The Hi-Tech Data Convergence Migration Playbook: Field Examples and How to Start

In Part 2 of this series, I walked through the architecture - what real-time data convergence actually looks like, the five building blocks hi-tech teams need, how it modernises the data stack, and where SingleStore fits honestly within the stack.

This post is about field work. How do teams actually make the shift? How do you know when the problem is acute enough to act on it? And what does a migration that avoids 'just adding one more system' actually look like in practice?

How to Know You're Ready

Before the migration approach, it is worth asking whether the problem is acute enough to justify the change. These four diagnostic questions tend to surface an honest answer quickly.

Four diagnostic questions for teams building data-intensive applications:

• How many systems does a single agent query touch before returning a result? If the answer is more than two, the latency budget is being spent on infrastructure coordination rather than useful work.

• Do your operational and analytical views of the same data ever disagree? When they do, teams argue about numbers rather than acting on them. A converged architecture has one version of the truth, creating a stronger foundation for data driven decision-making.

• What happens to your architecture if AI agent traffic increases ten times? If the honest answer involves adding more systems, the architecture is not built for the direction modern data applications and AI workloads are heading.

• Where is your GPU compute relative to your data? For hi-tech teams doing AI processing at ingestion or query time, the latency cost of compute being remote surfaces faster than most expect once it is measured.

If more than two of these have answers that feel uncomfortable, the consolidation conversation is worth having now - before the next scaling event or agentic AI rollout forces it.

What This Looks Like in Practice

Theory is useful. Field examples are more useful. Three organizations running large-scale software platforms that have implemented variations of this architecture in production:

Global leader in internet connection diagnostics - from six databases to three

This customer is a clear example of the database sprawl pattern from Part 1 in real life, where a fragmented data stack created operational complexity. They were running MySQL, BigQuery, Clickhouse, Redshift, and SingleStore for different use cases across their data stack - each chosen for a real reason at the time, collectively producing the fragmentation and operational cost that comes with a six-database stack.

What made the migration work was the approach: a proper landscape assessment before any replacement was proposed. Understand the full picture, identify where the HTAP capability - high-frequency ingestion combined with high-concurrency analytical queries - creates genuine advantage, and only move where there is real business impact. Not everything needs to move.

Epigen - real-time vector search on live video

Epigen uses SingleStore for real-time facial recognition on video streams, one example of the demanding data applications emerging in AI-driven environments. Frames are processed as they arrive, vectorised, and queried - with vector search running against live, continuously updated data rather than a static index.

This is the multimodal and compute-close-to-data building blocks in production. The pattern requires vector similarity search on streaming data at the latency real-time video analysis demands. Separating ingestion, vectorisation, and retrieval across multiple systems would introduce exactly the latency that makes real-time analysis impractical.

Heap - full-fidelity analytics at billions of events

The Heap case study is a useful counterpoint to the 'roll up everything to make it queryable' pattern many hi-tech analytics stacks rely on. Heavy aggregations make data queryable at scale but destroy granularity and introduce delay between events and insight.

By ingesting billions of events and querying at full fidelity, Heap's teams answer questions at the level of individual user interactions, up to the current moment, enabling more data driven decisions without waiting for a modelling job to finish. Analysis happens on operational data, not on a pre-summarised approximation of it, enabling more data driven decisions in real time.

The Migration Playbook - Stage-Gated, Not Big Bang

The approach that works is deliberate and phased, especially when consolidating a modern data platform. The goal is not to rip out the entire stack - it is to collapse the hot path in a sequence that proves value before expanding scope. The SingleStore hi-tech solution centre covers common starting points for different industries and workload types.

Stage-gated migration - five steps:

1. Start with a landscape assessment. Before recommending anything, understand the full picture: what systems exist, what problems each is causing, and where the real business pain is. The goal is to find where consolidation creates genuine impact - not to go in with a solution already decided.

2. Identify the highest-value hot path use cases. These are typically workloads where latency creates real business risk: customer-facing queries, real-time AI retrieval, telemetry processing, manufacturing quality loops. Start there, not with the easiest migration.

3. Replace one system at a time. Prove performance in a bounded scope, build confidence with the team, then expand. The Ookla approach - replacing BigQuery first, then working through the remaining databases - is a better model than moving everything at once.

4. Keep the warehouse. The migration is about removing the warehouse from the hot path, not removing it from the architecture. It keeps doing what it does well. The convergence layer handles what it could not.

5. Avoid adding SingleStore as yet another silo. The whole point is fewer systems in the hot path. If the migration ends with SingleStore alongside the existing stack rather than replacing something specific, the complexity problem has not been solved - it has been extended.

Closing Thought

Hi-tech workloads were already demanding more than traditional architectures could deliver. Agentic AI has raised the ceiling significantly - on concurrency, on latency expectations, and on the completeness of context that agents need to operate accurately.

The architectural response is not to add more systems. It is to collapse the hot path.

The warehouse and lake stay as foundational components of the broader data stack. The convergence layer - continuous ingestion, multi-pattern queries, machine-scale concurrency, multimodal data, compute close to data - handles everything that needs to be fast, fresh, and AI-ready.

That shift does not have to happen all at once. But it is better to start the conversation before the next scaling event or agentic rollout forces it.

Ready to talk through your architecture?
SingleStore's solutions team runs architecture reviews regularly. Free, and they tend to surface things worth knowing before the next procurement review or scaling event does. Talk to a solutions engineer or explore the SingleStore Hi-Tech solution centre.