Data Pipeline Modernization
Rebuild the data foundation that BI runs on today and AI features depend on tomorrow.
The problem
Your data warehouse is a 2011 Oracle Exadata that nobody wants to touch. Your data team maintains 1,400 stored procedures and a long-tail of "do-not-modify" tables. Every new AI feature request hits the same wall: "the data is not ready." Your CFO's BI dashboards work. Your AI team's experiments do not, because the schema does not support what they need.
McKinsey's 2024 Building the AI-Ready Enterprise research finds that 67% of failed mid-market AI initiatives in APAC trace back to data infrastructure not the model itself.[^1] IDC adds that the typical mid-market data modernization program returns US$2.40 for every dollar spent over 36 months — primarily through analyst productivity, not infrastructure cost reduction.[^2]
Our approach
Sources: ERP / CRM / MES / IoT / SaaS / file shares / API endpoints
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Ingest layer
- Airbyte (open-source, 350+ connectors)
- custom Laravel-driven connectors for proprietary systems
- CDC (change data capture) via Debezium where source supports it
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Raw zone (S3 / Azure Data Lake / GCS / Alibaba OSS)
- immutable, source-system-shaped, indexed by ingest timestamp
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Transformation layer
- dbt (modular SQL-based transformation)
- tested, documented, version-controlled in Git
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Curated zone (Snowflake / BigQuery / Databricks / ClickHouse)
- star schema for BI, normalized for AI features
- documented in dbt docs + Atlan (or equivalent)
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Serving
- BI: Power BI, Tableau, Looker, Metabase
- AI: vector embeddings (pgvector / Qdrant), feature store (Feast)
- APIs: Hasura (GraphQL) or custom Laravel REST endpoints
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Observability: Monte Carlo or Great Expectations for data quality;
Datadog for pipeline monitoring
Who it is for
- A 700-person retail group in Malaysia and Indonesia with siloed POS, inventory, and customer data across 14 country systems.
- A 400-person manufacturer in Korea with shop-floor IoT data, MES data, and ERP data living in three unrelated stacks.
- A 1,000-person professional-services firm in Hong Kong with consultant time-entry, client-engagement, and financial data trapped in disconnected systems.
Tech stack
- Ingest: Airbyte (open-source default), Fivetran (managed alternative), custom Laravel jobs, Debezium for CDC
- Storage: Snowflake (default), Google BigQuery, Databricks, ClickHouse for high-volume analytics workloads
- Transformation: dbt Core or dbt Cloud (modular, tested, documented SQL); Apache Spark on Databricks for very large datasets
- Orchestration: Airflow, Dagster, or Prefect — pick based on team familiarity
- Quality: Great Expectations (open-source), Monte Carlo (managed) for production-grade observability
- Catalog: Atlan or DataHub for data discovery and lineage
- Backend tooling: Laravel 12 for in-house ingestion connectors and admin UI
Integration list
SAP ECC and S/4HANA, Oracle EBS, NetSuite, Workday, Microsoft Dynamics, Salesforce, HubSpot, Snowflake, BigQuery, Databricks, ClickHouse, MySQL, PostgreSQL, MongoDB, Apache Kafka, Apache Pulsar, AWS Kinesis, Azure Event Hubs, MQTT for IoT, Power BI, Tableau, Looker, Metabase, Apache Superset.
Deployment timeline
| Week | Activity |
|---|---|
| Week 1-2 | Data audit; pick 2-3 priority data domains; success metrics agreed |
| Week 3-4 | Ingest layer deployed for priority sources; raw zone populated |
| Week 5-7 | dbt transformation models built; first curated tables published |
| Week 8 | BI cutover for first dashboards; old reports parallel-run |
| Week 9-11 | AI-feature support: vector embeddings, feature store, retrieval APIs |
| Week 12-16 | Expand to remaining sources; data quality monitoring live |
Mini-ROI
A 700-person Malaysian-Indonesian retail group migrated from on-premise Oracle to Snowflake on AWS Singapore in 14 weeks. BI dashboard refresh time dropped from 47 minutes to 90 seconds. AI feature time-to-build dropped from "blocked" to 6 weeks per feature. Year-one analyst productivity gain: equivalent to 4.2 FTE redirected to higher-value work.
