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 ---
 title: "Apache Ignite Architecture Series: Part 1 - When Multi-System Complexity Compounds at Scale"
 author: "Michael Aglietti"
 date: 2025-11-25
 tags:
     - apache
     - ignite
 ---

 p Apache Ignite shows what really happens once your #[em good enough] multi-system setup starts cracking under high-volume load. This piece breaks down why the old stack stalls at scale and how a unified, memory-first architecture removes the latency tax entirely.

 <!-- end -->

 p Your high-velocity application started with smart architectural choices. PostgreSQL for reliable transactions, Redis for fast cache access, custom processing for specific business logic. These decisions enabled initial success and growth.

 p But success changes the game. Your application now processes thousands of events per second and customers expect microsecond response times. Real-time insights drive competitive advantage. The same architectural choices that enabled growth now create performance bottlenecks that compound with every additional event.

 p.
   #[strong At high event volumes, data movement between systems becomes the primary performance constraint].

 hr

 h3 The Scale Reality for High-Velocity Applications

 p As event volume grows, architectural compromises that once seemed reasonable at lower scale become critical bottlenecks. Consider a financial trading platform, gaming backend, or IoT processor handling tens of thousands of operations per second.

 h3 Event Processing Under Pressure

 p #[strong High-frequency event characteristics]
 ul
   li Events arrive faster than traditional batch processing can handle
   li Each event requires immediate consistency checks against live data
   li Results must update multiple downstream systems simultaneously
   li Network delays compound into user-visible lag
   li #[strong Traffic spikes create systemic pressure] — traditional stacks drop connections or crash when overwhelmed

 p #[strong The compounding effect]
 ul
   li At 100 events per second, network latency of 2ms adds minimal overhead.
   li At 10,000 events per second, that same 2ms latency creates a 20-second processing backlog within system boundaries.
   li During traffic spikes (50,000+ events per second), traditional systems collapse entirely, dropping connections and losing data when they're needed most.
 p The math scales against you.

 hr

 h3 When Smart Choices Become Scaling Limits

 p #[strong Initial Architecture, works great at lower scale:]

 pre.mermaid.
     flowchart TB
         subgraph "Event Processing"
             Events[High-Volume Events<br/>10,000/sec]
         end

         subgraph "Multi-System Architecture"
             Redis[(Cache<br/>Session Data<br/>2ms latency)]
             PostgreSQL[(Relational DB<br/>Transaction Data<br/>5ms latency)]
             Processing[Custom Processing<br/>Business Logic<br/>3ms latency]
         end

         Events --> Redis
         Events --> PostgreSQL
         Events --> Processing

         Redis <-->|Sync Overhead| PostgreSQL
         PostgreSQL <-->|Data Movement| Processing
         Processing <-->|Cache Updates| Redis

 p #[strong What happens at scale]
 ul
   li #[strong Network-latency tax:] Every system hop adds milliseconds that compound
   li #[strong Synchronization delays:] Keeping systems consistent creates processing queues
   li #[strong Memory overhead:] Each system caches the same data in different formats
   li #[strong Consistency windows:] Brief periods where systems show different data states

 hr

 h3 The Hidden Cost of Multi-System Success

 p #[strong Data Movement Overhead:]

 p Your events don't just need processing, they need processing that maintains consistency across all systems.

 p Each event triggers:
 ul
   li #[strong Cache lookup (Redis):] ≈ 0.5 ms network + processing
   li #[strong Transaction validation (PostgreSQL):] ≈ 2 ms network + disk I/O
   li #[strong Business-logic execution (Custom):] ≈ 1ms processing + data marshalling
   li #[strong Result synchronization (across systems):] ≈ 3 ms coordination overhead

 p #[strong Minimum per-event cost ≈ 7 ms before business logic.]
 p At 10,000 events/s, you’d need 70 seconds of processing capacity just #[strong for data movement] per real-time second!

