What does a production data analytics platform include?

At minimum: reliable ETL pipelines that keep data fresh, a governed data warehouse with clear ownership, BI dashboards your team actually uses, and self-service query access for analysts. Allerin builds all four layers with data quality checks, access controls, and documentation so the platform does not become another abandoned tool.

How long does it take to build a data analytics platform?

A focused build covering one business domain (for example sales analytics or operational metrics) typically takes 3 to 6 weeks. Wider rollouts with more data sources and cross-domain reporting extend from there, scoped on data-source complexity and the number of stakeholder groups.

Can you integrate data from multiple sources into one platform?

Yes. We build ETL and ELT pipelines connecting databases, SaaS APIs, flat files, streaming sources, and legacy systems into a unified data warehouse. Common sources include Salesforce, HubSpot, Stripe, PostgreSQL, MongoDB, S3, and internal APIs. Data is cleaned, deduplicated, and modeled before reaching your dashboards.

How do you ensure data quality and governance?

We implement automated data quality checks at every pipeline stage: schema validation on ingestion, null and duplicate detection during transformation, and freshness monitoring on the warehouse. Governance includes column-level access controls, data lineage tracking, and a business glossary so everyone agrees on what "active customer" means.

Data & Analytics Platform

KPI and GIS dashboards on a governed warehouse, with data-quality checks and alerting so the numbers your team acts on hold up.

Timeline: 3-6 weeks
Team: Data Engineer · Frontend Dashboard Developer · Data Analyst · Project Manager
Typical stack: Data Warehouses: Snowflake, BigQuery, Redshift, Azure Synapse, Databricks. GIS: ArcGIS Enterprise/Online, QGIS, Mapbox, Google Maps Platform, PostGIS. BI Tools: Tableau, Power BI, Looker, Metabase, custom React dashboards with Recharts/D3. Data Quality: Great Expectations, dbt tests, custom Python validators, Soda. ETL/ELT: Fivetran, Airbyte, dbt, custom Airflow/Prefect DAGs. Orchestration: Airflow, Prefect, dbt Cloud. Integration: REST/GraphQL APIs, CDC (Debezium), message queues (Kafka, RabbitMQ). Observability: Prometheus, Grafana, Monte Carlo, elementary. Export formats: CSV, Excel, PDF, Shapefiles, GeoJSON, ArcGIS Feature Service.

What you get

Data connectors with retry logic and monitoring (source → warehouse)
KPI catalog with definitions, owners, refresh schedules, and SLAs
Data quality pipeline: profiling, validation rules, alerts on critical failures
GIS-enabled dashboards with zoom, filter, layer controls, and export
Narrative report generator with automated summaries and anomaly detection
Runbook: troubleshooting, scaling, adding KPIs, data refresh procedures

Outcomes

Live KPI dashboards with GIS layers and drill-down capabilities
Data quality pipeline with automated alerts on critical field failures
Scheduled narrative reports with anomaly detection and trend analysis
Measurable reduction in report prep time (typically 75%)
Self-service analytics with governed data catalog

Selected work

$42M in cargo saved. 250K shipments monitored.

Real-time IoT monitoring across 340 pharma facilities, 3.5M telemetry pings/hour. Excursion discovery went from 6-8 hours to 4.2 minutes with a 15-minute intervention window; 8,400 critical excursions caught in year one. FDA 21 CFR Part 11 compliant.

Rails · MQTT · TimescaleDB · IoT

p95 latency ↓46% in 6 weeks

p95 840ms → 450ms. Re-platformed hot paths, added tracing, tuned indices. No feature freeze. Infra spend down 21% ($42k → $33k/mo); 7 critical CVEs closed before go-live.

Rails · Postgres · Grafana

How we approach it

Cloud-Native (Snowflake/BigQuery)

When:: Need scalability, managed services, ML integration, multi-region
Tradeoffs:: Best performance and features, cloud costs, requires connectivity
Best for:: Enterprises with cloud-first strategy, high data volumes

Hybrid (Cloud + On-Prem)

When:: Regulatory constraints, sensitive data on-prem, cloud analytics
Tradeoffs:: Balanced compliance and capabilities, moderate complexity
Best for:: Healthcare, finance, government with data residency requirements

On-Prem Only

When:: Air-gapped environments, full data sovereignty required
Tradeoffs:: Complete control, higher ops burden, limited scalability
Best for:: Defense, critical infrastructure, strict compliance environments

Where teams use it

Manufacturing

Production KPI dashboards with OEE & yield tracking

Real-time equipment health monitoring with downtime root cause analysis and predictive maintenance alerts integrated with EAM systems

Transportation & Rail

Track health GIS with maintenance priority heat maps

Visual inspection data overlaid on GIS with automated work order generation for high-priority segments based on condition scores

Warehousing & Logistics

Real-time inventory flow with geofence alerts

Package movement tracking across facilities with dwell-time anomalies and capacity utilization dashboards for operational optimization

Energy & Utilities

Asset health dashboards with outage prediction

Grid asset monitoring with failure prediction models, outage impact zones, and crew dispatch optimization via GIS routing

What we need from you

Data sources and existing workbooks to replace
KPI definitions and data stewardship owners
Data quality target tolerances and thresholds
Security roles and SSO configuration scopes
Reporting cadence and target audiences
GIS layer requirements and spatial data sources

Proof points

KPI catalog with owners, definitions, SLAs, and refresh schedules
Data quality scorecard: pass rates, failure alerts, remediation time
Dashboard usage metrics: active users, queries/day, p95 query latency
Before/after report prep time analysis (manual vs automated)
GIS layer performance: render time, zoom responsiveness, concurrent users
Data pipeline SLA tracking: uptime %, late runs, failure recovery time

Built for procurement

Data quality SLAs: Measurable thresholds for completeness, accuracy, timeliness with automated alerts
GIS export formats: Shapefiles, GeoJSON, KML, ArcGIS Feature Service, WMS/WFS endpoints
Dashboard access controls: RBAC, row-level security, SSO/SAML integration, audit logs
Data lineage tracking: End-to-end visibility from source to dashboard with transformation documentation
Performance SLAs: Query latency targets (p95 < 3s), dashboard load time, concurrent user capacity
On-prem/GovCloud deployment: Air-gapped installation support, FedRAMP considerations
Compliance: SOC 2 Type II, HIPAA-ready architecture, data retention policies, PII handling