DJEURNIE

Enterprise Use Cases

Real-world scenarios where Data Estuary solves concrete data logistics challenges for distributed and enterprise architectures.

Use Case #1

Edge Processing for Bandwidth Optimization

❌ The Problem

A manufacturing company has 50 factories with machines generating high-volume logs (telemetry, sensor data, error logs). Sending all raw data to the cloud costs $30k/month in bandwidth alone.

  • •Massive bandwidth costs
  • •High latency for local analysis
  • •Cloud storage costs for mostly unused data
  • •Network failures block all data flow

✓ Data Estuary Approach

Deploy a BYOC cluster at each factory. Pipelines run on-premise to aggregate logs locally. Only summaries and alerts sync to cloud clusters for analysis.

  • ✓90% reduction in bandwidth costs
  • ✓Instant local analysis
  • ✓Minimal cloud storage needed
  • ✓Async replication tolerates network issues

The Solution in Action

A pipeline running on the factory cluster:

  • •Triggers every 5 minutes to fetch recent logs
  • •Groups logs by machine and severity level
  • •Aggregates counts, averages, and critical errors
  • •Creates summary entities that replicate to cloud
  • •Generates alerts when critical errors exceed threshold

Architecture Flow

Machines→ write logs →factory-cluster (on-prem)
↓ pipeline aggregates ↓
LogSummary entities→ replicate →aws-central

Result: 10GB/hour of raw logs becomes 50MB/hour of summaries. Network outages don't block local operations.

Use Case #2

Multi-Region Data Distribution

❌ The Problem

A global e-commerce platform needs to serve customer data with low latency worldwide, while complying with GDPR (EU data stays in EU) and other data sovereignty laws.

  • •High latency for cross-region requests
  • •Complex compliance requirements
  • •Manual sync logic between regions
  • •Inconsistent data across regions

✓ Data Estuary Approach

Define regional clusters with replication rules. Customer entities stay in their home region while product/order data replicates globally for analytics.

  • ✓Low-latency regional access
  • ✓Automatic compliance via topology
  • ✓Declarative replication rules
  • ✓Eventual consistency handled by platform

The Solution in Action

Three regional clusters with smart replication:

EU Cluster (eu-west-1)

  • • Stores all entity types: Customer, Order, Product
  • • Replicates Order & Product data to other regions
  • • Customer data never leaves EU (GDPR compliance)

US & Asia Clusters

  • • Same configuration as EU cluster
  • • Customer data stays in respective regions
  • • Order & Product data flows bi-directionally

Data sovereignty enforced by topology, not application logic

Data Flow

Customer data:

eu-west-1 only(stays in EU)

Order & Product data:

eu-west-1↔us-east-1↔ap-south-1

Result: Sub-50ms local reads, automatic compliance, unified analytics across regions.

Use Case #3

Composable Architecture Without ESB

❌ The Problem

An enterprise has separate business units (Orders, Inventory, Shipping) that need to coordinate. They've built a heavyweight ESB that becomes a bottleneck and development blocker.

  • •Tight coupling through ESB
  • •Slow development (ESB team gatekeeping)
  • •Complex transformation logic in ESB
  • •Single point of failure

✓ Data Estuary Approach

Each business unit owns their entities and pipelines. Data Estuary handles logistics (where data lives, how it moves). Business logic stays in-house.

  • ✓Teams work independently
  • ✓Loose coupling via entities
  • ✓Business logic in team's pipelines
  • ✓Distributed, no single failure point

The Solution in Action

Three teams, three pipelines, zero coordination overhead:

Orders Team Pipeline

  • • Triggered by API call
  • • Creates Order entity and replicates to all clusters
  • • Runs on their own cluster

Inventory Team Pipeline

  • • Automatically triggered when Order entity is created
  • • Checks inventory availability and reserves items
  • • Updates Order with inventory status
  • • No direct API calls to orders team

Shipping Team Pipeline

  • • Watches for Order updates matching conditions
  • • Triggers when inventory reserved AND payment confirmed
  • • Creates Shipment entity autonomously

Teams coordinate through entities, not meetings

How Teams Coordinate

1

Orders team creates Order entity → replicates to all clusters

2

Inventory team's pipeline triggers automatically, reserves inventory

3

Shipping team's pipeline triggers when order is ready, creates shipment

Result: No ESB, no tight coupling, no central bottleneck. Teams deploy independently.

Use Case #4

Hybrid Cloud Migration

❌ The Problem

A company wants to migrate from on-premise to cloud but can't do a "big bang" migration. They need to move gradually while keeping systems operational.

  • •Can't migrate everything at once
  • •Need to sync data between on-prem and cloud
  • •Risky cutover process
  • •Dual-run infrastructure is complex

✓ Data Estuary Approach

Set up bidirectional sync between on-prem and cloud clusters. Gradually migrate pipelines and workloads. Roll back easily if needed.

  • ✓Gradual, low-risk migration
  • ✓Automatic data sync
  • ✓Easy rollback if issues arise
  • ✓Hybrid mode as long as needed

The Solution in Action

Gradual migration with bidirectional sync:

Initial Setup

  • • Configure on-prem cluster with existing nodes
  • • Add new cloud cluster in AWS
  • • Enable bidirectional replication between them
  • • All data automatically syncs in both directions

Migration Process

  • • Move pipelines one at a time from on-prem to cloud
  • • Simply update pipeline's "runOn" configuration
  • • No changes to pipeline logic needed
  • • Data remains accessible from both locations

Final Cutover

  • • When ready, remove on-prem cluster from config
  • • All entities already live in cloud
  • • Can keep hybrid mode running indefinitely if needed

Migration as configuration change, not a rewrite

Migration Phases

Phase 1

Set up cloud cluster + bidirectional sync. Monitor sync health.

Phase 2

Migrate read-only pipelines first. Test thoroughly.

Phase 3

Gradually move write pipelines. Data automatically in sync.

Phase 4

Remove on-prem cluster when comfortable. No big-bang cutover.

Result: Zero-downtime migration over 6 months instead of risky weekend cutover.

Common Themes

Cost Optimization

Process data where it's generated. Reduce bandwidth and storage costs by sending only what's needed.

Deployment Flexibility

Run clusters anywhere: cloud, on-prem, edge. Mix and match based on technical and business requirements.

Team Autonomy

Teams own their pipelines and logic. No central gatekeepers or heavyweight coordination layers.

Does This Sound Familiar?

If these scenarios resonate with your architecture challenges, let's discuss how Data Estuary can help solve them.

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