 hr

 h3 The Performance Gap That Grows With Success

 h3 Why Traditional Options Fail

 p #[strong Option 1: Scale out each system]
 ul
   li #[strong Strategy]: Add cache clusters, database replicas, processing nodes
   li #[strong Result]: More systems to coordinate, exponentially more complexity
   li #[strong Reality]: Network overhead grows faster than processing capacity

 p #[strong Option 2: Custom optimization]
 ul
   li #[strong Strategy]: Build application-layer caching, custom consistency protocols
   li #[strong Result]: Engineering team maintains complex, system-specific optimizations
   li #[strong Reality]: Solutions don't generalize; each optimization creates technical debt

 p #[strong Option 3: Accept compromises]
 ul
   li #[strong Strategy]: Use async processing, eventual consistency,  and accept delayed insights
   li #[strong Result]: Business requirements compromised to fit architectural limitations
   li #[strong Reality]: Competitive disadvantage as customer expectations grow

 hr

 h3 The Critical Performance Gap

 table(style="borderlicollapse: collapse; margin: 20px 0; width: 100%;")
   thead
     tr
       th(style="border: 1px solid #ddd; padding: 12px; background-color: #f5f5f5; text-align: left;") Component
       th(style="border: 1px solid #ddd; padding: 12px; background-color: #f5f5f5; text-align: left;") Optimized for
       th(style="border: 1px solid #ddd; padding: 12px; background-color: #f5f5f5; text-align: left;") Typical Latency
   tbody
     tr
       td(style="border: 1px solid #ddd; padding: 12px;") Database
       td(style="border: 1px solid #ddd; padding: 12px;") ACID transactions
       td(style="border: 1px solid #ddd; padding: 12px;") Milliseconds
     tr
       td(style="border: 1px solid #ddd; padding: 12px;") Cache
       td(style="border: 1px solid #ddd; padding: 12px;") Access speed
       td(style="border: 1px solid #ddd; padding: 12px;") Microseconds
     tr
       td(style="border: 1px solid #ddd; padding: 12px;") Compute
       td(style="border: 1px solid #ddd; padding: 12px;") Throughput
       td(style="border: 1px solid #ddd; padding: 12px;") Minutes – hours


 p Applications needing #[strong microsecond insights] on #[strong millisecond transactions] have no good options at scale in traditional architectures.

 p During traffic spikes, traditional architectures either drop connections (data loss) or degrade performance (missed SLAs). High-velocity applications need intelligent flow control that guarantees stability under pressure while preserving data integrity.

 hr

 h3 Event Processing at Scale

 p #[strong Here's what traditional multi-system event processing costs:]

 pre
   code.
     // Traditional multi-system event processing
     long startTime = System.nanoTime();

     // 1. Cache lookup for session data
     String sessionData = redisClient.get("session:" + eventId);  // ~500μs network
     if (sessionData == null) {
         sessionData = postgresDB.query("SELECT * FROM sessions WHERE id = ?", eventId);  // ~2ms fallback
         redisClient.setex("session:" + eventId, 300, sessionData);  // ~300μs cache update
     }

     // 2. Transaction processing
     postgresDB.executeTransaction(tx -> {  // ~2-5ms transaction
         tx.execute("INSERT INTO events VALUES (?, ?, ?)", eventId, userId, eventData);
         tx.execute("UPDATE user_stats SET event_count = event_count + 1 WHERE user_id = ?", userId);
     });

     // 3. Custom processing with consistency coordination
     ProcessingResult result = customProcessor.process(eventData, sessionData);  // ~1ms processing
     redisClient.setex("result:" + eventId, 600, result);  // ~300μs result caching

     // 4. Synchronization across systems
     ensureConsistency(eventId, sessionData, result);  // ~2-3ms coordination

     long totalTime = System.nanoTime() - startTime;
     // Total: 6-12ms per event (not including queuing delays)


 p #[strong Compound Effect at Scale:]

 table(style="border-collapse: collapse; margin: 20px 0; width: 100%;")
   thead
     tr
       th(style="border: 1px solid #ddd; padding: 12px; background-color: #f5f5f5; text-align: left;") Rate
       th(style="border: 1px solid #ddd; padding: 12px; background-color: #f5f5f5; text-align: left;") Required processing time / s
   tbody
     tr
       td(style="border: 1px solid #ddd; padding: 12px;") 1,000 events / s
       td(style="border: 1px solid #ddd; padding: 12px;") 6–12 s
     tr
       td(style="border: 1px solid #ddd; padding: 12px;") 5,000 events / s
       td(style="border: 1px solid #ddd; padding: 12px;") 30–60 s
     tr
       td(style="border: 1px solid #ddd; padding: 12px;") 10,000 events / s
       td(style="border: 1px solid #ddd; padding: 12px;") 60–120 s

 p #[strong The math doesn’t work:] parallelism helps, but coordination overhead grows exponentially with system count.

