Data Platforms
One set of numbers. Fewer meetings about them.
InfoMover designs and builds data platforms on AWS with Spark, Kafka and Delta Lake: streaming and batch pipelines, a lakehouse your analysts can trust, and dashboards in tools such as Superset. We have delivered two large big-data applications, and we design data platforms that also feed the AI systems that come next.
Sound familiar?
- Three dashboards, three different totals.
- Reports run overnight and are out of date by the morning meeting.
- Data lives in operational databases that nobody wants to query at peak hours.
- Your AI plans assume clean data you do not have yet.
What you get
- Pipelines that run unattendedStreaming and batch ingestion with Kafka and Spark, with retries, monitoring and alerts.
- A lakehouse with rulesDelta Lake tables with clear ownership, history and quality checks, so a number means one thing.
- Dashboards people useSuperset or your BI tool of choice, built on agreed definitions.
- AI-ready dataClean, documented sources that RAG and analytics workloads can build on.
Ways to engage
Pick the shape that fits.
A short review or proof of concept first, then the platform in phases.
Questions & answers
Do you work on AWS only?
Most of our data work runs on AWS, but the core tools, Spark, Kafka and Delta Lake, run on any major cloud or on your own servers.
Can you start from our existing warehouse?
Yes. We usually start with a short review of what exists, then decide what to keep, what to move and in what order.
How does this connect to AI?
RAG and analytics assistants are only as good as their data. A clean, documented platform is the cheapest way to make later AI work reliable.
Do you provide ongoing support?
Yes, as part of a Long-term Delivery and Support engagement, or as a handover to your team with documentation and runbooks.
Start a conversation
Talk to an architect about Data Platforms.
Tell us where your data lives today and which numbers people argue about. We'll suggest a first step.