Data infrastructure for bursty research vs steady services

Some workloads sprint. Others pace. Research is bursty by nature with unpredictable spikes, exploratory runs, and short feedback loops. Services are steady with known demand curves, strict approvals, and clear service levels. Trying to run both on the same footing creates waste and friction. At Datatailr we design for two distinct lanes on one platform in your cloud so teams move fast when discovery calls for it and stay predictable when production demands it.

Two lanes on one platform

Research lane
Optimized for exploration and iteration. Elastic capacity, small warm pools for interactive starts, permissive defaults with clear ceilings, and rapid promotion paths when a promising idea appears.

Service lane
Optimized for reliability and cost predictability. A right sized baseline with a measured surge buffer, strict approvals, blue green style releases, budgets by feature, and deep observability.

Both lanes share the same control plane for identity, policy, approvals, lineage, cost, and audit. Both run inside your cloud under one policy layer. The difference is how resources are requested, how promotions are gated, and how cost guardrails are applied.

Workload profiles and goals

Bursty research
Traits: variable runtime, interactive loops, GPU or memory heavy trials, wide scatter in concurrency, low tolerance for waiting.
Goals: fast time to first result, low queue depth during spikes, explicit ceilings to avoid surprise spend, easy path from notebook or IDE to a scheduled pipeline.

Steady services
Traits: predictable cadence, defined SLOs, narrow variance in concurrency, strict change control, stable footprint with occasional surges.
Goals: consistent latency and throughput, smooth rollouts and rollbacks, budgets by feature, clear chargeback, and clean handoffs to business consumers including Excel.

Capacity strategy

Research lane

• Elastic on demand with a small warm pool sized to interactive use
  
  

• Max fleet and per user caps that rise only with approval
  
  

• Time windows for heavier bursts so peaks land when they cost less
  
  

• Idle retraction that returns capacity to baseline as soon as queues drain
  
  
  

Service lane

• Right sized baseline sized to normal demand
  
  

• A surge buffer that pre warms during known peaks and retracts after
  
  

• Scale signals tied to p95 latency and queue depth rather than raw CPU
  
  

• Multi AZ placement for resilience and quick rollback to last known good
  
  
  

Data access and movement

Research slows when data must move to a new home. Services bloat when they materialize more than consumers need. Use a Data Bridge to bring tools to data and a Data OS to apply policy once.

• Query in place across Snowflake, BigQuery, S3, Kafka, Postgres, and on prem sources with federated SQL under one policy layer
  
  

• Let analysts and product owners express intent in plain English with text to SQL when that is faster than writing a query
  
  

• Publish governed outputs back to tables, internal services, dashboards, and Excel so consumers get what they need without extra copies
  
  
  

Orchestration and SDLC

Research lane

• Build in notebooks or your preferred IDE under Git
  
  

• Convert promising notebooks to pipelines with owners and runtime limits
  
  

• Promote to Pre with lightweight approvals and automatic lineage capture
  
  

• Keep retries and partial reruns easy so iteration speed remains high
  
  
  

Service lane

• Pipelines and services carry stricter policies, approvals, and release windows
  
  

• Blue green style promotion with one click rollback
  
  

• Versioned artifacts and immutable lineage for audit and root cause
  
  

• Alerts on error rate, latency, and spend tied to budgets by feature
  
  
  

Caching and materialization

Research lane

• Cache deterministic steps by fingerprinting inputs and parameters
  
  

• Expire or archive artifacts that are not read within a set window
  
  

• Share engineered features across experiments to avoid duplicate work
  
  

Service lane

• Materialize only what downstream consumers require and attach ownership and retention rules
  
  

• Use change driven triggers so recomputation runs when inputs move and schedules fill the gaps required by compliance
  
  

Cost governance that matches how people decide

Research lane

• Budgets by user and by project with alerts at 70%, 90%, and 100%
  
  

• Soft caps to warn and hard caps to pause with an approval door when needed
  
  

• Per workspace ceilings for concurrency, GPU hours, and runtime
  
  

• Showback views to build awareness before chargeback begins
  
  

Service lane

• Budgets by feature and by product area for chargeback
  
  

• Weekly review of cost per run and cost per feature next to SLOs
  
  

• Warm pool schedules that coincide with traffic patterns
  
  

• Reserved plus Spot mix where appropriate with policies that prefer cheaper capacity without impacting targets
  
  
  

Security and compliance that travel with the work

Run everything inside your cloud in a single tenant posture. Identity flows from your directory through SSO with optional MFA. Apply role based access at projects, jobs, services, datasets, tables, and columns. Keep egress explicit through allowlists. Tie every run back to a commit and a user and retain full audit logs. The same policy engine governs both lanes so speed does not bypass control.

Reference policies in Python style pseudocode

Research lane policy

policy("research-default",

  max_vms=200,

  warm_pool=20,

  scale_up_if="queue>25 or interactive_latency>250ms",

  scale_down_if="queue==0 for 5m",

  user_budget_usd=1500, alert=[0.7, 0.9, 1.0],

  max_gpu_hours_per_user=80,

  max_concurrency_per_workspace=100,

  sleep_windows=["22:00-06:00"],

  retention_days=14

)

Service lane policy

policy("service-pricing-api",

  baseline_vms=30,

  surge_cap=60,

  prewarm_windows=["07:45-09:30","11:45-13:30","15:45-17:30"],

  scale_up_if="p95_latency>200ms or queue>10",

  scale_down_if="p95_latency<120ms and queue==0 for 10m",

  feature_budget_usd=12000, alert=[0.7, 0.9, 1.0],

  canary_percent=10, release_window=["09:00-17:00"],

  rollback_on="error_rate>1% for 5m",

  retention_days=90

)

Function names are illustrative. The platform supports policy in code or UI without YAML.

Moving from research to service in six steps

1. Stabilize the notebook
   Clean inputs, fix seeds, and attach tests for deterministic steps
   
   
   

2. Promote to a pipeline
   Add owners, runtime limits, and retries. Capture lineage
   
   
   

3. Size the footprint
   Profile CPU, memory, and IO. Pick classes and warm pool depth that meet targets
   
   
   

4. Add guardrails
   Set budgets, concurrency ceilings, and change windows
   
   
   

5. Expose outputs
   Publish a governed table, a dashboard, and a service endpoint as needed. Add an Excel function if business users need direct access
   
   
   

6. Canary and roll forward
   Route a small percentage, watch latency and errors, then increase traffic. Keep rollback one click away
   
   

What to review each week

• Queue depth and time to first result in research workspaces
  
  

• p95 latency, error rate, and rollback count for services
  
  

• Utilization of the largest instances, especially GPUs and memory heavy classes
  
  

• Share of runs served from cache versus recomputed
  
  

• Cost per run and cost per feature for the top five jobs and services
  
  

• Number of environments with zero promotions in the last 30 days
  
  

Why this pattern endures

It is tempting to treat research and services the same to keep the platform simple. In practice that is what creates idle spend for research and delays for services. The two lane pattern keeps both honest. Research stays elastic, capped, and fast. Services stay steady, observable, and easy to roll back. Policy, approvals, lineage, and cost travel with the work so security and finance stay comfortable. Because everything runs in your cloud, engines and tools can change without moving data or rewriting the foundation.

If you want, I can tailor these lane policies to your workload mix and produce a quick start checklist for your AWS account.