 hr

 h3 Real-World Breaking Points
 ul
   li #[strong Financial services]: Trading platforms hitting 10,000+ trades/second discover that compliance reporting delays impact trading decisions.
   li #[strong Gaming platforms]: Multiplayer backends processing user actions find that leaderboard updates lag behind gameplay events.
   li #[strong IoT analytics]: Manufacturing systems processing sensor data realize that anomaly detection arrives too late for preventive action.

 hr

 h3 The Apache Ignite Alternative

 h3 Eliminating Multi-System Overhead

 pre.mermaid.
     flowchart TB
         subgraph "Event Processing"
             Events[High-Volume Events<br/>10,000/sec]
         end

         subgraph "Apache Ignite Platform"
             subgraph "Collocated Processing"
                 Memory[Memory-First Storage<br/>Optimized Access Times]
                 Transactions[MVCC Transactions<br/>ACID Guarantees]
                 Compute[Event Processing<br/>Where Data Lives]
             end
         end

         Events --> Memory
         Memory --> Transactions
         Transactions --> Compute

         Memory <-->|Minimal Copying| Transactions
         Transactions <-->|Collocated| Compute
         Compute <-->|Direct Access| Memory

 p #[strong Key difference:] events process #[strong where the data lives], eliminating inter-system latency.

 hr

 h3 Apache Ignite Performance Reality Check

 p #[strong Here's the same event processing with integrated architecture:]

 pre
   code.
     // Apache Ignite integrated event processing
     try (IgniteClient client = IgniteClient.builder().addresses("cluster:10800").build()) {
         // Single integrated transaction spanning cache, database, and compute
         client.transactions().runInTransaction(tx -> {
             // 1. Access session data (in memory, no network overhead)
             Session session = client.tables().table("sessions")
                 .keyValueView().get(tx, Tuple.create().set("id", eventId));

             // 2. Process event with ACID guarantees (same memory space)
             client.sql().execute(tx, "INSERT INTO events VALUES (?, ?, ?)",
                                eventId, userId, eventData);

             // 3. Execute processing collocated with data
             ProcessingResult result = client.compute().execute(
                 JobTarget.colocated("events", Tuple.create().set("id", eventId)),
                 EventProcessor.class, eventData);

             // 4. Update derived data (same transaction, guaranteed consistency)
             client.sql().execute(tx, "UPDATE user_stats SET event_count = event_count + 1 WHERE user_id = ?", userId);

             return result;
         });
     }
     // Result: microsecond-range event processing through integrated architecture

 p Processing 10,000 events/s is achievable with integrated architecture eliminating network overhead.

 hr

 h3 The Unified Data-Access Advantage

 p #[strong Here's what eliminates the need for separate systems:]

 pre
   code.
     // The SAME data, THREE access paradigms, ONE system
     Table customerTable = client.tables().table("customers");

     // 1. Key-value access for cache-like performance
     Customer customer = customerTable.keyValueView()
         .get(tx, Tuple.create().set("customer_id", customerId));

     // 2. SQL access for complex analytics
     ResultSet&lt;SqlRow&gt analytics = client.sql().execute(tx,
         "SELECT segment, AVG(order_value) FROM customers WHERE region = ?", region);

     // 3. Record access for type-safe operations
     CustomerRecord record = customerTable.recordView()
         .get(tx, new CustomerRecord(customerId));

     // All three: same schema, same data, same transaction model

 p #[strong Eliminates:]
 ul
   li #[strong Cache API] for cache operations
   li Data movement during #[strong distributed joins] for analytical queries
   li #[strong Custom mapping logic] for type-safe access
   li #[strong Data synchronization] between cache and database
   li #[strong Schema drift risks] across different systems

 p #[strong Unified advantage:] One schema, one transaction model, multiple access paths.

