Batch Is Broken: Time to Think Incremental-Tapdata

Batch Is Broken: Time to Think Incremental

Sep 03,2025

In today’s digital landscape, businesses aren’t just hoarding data—they’re obsessed with turning it into actionable insights, fast. The real edge comes from spotting changes in real time and reacting instantly, whether it’s tweaking recommendations or averting a crisis.

A decade ago, tech advances in hardware and platforms let us tackle massive datasets with ease. We built data warehouses, ran batch jobs, and cranked out reports, pulling value from historical data in hours or days.

But here’s the catch: data doesn’t wait for your schedule anymore—it’s evolving every second.

Why Batch Processing Is Falling Short

As businesses go digital, data changes faster than our systems can keep up. According to IDC’s Data Age 2025 report, global data will hit 181 zettabytes by 2025, with over 30% generated in real time—and 95% of that from IoT devices, endpoints, and online interactions.

That means data isn’t piling up for batch runs; it’s shifting constantly during operations. Miss the timing, and you’re not just slow—you’re risking real business hits:

Financial Transactions Traditional fraud detection often lags 15–20 minutes in batch mode, but scams can strike in seconds. Per the IJCET industry report, high-value fraud from delays averages about $12,000 per account. The European Payments Council (EPC) in its 2024 report stresses that instant transfers like SCT Inst demand real-time fraud monitoring, not batch windows.
Online Services and Recommendation Systems Platforms thrive on instant feedback. Take Netflix: their public data shows personalized recommendations drive about 80% of viewing hours, so any delay in responding to user behavior tanks engagement and retention.
Ecommerce and Retail Inventory and pricing need constant syncing. The IHL Group report pegs global retail losses from inventory mismatches (like stockouts or overstock) at 1.77 trillion annually, with out-of-stocks alone costing 1.2 trillion. Overselling or slow restocking leads to cancellations, refunds, complaints, and damaged trust.
Manufacturing and Industrial IoT Siemens’ downtime cost report estimates large auto plants lose $2.3 million per hour of downtime. Relying on batch or periodic sensor analysis? A few minutes’ delay can snowball into massive losses. Real-time IoT capture and analysis spots anomalies in seconds, slashing unplanned shutdowns.

From lost recommendations to billion-dollar inventory snafus and factory halts costing millions, the issue boils down to one thing—batch processing is too slow. To keep pace with real-time shifts, we need a smarter approach: incremental computation.

Incremental Computation: Focus on What Changes

Traditional data processing scans everything every time, recalculating from scratch. Incremental computation flips that: it only touches what’s changed.

Picture running a big logistics firm with millions of packages zipping across the country. Your system tracks status, location, and ETA for monitoring and customer queries. The old way? Scan the whole database hourly to recalculate progress and alerts—wasting resources and lagging behind real events.

With incremental computation, you zero in on updated packages. If just 2% changed since last check, that’s all you process—dropping delays from hours to milliseconds and cutting resource use by over 90%.

The beauty? It thrives as data grows and shifts faster, delivering fresh results with minimal overhead. Core perks include:

Boosted Performance Full scans scale poorly with data size; incremental handles change volume (Δ), perfect for high-update spots like ecommerce, finance, or IoT.
Cost Savings Skip redundant work. For a 1TB dataset with 1% daily changes, you’re only crunching 10GB—slashing compute and storage bills.
Real-Time Reliability Async updates and streaming keep things current at sub-second speeds, fitting microservices, edge setups, and cloud-native environments.

In short, the bigger and busier your data, the more incremental shines. It’s not just optimization—it’s a scalable way to power real-time business.

But pulling it off takes more than theory; it demands solid data capture and processing.

Prerequisites for Incremental Computation

Incremental computation sounds straightforward, but getting it right means nailing two essentials: reliably spotting changes and swiftly handling them. Miss these, and you’ll hit delays or inconsistencies.

Solid Incremental Data Change Capture

The heart of incremental is pinpointing what’s new, usually via Change Data Capture (CDC) tech that grabs source system events (like INSERT, UPDATE, DELETE) in real time.

Why Crucial?

Shaky capture—dropped events or high lag—messes up results or corrupts data. Top-notch CDC needs:

Low latency and high throughput (handling tens of thousands of events per second);
Broad support for sources (MySQL, Oracle, MongoDB, Kafka);
Accurate parsing of complex types (JSON, nested structures).

Log-based CDC (like Debezium) is a go-to, monitoring changes invisibly for rock-solid streams.

Example: In a distributed ecommerce setup, CDC grabs order status shifts instantly, letting incremental aggregate just new orders—no full history rescan.

High-Performance Data Processing

Once captured, changes need quick handling—JOINs, custom calcs, filters—all without bottlenecking.

Why Crucial?

Slow processing backs up queues, spiking latency or crashing systems. The ideal engine enables consistent updates.

Core Tech

Relies on memory state management (like RocksDB for result persistence) and optimized frameworks (incremental-friendly engines). For multi-stream JOINs, update only affected records—no full-table sweeps.

Deployment Notes: Add fault tolerance (change replays) and monitoring (Prometheus) to handle network glitches or spikes. These turn incremental from idea to reliable ops, though they call for skilled teams and tools.

Why Skip Stored Procedures, Materialized Views, or Triggers?

For real-time needs, teams often turn to database builtins like stored procedures, materialized views, or triggers. They’re handy for small stuff, but scale poorly with concurrency, volume, or complexity—hitting performance walls, maintenance headaches, and security risks.

Scheme	Performance	Cost	Real-Time	Maintainability	Typical Use
Stored Procedures	Medium, lock-prone	Low	Seconds (scheduler-dependent)	Low, tight coupling	Simple table ops
Materialized Views	High (precomputed)	Medium (high refresh)	Minutes (mostly full refresh)	Medium	Static report queries
Triggers	Low (crashes under load)	Low	High (immediate)	Low, hard to manage complex logic	Small event reactions
Incremental Engine	High (change-only)	Low	Milliseconds	High, modular/integration-friendly	Large-scale real-time analysis

Why They Fall Short

Stored Procedures: Embedded in the DB, they lack scalability and real-time flex—tough for frequent changes. Peak loads spike source DB strain, making performance unpredictable.
Materialized Views: Precompute for speed, but refreshes are often full-bore, costly and slow for updates. Tied to source DB, they’re invasive and disrupt core ops.
Triggers: Fire per change for immediacy, but high traffic tanks the DB. Maintenance is a nightmare with complex JOINs, and source-binding creates load and security risks.

Incremental computation is built for real-time scalability, with “capture + process + update” decoupled from the source—boosting performance, controlling loads, and minimizing risks by avoiding direct DB access.

Redefining Data Processing: From Full to Incremental

In a world where data outpaces our tools, sticking to full recomputes means bottlenecks, skyrocketing costs, and missed opportunities.

Incremental computation flips the script: focus on changes alone, updating results with minimal effort for always-fresh insights. It’s more than efficiency—it’s the shift from post-hoc analysis to live response, making or breaking edges in finance, retail, manufacturing, and healthcare.

It’s not plug-and-play, though; it needs robust change capture, swift processing, and solid isolation. That’s where picking the right tool matters.

As pioneers in this space, TapData offers an easy-to-deploy incremental engine with cross-source CDC, quick incremental materialized views, API-ready results, and workflow management—turning weeks of dev into minutes of setup for real-time views.

Facing real-time data hurdles or curious about incremental in action? Drop your questions or scenarios in the comments—we’d love to chat! Stay tuned for deep dives on architecture, cases, and rollouts.

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