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SKYLIFTAI
SkyliftAI MigDB — Use Case

Data
Warehousing
& Analytics

Stream every transaction from your OLTP databases into your data warehouse in real time — no nightly batches, no stale dashboards, no blocked source systems. Decisions powered by data that is always current.

⚡ Sub-second freshness
🏗️ Zero-ETL pipeline
🔄 CDC Streaming
📊 Redshift · Snowflake · BigQuery
🤖 AI-powered transformation
Pipeline Throughput LIVE
2.4M
rows / minute streaming to warehouse
Replication Lag
OLTP → Stage
0.3s
Stage → DW
1.8s
DW → Lake
4.2s
Analytics Query Freshness
Live
vs. nightly batch: 8–12 hours stale
CDC Pipeline Status
Oracle OLTP → Extract RUNNING
Data Pump → Redshift Stage RUNNING
Transform Layer (AI) APPLYING
Snowflake Replicat RUNNING
The Problem

The Nightly Batch Is
Killing Your Analytics

Traditional ETL pipelines extract data from OLTP systems on a schedule — nightly, hourly, or every few minutes. By the time your analysts run their dashboards, the data is already stale. Decisions are made on yesterday's reality. SkyliftAI MigDB's CDC-based streaming pipeline eliminates the batch entirely, delivering a continuous, sub-second flow of changes from your production database straight into your data warehouse.

"Organisations running real-time analytics outperform batch-based peers by 2.5× on decision velocity. The difference isn't compute power — it's data freshness." — SkyliftAI Data Intelligence Report, 2025
Architecture

The Complete Data Pipeline Stack

Source
Systems
🏦 Oracle OLTP
🛒 PostgreSQL Commerce
🏥 MySQL CRM
🏭 SQL Server ERP
📱 Aurora Microservices
Powerful
CDC Layer
⚡ SkyliftAI Extract
→
📄 Trail Files
→
🔄 Data Pump
→
🔒 TLS Transfer
AI Transform
Layer
🤖 Schema Mapping
🔧 Type Coercion
🧹 Data Cleanse
🔗 Key Enrichment
📐 Dimensional Model
Data
Warehouse
🏗️ Staging Tables
→
⚙️ Replicat Apply
→
📦 Fact Tables
→
🗂️ Dim Tables
Analytics
& BI
📊 Tableau / Power BI
🔎 Redshift Spectrum
❄️ Snowflake
☁️ BigQuery
🐍 Python / dbt
🤖 ML Pipelines
End-to-End CDC Streaming Pipeline — OLTP to Analytics
🗄️
OLTP Source
Oracle / PG / MySQL
›
Redo Logs
⚡
CDC Extract
SkyliftAI Engine
›
Trail Files
🤖
AI Transform
Map · Cleanse · Enrich
›
Pump
📥
Staging
DW Stage Tables
›
Replicat
🏗️
Data Warehouse
Redshift / Snowflake
›
Query
📊
BI / Analytics
Always fresh
<2s
OLTP-to-warehouse
end-to-end latency
10×
Faster analytics
vs. nightly batch ETL
0
Load on OLTP source —
log-based CDC only
100%
Transaction coverage —
every INSERT UPDATE DELETE
5+
Target platforms:
Redshift, Snowflake, BigQuery…
Capabilities

What Makes SkyliftAI's DW
Pipeline Different

Every component of the pipeline is AI-augmented — from schema mapping to anomaly detection — delivering accuracy and speed that manual ETL pipelines cannot match.

⚡
Log-Based CDC — Zero Source Impact
SkyliftAI reads directly from the database redo/binary log — never querying the OLTP tables directly. Your production database experiences zero additional load from the analytics pipeline, regardless of how many downstream consumers are connected.
Redo Log Mining Zero Query Load Non-Intrusive
🤖
AI-Powered Schema Mapping & Transformation
OLTP schemas are normalised — data warehouses are dimensional. SkyliftAI's AI transform layer automatically maps source tables to target fact and dimension tables, handles type coercions, flattens nested structures, and applies business rules in the pipeline — no hand-coded ETL scripts required.
Auto Dimensional Mapping Type Coercion AI Business Rules
🔗
Multi-Source Fan-Out
A single SkyliftAI deployment can stream changes from multiple heterogeneous OLTP sources — Oracle ERP, MySQL CRM, PostgreSQL commerce — simultaneously into a unified data warehouse. The AI layer resolves cross-source key conflicts and creates a single, consistent analytical view.
Multi-Source Cross-DB Keys Unified Schema
🎯
Multi-Target Delivery
The same CDC stream can fan out to multiple targets simultaneously — Amazon Redshift for SQL analytics, Snowflake for data sharing, Apache Kafka for real-time event processing, Amazon S3 for the data lake, and Google BigQuery for ML pipelines. One pipeline feeds them all.
Redshift Snowflake Kafka BigQuery S3 Lake
🧹
Real-Time Data Quality Enforcement
SkyliftAI's DataSentinel module validates data quality in-flight before records reach the warehouse. Nullability violations, referential integrity breaks, and value range anomalies are caught, quarantined, and alerted on in the pipeline — not discovered after analysts run broken reports.
In-Flight Validation Quarantine Queue Quality Metrics
📈
Slowly Changing Dimensions (SCD) Support
Data warehouses require history — not just the current state of a record, but every version it has ever been. SkyliftAI natively handles SCD Type 1, 2, and 3 patterns, automatically managing effective dates, current flags, and historical snapshots as UPDATE events arrive from the source.
SCD Type 1 SCD Type 2 SCD Type 3 History Tracking
Industry Use Cases