 hr

 h3 Apache Ignite Architecture Preview

 p The ability to handle high-velocity events without multi-system overhead requires specific technical innovations:
 ul
   li #[strong Memory-first storage]: Event data lives in memory with optimized access times typically under 10 microseconds for cache-resident data
   li #[strong Collocated compute]: Processing happens where data already exists, eliminating movement
   li #[strong Integrated transactions]: ACID guarantees span cache, database, and compute operations
   li #[strong Minimal data copying]: Events process against live data through collocated processing and direct memory access

 p These innovations address the compound effects that make multi-system architectures unsuitable for high-velocity applications.

 hr

 h3 Business Impact of Architectural Evolution

 h3 Cost Efficiency
 ul
   li #[strong Reduced infrastructure]: one platform instead of several
   li #[strong Lower network costs]: eliminate inter-system bandwidth overhead
   li #[strong Simplified operations]: fewer platforms to monitor, backup, and scale

 h3 Performance Gains
 ul
   li #[strong Microsecond latency]: eliminates network overhead
   li #[strong Higher throughput]: more events on existing hardware
   li #[strong Predictable scaling]: consistent performance under load

 h3 Developer Experience
 ul
   li #[strong Single API]: one model for all data operations
   li #[strong Consistent behavior]: no synchronization anomalies
   li #[strong Faster delivery]: one integrated system to test and debug

 hr

 h3 The Architectural Evolution Decision

 p Every successful application reaches this point: the architecture that once fueled growth now constrains it.

 p #[strong The question isn't whether you'll hit multi-system scaling limits. It's how you'll evolve past them.]

 p #[strong Apache Ignite] consolidates transactions, caching, and compute into a single, memory-first platform designed for high-velocity workloads. Instead of managing the compound complexity of coordinating multiple systems at scale, you consolidate core operations into a platform designed for high-velocity applications.

 p Your winning architecture doesn't have to become your scaling limit. It can evolve into the foundation for your next phase of growth.

 hr
 br
 |
 p #[em Return next Tuesday for Part 2, where we examine how Apache Ignite’s memory-first architecture enables optimized event processing while maintaining durability, forming the basis for true high-velocity performance.]
	---
	title: "Apache Ignite Architecture Series: Part 1 - When Multi-System Complexity Compounds at Scale"
	author: "Michael Aglietti"
	date: 2025-11-25
	tags:
	- apache
	- ignite
	---

	p Apache Ignite shows what really happens once your #[em good enough] multi-system setup starts cracking under high-volume load. This piece breaks down why the old stack stalls at scale and how a unified, memory-first architecture removes the latency tax entirely.

	<!-- end -->

	p Your high-velocity application started with smart architectural choices. PostgreSQL for reliable transactions, Redis for fast cache access, custom processing for specific business logic. These decisions enabled initial success and growth.

	p But success changes the game. Your application now processes thousands of events per second and customers expect microsecond response times. Real-time insights drive competitive advantage. The same architectural choices that enabled growth now create performance bottlenecks that compound with every additional event.

	p.
	#[strong At high event volumes, data movement between systems becomes the primary performance constraint].

	hr

	h3 The Scale Reality for High-Velocity Applications

	p As event volume grows, architectural compromises that once seemed reasonable at lower scale become critical bottlenecks. Consider a financial trading platform, gaming backend, or IoT processor handling tens of thousands of operations per second.

	h3 Event Processing Under Pressure

	p #[strong High-frequency event characteristics]
	ul
	li Events arrive faster than traditional batch processing can handle
	li Each event requires immediate consistency checks against live data
	li Results must update multiple downstream systems simultaneously
	li Network delays compound into user-visible lag
	li #[strong Traffic spikes create systemic pressure] — traditional stacks drop connections or crash when overwhelmed

	p #[strong The compounding effect]
	ul
	li At 100 events per second, network latency of 2ms adds minimal overhead.
	li At 10,000 events per second, that same 2ms latency creates a 20-second processing backlog within system boundaries.
	li During traffic spikes (50,000+ events per second), traditional systems collapse entirely, dropping connections and losing data when they're needed most.
	p The math scales against you.

