Proven with Industry Leaders
Why AI readiness is a data latency problem
Most AI programs that stall have a model that works and data that is late.
Stale data, confident answers
An agent that answers from yesterday's data gives yesterday's answer, with full confidence.
Master data drifts
Customer and account records drift when every system loads its own copy on its own schedule.
The expensive fix
Large master data platforms fix drift at a price many programs can't justify. Current data at the source often removes most of the need.
Keep, bridge or move your messaging
Most estates already run more than one messaging product. The goal is current data for AI, not a new standard for its own sake.
| You run today | Our usual advice |
|---|---|
| RabbitMQ | Keep it for application queues. Bridge the events AI needs to Pub/Sub or Kafka. |
| Kafka, self-managed | Consider Managed Service for Apache Kafka or Confluent Cloud, and weigh the run cost against your team's time. |
| Batch file drops | Replace the feeds AI depends on first. Leave the rest until they matter. |
| Pub/Sub | Keep it for Google-native fan-out. Add ordering and dead-letter handling where agents depend on it. |
For messaging outside Google Cloud, see event-driven architecture and Kafka support.
Master data without a large master data platform
Matching and merging customer records as changes arrive keeps one current view without a separate platform. We size this against your volumes and budget before recommending a tool.
What the first engagement gives you
- A lineage map of the flows feeding your operational data store
- The measured lag on each feed
- A ranked list of fixes, including items that cut cost
- A plan you can take to budget review
See where your data goes stale
Our team maps the data behind one AI use case with Cloudera Data Lineage, from source systems through pipelines to the store your AI reads, including BigQuery.
- Price: free for one closely defined use case. Up to $8K for a wider scope.
- Timing: about seven working days after access is set up. Three to run the tool, four to write the report.
- Your data stays put: only metadata leaves your environment.
Real-time data on Google Cloud, answered plainly.
What does near real time mean here?
Seconds to minutes, set for each feed. A fraud feed and a marketing feed rarely need the same target, so we agree one for each.
Do we need Kafka?
Not always. Pub/Sub covers most Google-native fan-out. Kafka earns its place when you need long retention, replay, or you already run a large Kafka estate.
Can we keep RabbitMQ?
Yes. RabbitMQ is good at application queues. We bridge the events AI needs to Pub/Sub or Kafka and leave the rest in place.
What does AI readiness mean in this context?
Data that is current, traceable and governed before an agent reads it. The lineage map shows how far you are from that today.
Which lineage tool do you use?
Cloudera Data Lineage (formerly Octopai), which maps lineage across mixed estates, including BigQuery. Only metadata leaves your environment.
How long before we see results?
The lineage map report arrives about seven working days after access is set up. The first feeds usually move to change data capture within a quarter.