change is likely to break, ranked.
- Phased planWhat moves first, which tools lead, and the fixed price.
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.