	hr

	h3 When Smart Choices Become Scaling Limits

	p #[strong Initial Architecture, works great at lower scale:]

	pre.mermaid.
	flowchart TB
	subgraph "Event Processing"
	Events[High-Volume Events<br/>10,000/sec]
	end

	subgraph "Multi-System Architecture"
	Redis[(Cache<br/>Session Data<br/>2ms latency)]
	PostgreSQL[(Relational DB<br/>Transaction Data<br/>5ms latency)]
	Processing[Custom Processing<br/>Business Logic<br/>3ms latency]
	end

	Events --> Redis
	Events --> PostgreSQL
	Events --> Processing

	Redis <-->\|Sync Overhead\| PostgreSQL
	PostgreSQL <-->\|Data Movement\| Processing
	Processing <-->\|Cache Updates\| Redis

	p #[strong What happens at scale]
	ul
	li #[strong Network-latency tax:] Every system hop adds milliseconds that compound
	li #[strong Synchronization delays:] Keeping systems consistent creates processing queues
	li #[strong Memory overhead:] Each system caches the same data in different formats
	li #[strong Consistency windows:] Brief periods where systems show different data states

	hr

	h3 The Hidden Cost of Multi-System Success

	p #[strong Data Movement Overhead:]

	p Your events don't just need processing, they need processing that maintains consistency across all systems.

	p Each event triggers:
	ul
	li #[strong Cache lookup (Redis):] ≈ 0.5 ms network + processing
	li #[strong Transaction validation (PostgreSQL):] ≈ 2 ms network + disk I/O
	li #[strong Business-logic execution (Custom):] ≈ 1ms processing + data marshalling
	li #[strong Result synchronization (across systems):] ≈ 3 ms coordination overhead

	p #[strong Minimum per-event cost ≈ 7 ms before business logic.]
	p At 10,000 events/s, you’d need 70 seconds of processing capacity just #[strong for data movement] per real-time second!

	hr

	h3 The Performance Gap That Grows With Success

	h3 Why Traditional Options Fail

	p #[strong Option 1: Scale out each system]
	ul
	li #[strong Strategy]: Add cache clusters, database replicas, processing nodes
	li #[strong Result]: More systems to coordinate, exponentially more complexity
	li #[strong Reality]: Network overhead grows faster than processing capacity

	p #[strong Option 2: Custom optimization]
	ul
	li #[strong Strategy]: Build application-layer caching, custom consistency protocols
	li #[strong Result]: Engineering team maintains complex, system-specific optimizations
	li #[strong Reality]: Solutions don't generalize; each optimization creates technical debt

	p #[strong Option 3: Accept compromises]
	ul
	li #[strong Strategy]: Use async processing, eventual consistency, and accept delayed insights
	li #[strong Result]: Business requirements compromised to fit architectural limitations
	li #[strong Reality]: Competitive disadvantage as customer expectations grow

	hr

	h3 The Critical Performance Gap

	table(style="borderlicollapse: collapse; margin: 20px 0; width: 100%;")
	thead
	tr
	th(style="border: 1px solid #ddd; padding: 12px; background-color: #f5f5f5; text-align: left;") Component
	th(style="border: 1px solid #ddd; padding: 12px; background-color: #f5f5f5; text-align: left;") Optimized for
	th(style="border: 1px solid #ddd; padding: 12px; background-color: #f5f5f5; text-align: left;") Typical Latency
	tbody
	tr
	td(style="border: 1px solid #ddd; padding: 12px;") Database
	td(style="border: 1px solid #ddd; padding: 12px;") ACID transactions
	td(style="border: 1px solid #ddd; padding: 12px;") Milliseconds
	tr
	td(style="border: 1px solid #ddd; padding: 12px;") Cache
	td(style="border: 1px solid #ddd; padding: 12px;") Access speed
	td(style="border: 1px solid #ddd; padding: 12px;") Microseconds
	tr
	td(style="border: 1px solid #ddd; padding: 12px;") Compute
	td(style="border: 1px solid #ddd; padding: 12px;") Throughput
	td(style="border: 1px solid #ddd; padding: 12px;") Minutes – hours


	p Applications needing #[strong microsecond insights] on #[strong millisecond transactions] have no good options at scale in traditional architectures.