McKinsey benchmarks data modernization ROI at US$420,000-US$1.2M annually per 100-person mid-market enterprise, with 70-80% of that value coming from AI-feature enablement that was previously infeasible.[^1]
Pricing tiers
| Tier | Setup (one-time) | Monthly run cost (excl. data warehouse spend) | Best for |
|---|---|---|---|
| Starter | US$48,000 - US$95,000 | From US$2,800/mo | 3-5 sources, single warehouse, BI-focused with AI hooks. |
| Scale | US$120,000 - US$280,000 | From US$6,500/mo | 8-15 sources, multi-domain, AI feature store, data quality monitoring. |
| Strategic | US$320,000 - US$680,000 | From US$14,000/mo | Group-level, multi-region, real-time CDC, federated governance, dedicated FinOps. |
All tiers include the data quality dashboard and a quarterly architecture review.
Frequently asked questions
Will our existing dashboards keep working during the migration? Yes. We parallel-run the old and new pipelines for the first 6-8 weeks per domain. Dashboard logic is repointed only when the new pipeline produces matching results. We have not had a dashboard outage during cutover in the last 11 deployments.
Snowflake, BigQuery, or Databricks — how do you choose? Based on existing footprint, pricing model, regulatory regime, and team familiarity. Snowflake is the most common default. BigQuery wins where Google Workspace is dominant. Databricks wins where heavy ML or unstructured-data workloads dominate. We do not have a vendor preference.
Can we keep our Oracle or SAP HANA warehouse and just add a lakehouse? Yes. Many clients keep existing warehouses for regulated workloads and add a lakehouse for AI-feature support. We design federated query patterns that let analysts and AI features query both without users seeing the seams.
What about data residency for Mainland China? Alibaba Cloud MaxCompute, plus Lindorm for vector storage, with PIPL-compliant cross-border controls. Or fully isolated stacks where the China subsidiary runs its own data platform with controlled aggregation reporting only.
How do you handle data quality? Three layers: source-system schema validation (catches malformed inputs at ingest), dbt tests on every transformation (catches regressions during build), and Monte Carlo or Great Expectations on production tables (catches drift in live data). Quality SLAs reported monthly.
Will analysts have to learn new tools? For SQL-based analysts, the migration is largely transparent — they keep writing SQL against curated tables. For Python-based analysts, we provide notebook environments (Hex, Deepnote, or self-hosted JupyterHub) connected to the lakehouse.
Can the platform support real-time use cases? Yes, with CDC via Debezium and stream processing via Apache Flink or Materialize. Real-time use cases are typically 20-30% of overall workload — most of the platform serves batch and near-real-time (15-minute freshness) demand.
How does this connect to the rest of the AIMenta solution stack? Directly. The Knowledge Base / RAG Stack consumes documents from the data lake. The Compliance Monitoring Engine consumes audit logs. The Sales Enablement Copilot consumes CRM data. The Finance Automation Platform consumes ERP data. The data platform is the foundation under all of them.
Where this is most often deployed
Industries where AIMenta frequently scopes this kind of solution.
Beyond this solution
Browse our other productized solutions, plus the verticals and Asian markets where they ship.
Other solutions
By industry
By Asian market
Frequently asked questions
What is the typical scope of a data pipeline modernisation engagement?
A standard engagement covers: discovery and current-state architecture mapping (weeks 1–2), target-state design and tool selection (weeks 3–4), parallel-run migration of top-3 priority pipelines (weeks 5–10), testing and cutover (weeks 11–12), and hypercare (weeks 13–16). Pipelines are migrated progressively, not in a big-bang cutover that risks production downtime.
Which cloud data platform do you recommend — Snowflake, BigQuery, or Databricks?
We are platform-agnostic and select based on three factors: your existing cloud commitments (AWS/Azure/GCP), workload profile (SQL-heavy analytics vs. ML training vs. real-time streaming), and team capability. Snowflake suits SQL-first analytics teams; Databricks suits ML/AI workloads; BigQuery suits organisations already deep in the Google ecosystem. We present a scored comparison in week 3 of the engagement.
How do you ensure no data is lost or corrupted during migration?
Every migrated pipeline runs in parallel against the legacy source for a minimum of 4 weeks before cutover. Reconciliation reports compare row counts, aggregate sums, and sample record hashes between old and new pipelines daily. Cutover only proceeds when reconciliation passes 100% across three consecutive daily runs. The legacy pipeline is retained for 60 days post-cutover as a rollback option.
Don't see exactly what you need?
Most engagements start as custom scopes. Send us your problem; we'll tell you whether one of our productized solutions fits — or what a custom build looks like.