Where Real-Time Data Warehousing
Changes the Business

The difference between batch analytics and real-time analytics isn't just speed — it's the nature of the decisions that become possible. These are the scenarios where data freshness is a competitive advantage.

💹
Real-Time Financial Reporting
CFOs and finance teams need P&L, revenue, and cash flow data that reflects transactions from minutes ago — not last night's close. SkyliftAI streams every financial transaction from the ERP into Redshift as it is posted, enabling live management reporting dashboards with sub-minute data freshness.
Revenue visible within 45 seconds of transaction commit
🛒
Live E-Commerce Analytics
Merchandising teams need to see which products are selling, which promotions are converting, and which inventory lines are running low — right now, during the campaign. CDC streaming delivers order, cart, and inventory events into Snowflake within seconds of each customer action.
Campaign performance dashboards updated every 30 seconds
🏦
Fraud Detection Pipelines
Fraud detection ML models require a continuous stream of transaction events, not hourly batches. SkyliftAI feeds every card transaction into a Kafka topic and simultaneously into BigQuery, where ML feature pipelines train and score in near real time — catching patterns as they emerge.
Transaction events in ML pipeline within 2 seconds
🏥
Healthcare Operations Intelligence
Hospital operations teams track bed occupancy, patient flow, supply consumption, and staff allocation in real time. SkyliftAI streams EHR and operational system events into a centralised data warehouse, powering live operational dashboards that help staff respond to capacity pressures before they become crises.
Bed and staffing dashboards reflect live EHR state
🏭
Supply Chain Visibility
Procurement and logistics teams need to see purchase orders, GRNs, shipment updates, and inventory movements as they happen across multiple ERP instances. SkyliftAI's multi-source fan-out streams all of these into a unified supply chain analytics layer, enabling end-to-end supply chain visibility in one dashboard.
End-to-end supply chain latency reduced from 24h to <60s
🎮
Product & User Behaviour Analytics
Product managers need to see how users are interacting with new features the moment they deploy — not the next morning. SkyliftAI streams user activity events, feature usage flags, and session data from the application database into Snowflake, powering live product analytics without a separate event tracking infrastructure.
Feature adoption visible within 60 seconds of rollout
Comparison

Batch ETL vs. SkyliftAI
CDC Streaming

Dimension Nightly Batch ETL Hourly Micro-Batch SkyliftAI CDC Streaming
Data Freshness 8–12 hours stale 30–60 mins stale < 2 seconds
Source Database Load High — full table scans Medium — repeated queries Zero — log-based only
Delete / Update Capture Often missed or approximated Partial, with watermarks Complete — all DML captured
SCD Type 2 History Manual scripting required Complex, error-prone Native, automatic
Schema Change Handling Manual pipeline updates Manual pipeline updates AI-assisted auto-adapt
Multi-Target Fan-Out Separate jobs per target Separate jobs per target Single stream, all targets
Data Quality Checks Post-load, discovered late Post-load, discovered late In-flight, real-time
Analytics Decision Velocity Next-day decisions Near-hour decisions Real-time decisions
Operational Complexity Medium (scheduled jobs) High (orchestration) Low (AI-managed pipeline)

Your Warehouse,
Always Current.

Stop making decisions on yesterday's data. SkyliftAI MigDB turns your data warehouse into a living, breathing reflection of your business — updated in real time, continuously, with every transaction that matters.

<2s
OLTP-to-warehouse latency
10×
Faster analytics vs batch
0
Source database load
5+
Target platforms supported