Empirical Intelligence & BI Cockpits
Data Management & Business Intelligence
Transform fragmented, siloid enterprise records into an infallible single source of executive truth. Modern cloud lakehouses, sub-second BI cockpits, real-time CDC streams, and predictive machine learning models.
Turn disconnected data silos into an infallible single source of executive truth
Most enterprises do not suffer from a lack of data — they drown in it. Financial transactions in one core ledger, operational volumes in isolated spreadsheets, customer journeys locked inside SaaS tools, and logistics logs in legacy servers. When executive leadership demands consolidated EBITDA, cash runway, or cohort margins, manual reporting takes weeks of spreadsheet triage. WAGONN permanently eliminates reporting blindness.
We architect unified enterprise lakehouses, stream live transaction mutations via Change Data Capture (CDC), and deliver sub-second executive BI cockpits. From inventory turnover and supplier payment aging to customer lifetime value and predictive demand forecasting; our decision support systems provide leadership with complete empirical clarity to navigate market volatility.
High-Throughput Data Architecture & Intelligence Disciplines
From petabyte-scale lakehouses to real-time CDC streams, sub-second OLAP cockpits, sovereign data governance, and predictive MLOps: empirical certainty without compromise.
Modern Data Lakehouse & Warehousing
Architecting unified modern lakehouses on Snowflake, Databricks, or ClickHouse using open Iceberg formats to permanently eliminate contradictory metrics across departments.
Real-Time Streaming & Change Data Capture (CDC)
Non-intrusive stream pipelines reading transaction logs (WAL/Binlog) asynchronously via Debezium and Kafka, replicating state to data lakehouses in sub-500ms with zero DB locking.
Sub-Second BI & Executive Cockpits
Columnar OLAP engines and pre-aggregated data marts rendering cash runways, gross margins, and inventory turnover in under 120ms with dynamic drill-down.
Data Governance, Lineage & Statutory Compliance
Tracking data lineage from source to executive report with OpenLineage, enforcing granular cell-level RBAC and automated PII anonymization for full statutory audit readiness.
Predictive AI, Demand Forecasting & Feature Stores
Turning historical archives into predictive foresight; 90-day cash and stock forecasting, churn propensity, and dynamic pricing models backed by centralized Feature Stores.
Operational Reverse ETL & Automated Activation
Pushing refined analytical scores and anomaly triggers back into frontline CRM, sales, procurement, and messaging backbones to automate executive actions.
Mission-Critical Data Topologies & Implementation:
Empirical Rigor for Every Enterprise Model
From modern dbt-managed lakehouses to real-time Debezium CDC streaming, sub-second ClickHouse marts, and centralized Feast feature stores; inspect our production-ready data pipelines.
Consolidated Lakehouse & Modular dbt Transformations
Consolidating relational databases, SaaS platforms, and raw files into open Apache Iceberg formats, transformed by modular version-controlled dbt models with continuous testing.
-- models/marts/finance/fct_consolidated_cash_runway.sql
{{
config(
materialized = 'incremental',
unique_key = 'report_date',
schema = 'analytics_marts',
tags = ['finance', 'board_kpi']
)
}}
WITH daily_transactions AS (
SELECT
DATE_TRUNC('day', transaction_timestamp) AS report_date,
ledger_currency,
SUM(CASE WHEN flow_type = 'INFLOW' THEN amount_base_try ELSE 0 END) AS total_inflow_try,
SUM(CASE WHEN flow_type = 'OUTFLOW' THEN amount_base_try ELSE 0 END) AS total_outflow_try,
COUNT(DISTINCT transaction_id) AS transaction_volume
FROM {{ ref('stg_core_ledger_events') }}
{% if is_incremental() %}
WHERE transaction_timestamp >= (SELECT MAX(report_date) FROM {{ this }})
{% endif %}
GROUP BY 1, 2
)
SELECT
report_date,
total_inflow_try,
total_outflow_try,
(total_inflow_try - total_outflow_try) AS net_daily_cashflow,
SUM(total_inflow_try - total_outflow_try) OVER (
ORDER BY report_date
ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
) AS rolling_net_cash_reserve,
ROUND((total_inflow_try / NULLIF(total_outflow_try, 0))::NUMERIC, 2) AS liquidity_coverage_ratio
FROM daily_transactions;| Pipeline Node | Engine | Latency | Throughput | Status |
|---|---|---|---|---|
| cdc-debezium-stream | WAL Engine | 142ms | 14,200 msg/s | ● SYNC |
| kafka-event-mesh | Partition Mesh | 18ms | 48,500 msg/s | ● 0 LAG |
| clickhouse-olap-cluster | SummingMergeTree | 4ms | 1.2M row/s | ● OPTIMAL |
| dbt-analytics-mesh | Executive Marts | 100% | 42/42 Model | ● PASSED |
| reverse-etl-sync | CRM / Messaging | 310ms | 2,400 push/m | ● ACTIVE |
Empirical Certainty in Enterprise Decision Making
High-integrity data engineering eliminating guesswork, emotional forecasting, and fragmented spreadsheets across all operational tiers.
Single Source of Truth & Lakehouse Core
Consolidating core ledgers, transactional databases, and SaaS tools into an infallible lakehouse core to permanently eliminate conflicting numbers.