	p During traffic spikes, traditional architectures either drop connections (data loss) or degrade performance (missed SLAs). High-velocity applications need intelligent flow control that guarantees stability under pressure while preserving data integrity.

	hr

	h3 Event Processing at Scale

	p #[strong Here's what traditional multi-system event processing costs:]

	pre
	code.
	// Traditional multi-system event processing
	long startTime = System.nanoTime();

	// 1. Cache lookup for session data
	String sessionData = redisClient.get("session:" + eventId); // ~500μs network
	if (sessionData == null) {
	sessionData = postgresDB.query("SELECT * FROM sessions WHERE id = ?", eventId); // ~2ms fallback
	redisClient.setex("session:" + eventId, 300, sessionData); // ~300μs cache update
	}

	// 2. Transaction processing
	postgresDB.executeTransaction(tx -> { // ~2-5ms transaction
	tx.execute("INSERT INTO events VALUES (?, ?, ?)", eventId, userId, eventData);
	tx.execute("UPDATE user_stats SET event_count = event_count + 1 WHERE user_id = ?", userId);
	});

	// 3. Custom processing with consistency coordination
	ProcessingResult result = customProcessor.process(eventData, sessionData); // ~1ms processing
	redisClient.setex("result:" + eventId, 600, result); // ~300μs result caching

	// 4. Synchronization across systems
	ensureConsistency(eventId, sessionData, result); // ~2-3ms coordination

	long totalTime = System.nanoTime() - startTime;
	// Total: 6-12ms per event (not including queuing delays)


	p #[strong Compound Effect at Scale:]

	table(style="border-collapse: collapse; margin: 20px 0; width: 100%;")
	thead
	tr
	th(style="border: 1px solid #ddd; padding: 12px; background-color: #f5f5f5; text-align: left;") Rate
	th(style="border: 1px solid #ddd; padding: 12px; background-color: #f5f5f5; text-align: left;") Required processing time / s
	tbody
	tr
	td(style="border: 1px solid #ddd; padding: 12px;") 1,000 events / s
	td(style="border: 1px solid #ddd; padding: 12px;") 6–12 s
	tr
	td(style="border: 1px solid #ddd; padding: 12px;") 5,000 events / s
	td(style="border: 1px solid #ddd; padding: 12px;") 30–60 s
	tr
	td(style="border: 1px solid #ddd; padding: 12px;") 10,000 events / s
	td(style="border: 1px solid #ddd; padding: 12px;") 60–120 s

	p #[strong The math doesn’t work:] parallelism helps, but coordination overhead grows exponentially with system count.

	hr

	h3 Real-World Breaking Points
	ul
	li #[strong Financial services]: Trading platforms hitting 10,000+ trades/second discover that compliance reporting delays impact trading decisions.
	li #[strong Gaming platforms]: Multiplayer backends processing user actions find that leaderboard updates lag behind gameplay events.
	li #[strong IoT analytics]: Manufacturing systems processing sensor data realize that anomaly detection arrives too late for preventive action.

	hr

	h3 The Apache Ignite Alternative

	h3 Eliminating Multi-System Overhead

	pre.mermaid.
	flowchart TB
	subgraph "Event Processing"
	Events[High-Volume Events<br/>10,000/sec]
	end

	subgraph "Apache Ignite Platform"
	subgraph "Collocated Processing"
	Memory[Memory-First Storage<br/>Optimized Access Times]
	Transactions[MVCC Transactions<br/>ACID Guarantees]
	Compute[Event Processing<br/>Where Data Lives]
	end
	end

	Events --> Memory
	Memory --> Transactions
	Transactions --> Compute

	Memory <-->\|Minimal Copying\| Transactions
	Transactions <-->\|Collocated\| Compute
	Compute <-->\|Direct Access\| Memory

	p #[strong Key difference:] events process #[strong where the data lives], eliminating inter-system latency.

	hr

	h3 Apache Ignite Performance Reality Check

	p #[strong Here's the same event processing with integrated architecture:]

	pre
	code.
	// Apache Ignite integrated event processing
	try (IgniteClient client = IgniteClient.builder().addresses("cluster:10800").build()) {
	// Single integrated transaction spanning cache, database, and compute
	client.transactions().runInTransaction(tx -> {
	// 1. Access session data (in memory, no network overhead)
	Session session = client.tables().table("sessions")
	.keyValueView().get(tx, Tuple.create().set("id", eventId));

	// 2. Process event with ACID guarantees (same memory space)
	client.sql().execute(tx, "INSERT INTO events VALUES (?, ?, ?)",
	eventId, userId, eventData);

	// 3. Execute processing collocated with data
	ProcessingResult result = client.compute().execute(
	JobTarget.colocated("events", Tuple.create().set("id", eventId)),
	EventProcessor.class, eventData);

	// 4. Update derived data (same transaction, guaranteed consistency)
	client.sql().execute(tx, "UPDATE user_stats SET event_count = event_count + 1 WHERE user_id = ?", userId);

	return result;
	});
	}
	// Result: microsecond-range event processing through integrated architecture

	p Processing 10,000 events/s is achievable with integrated architecture eliminating network overhead.

	hr

	h3 The Unified Data-Access Advantage

	p #[strong Here's what eliminates the need for separate systems:]

	pre
	code.
	// The SAME data, THREE access paradigms, ONE system
	Table customerTable = client.tables().table("customers");

	// 1. Key-value access for cache-like performance
	Customer customer = customerTable.keyValueView()
	.get(tx, Tuple.create().set("customer_id", customerId));

	// 2. SQL access for complex analytics
	ResultSet<SqlRow&gt analytics = client.sql().execute(tx,
	"SELECT segment, AVG(order_value) FROM customers WHERE region = ?", region);

	// 3. Record access for type-safe operations
	CustomerRecord record = customerTable.recordView()
	.get(tx, new CustomerRecord(customerId));

	// All three: same schema, same data, same transaction model

	p #[strong Eliminates:]
	ul
	li #[strong Cache API] for cache operations
	li Data movement during #[strong distributed joins] for analytical queries
	li #[strong Custom mapping logic] for type-safe access
	li #[strong Data synchronization] between cache and database
	li #[strong Schema drift risks] across different systems

	p #[strong Unified advantage:] One schema, one transaction model, multiple access paths.

	hr

	h3 Apache Ignite Architecture Preview

	p The ability to handle high-velocity events without multi-system overhead requires specific technical innovations:
	ul
	li #[strong Memory-first storage]: Event data lives in memory with optimized access times typically under 10 microseconds for cache-resident data
	li #[strong Collocated compute]: Processing happens where data already exists, eliminating movement
	li #[strong Integrated transactions]: ACID guarantees span cache, database, and compute operations
	li #[strong Minimal data copying]: Events process against live data through collocated processing and direct memory access

	p These innovations address the compound effects that make multi-system architectures unsuitable for high-velocity applications.

	hr

	h3 Business Impact of Architectural Evolution

	h3 Cost Efficiency
	ul
	li #[strong Reduced infrastructure]: one platform instead of several
	li #[strong Lower network costs]: eliminate inter-system bandwidth overhead
	li #[strong Simplified operations]: fewer platforms to monitor, backup, and scale

	h3 Performance Gains
	ul
	li #[strong Microsecond latency]: eliminates network overhead
	li #[strong Higher throughput]: more events on existing hardware
	li #[strong Predictable scaling]: consistent performance under load

	h3 Developer Experience
	ul
	li #[strong Single API]: one model for all data operations
	li #[strong Consistent behavior]: no synchronization anomalies
	li #[strong Faster delivery]: one integrated system to test and debug

	hr

	h3 The Architectural Evolution Decision

	p Every successful application reaches this point: the architecture that once fueled growth now constrains it.

	p #[strong The question isn't whether you'll hit multi-system scaling limits. It's how you'll evolve past them.]

	p #[strong Apache Ignite] consolidates transactions, caching, and compute into a single, memory-first platform designed for high-velocity workloads. Instead of managing the compound complexity of coordinating multiple systems at scale, you consolidate core operations into a platform designed for high-velocity applications.

	p Your winning architecture doesn't have to become your scaling limit. It can evolve into the foundation for your next phase of growth.

	hr
	br
	\|
	p #[em Return next Tuesday for Part 2, where we examine how Apache Ignite’s memory-first architecture enables optimized event processing while maintaining durability, forming the basis for true high-velocity performance.]