Zero-Latency Decision Making (CDC & Sub-Second BI)
No waiting for month-end reconciliation. Real-time CDC streams every transaction into board dashboards in under 500 milliseconds.
Sovereign Governance, Lineage & Compliance
Full compliance with GDPR, KVKK, and sovereign banking audits with automated lineage tracking, cell-level RLS, and dynamic PII tokenization.
End-to-End Enterprise Data & Intelligence Lifecycle
From initial data catalog audit to real-time CDC deployment, sub-second ClickHouse marts, and automated MLOps governance: our disciplined 4-stage data engineering methodology.
Data Cataloging, Quality Audit & Lakehouse Blueprint
Cataloging all databases, SaaS endpoints, and flat files, auditing schema drift, and formalizing the modern sovereign Lakehouse architecture blueprint.
Non-Intrusive CDC Deployment & Kafka Streams
Deploying Debezium non-intrusive CDC over database WAL logs, orchestrating Kafka streaming backbones, and authoring modular dbt transformation models.
Columnar ClickHouse Marts & Sub-Second Dashboards
Engineering ClickHouse SummingMergeTree materialized views; deploying sub-second executive cockpits for consolidated EBITDA and cash runway tracking.
OpenLineage Tracking, Feast Feature Stores & Reverse ETL
Automated data quality contracts, GDPR-compliant dynamic tokenization, predictive ML forecasting engines, and 24/7 mission-critical pipeline SLA monitoring.
Data Cataloging, Quality Audit & Lakehouse Blueprint
Cataloging all databases, SaaS endpoints, and flat files, auditing schema drift, and formalizing the modern sovereign Lakehouse architecture blueprint.
$ wagonn-data audit --tenant holding-enterprise --scan-all
[1/4] Scanning connected operational systems:
→ 18 Operational relational DBs (PostgreSQL, Oracle, MySQL)
→ 6 Enterprise SaaS APIs (CRM, Billing, Warehouse WMS, Logistics)
→ 4 Legacy file servers & flat data lakes
[2/4] Schema & Data Quality Profiling:
→ Identified 4,280 tables, 28,400 columns
→ Schema Drifts: 14% unindexed join paths flagged
→ Duplicate & Stale Records: 18.4% purged automatically
[3/4] Lakehouse Blueprint Formulation:
→ Open Table Format: Apache Iceberg on S3/MinIO
→ Streaming CDC Engine: Debezium WAL listener
→ High-Speed OLAP Marts: ClickHouse SummingMergeTree
>> TARGET DATA LAKEHOUSE BLUEPRINT SIGNED OFF & AUDITED ✓Enterprise Data Management & Business Intelligence
Frequently Asked Questions
Key inquiries regarding modern lakehouse architectures, non-intrusive CDC streaming, sub-second executive cockpits, and data governance. For custom consultations, you can contact us. We are delighted to assist your enterprise.
We consolidate all disparate enterprise assets—relational databases (PostgreSQL, MySQL, Oracle), NoSQL data, SaaS APIs (CRM, billing, marketing), logistics telematics, and IoT streams—into a high-performance modern Lakehouse architecture (Snowflake, Databricks, ClickHouse, Apache Iceberg). Utilizing dbt for modular transformation, automated schema evolution, and deduplication, we establish an infallible enterprise Single Source of Truth that eliminates conflicting numbers across departments.
Rather than bogging down production databases with invasive batch SQL queries, we deploy non-intrusive Change Data Capture (CDC via Debezium) engines that read transaction logs (WAL/Binlog) asynchronously, coupled with Apache Kafka / Redpanda event backbones. Every transaction, order, payment, or inventory change replicates to analytical storage in under 500 milliseconds with zero transactional overhead on source systems.
To end slow, spinning reports across millions of transaction rows, we deploy columnar OLAP engines (ClickHouse, BigQuery) and materialized analytical data marts. Powering dashboards via Power BI, Apache Superset, or custom headless React/Next.js interfaces, our executive cockpits render consolidated EBITDA, cash runway, inventory turnover, and cohort LTV in under 250 milliseconds with dynamic drill-down capabilities.
We map every field from raw ingestion to the final executive dashboard using automated lineage tooling (OpenLineage). Automated data quality contracts via Great Expectations and dbt prevent corrupted metrics from polluting reports. Granular Row-Level Security (RLS) and Role-Based Access Control (RBAC) ensure strict boundary isolation, while sensitive customer PII is dynamically masked and tokenized to meet stringent GDPR, KVKK, and sovereign banking audits.
We transform static historical archives into predictive enterprise intelligence. By engineering centralized Feature Stores and automated MLOps pipelines, we deploy real-time demand forecasting, dynamic pricing, churn prediction, and financial anomaly detection models directly over live data streams, feeding automated actionable alerts straight to department leadership.
Rather than letting high-value analytics sit idle inside dashboards, we operationalize warehouse data. Through automated Reverse ETL pipelines, refined customer propensity scores push back into CRM tools, supplier risk indicators flow into procurement portals, and anomaly alerts dispatch immediately to operational teams via Webhooks, Slack, or ticketing engines for instant remediation.