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Stream Processing vs. Incremental Computing: Picking the Right Tool for Real-Time Data Challenges

In my previous piece, we explored why traditional batch processing falls short in today’s fast-paced world—such as detecting fraud in finance before it’s too late or maintaining real-time e-commerce stock levels. Batch jobs accumulate data and process it in batches, which can increase latency and consume excessive resources. Enter incremental computing: it zeroes in on just the deltas, dropping delays to mere milliseconds while slashing resource demands by 90% or more. That said, stream processing often pops up as the go-to for real-time workflows. It excels at log processing and triggering real-time alerts. However, when examining your actual business requirements closely, you’ll discover its limitations—especially when you need rock-solid accuracy, seamless consistency, or intricate analytics. Sometimes, it ends up complicating things more than streamlining them. In this post, we’ll pit incremental computing against stream processing head-to-head. We’ll tackle the burning question: Why skip stream processing for real-time data altogether? From core concepts and pitfalls to direct comparisons and hands-on examples, this should arm you with fresh insights to nail the right tech stack for your setup. Stream Processing: The Basics and Its Current Landscape At its heart, stream processing treats data as an endless river—processing chunks as they flow in,...

Oct 27,2025

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What Is Data as a Service (DaaS)?

Data as a Service (DaaS) is a data management and delivery model that enables organizations to access and use data on demand, without managing the infrastructure behind it. Similar to how SaaS revolutionized software delivery, DaaS decouples data access from backend storage and processing, offering scalable, flexible, and real-time data access via APIs or service endpoints. Why Enterprises Are Turning to DaaS The need for real-time analytics, decentralized operations, and API-first architectures has made DaaS platforms essential to modern enterprise IT strategies. Compared to traditional ETL pipelines or batch-oriented data warehouses, DaaS ensures: On-demand, real-time data access Lower latency and faster insights Greater agility through low-code integration Better governance and centralized control Key Components of a DaaS Architecture A modern Data as a Service platform typically includes: CDC-based Data Ingestion: Continuous sync from operational databases Real-Time Data Modeling: Transformations, joins, and filtering API Services Layer: RESTful endpoints for internal/external consumption Governance and Security: Authentication, access controls, logging >>> TapData supports all of the above with built-in CDC pipelines, no-code modeling, and live API publishing. DaaS vs ETL vs iPaaS: What’s the Difference? Feature DaaS ETL iPaaS Data Freshness Real-time or near real-time Batch-oriented Varies by vendor Delivery Format APIs or...

Oct 27,2025

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Why Operational Data Hubs Are the Missing Layer in Your Data Strategy

The Blind Spot in Modern Data Strategy Most enterprises today have invested heavily in data warehouses, data lakes, or even lakehouses. These platforms are excellent for historical analysis, batch ETL pipelines, and centralized data governance. But when it comes to real-time decision-making, low-latency operations, and unified data access across teams, these systems often fall short. What’s missing is a dedicated operational layer — one that synchronizes data from core systems in real time and serves it directly to operational applications. That layer is called an Operational Data Hub (ODH). What Is an Operational Data Hub? An operational data hub is a centralized, real-time integration layer that connects transactional systems, streams change data, and provides fresh, query-ready views to downstream consumers — such as APIs, dashboards, customer apps, and internal tools. Unlike warehouses that are optimized for batch analytics, the data hub focuses on live operational access and sub-second real-time data integration. Key characteristics of an operational data hub include: Log-based change data capture (CDC) from source systems Schema-aware transformation and mapping Support for real-time materialized views Low-latency delivery to targets like MongoDB, ClickHouse, and API endpoints Why It Complements — Not Replaces — Your Data Warehouse This is not a...

Oct 27,2025

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From Silo-ed Systems to Real-Time DaaS — How Chow Sang Sang Unified Data Across Four Regions and Six Brands

Imagine a shopper walking into a boutique in Hong Kong, while another browses the online store in Shanghai. Both are looking for the same limited-edition necklace. In the past, those two moments might have triggered two separate systems, with no guarantee the stock view was aligned. Today, thanks to a unified real-time data service, the answer is consistent everywhere: “Yes, it’s available — and reserved for you.” Background & Challenges Chow Sang Sang (CSS) is a heritage jewelry retailer with over a thousand stores and six brands across Mainland China, Hong Kong, Macau, and Taiwan. Decades of growth left the company with a tangle of business systems, and silo-ed data: more than a dozen disparate ERP, POS, and WMS systems running independently across different regions and brands. The result was fragmented product data, such as product information and product inventory. The operational systems are similar in nature, however each system had its own set of business logic customized for the local market, making it difficult to deliver a seamless omnichannel experience. For associates, it meant uncertainty when promising stocks. For ecommerce, it meant inconsistencies in product attributes. For IT, it meant endless one-off integrations and long lead times just to...

Sep 26,2025

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Operational Data Hub Implementation Pitfalls — and How to Avoid Them

Introduction: Why Operational Data Hubs Fail — and How to Do It Right The idea of an operational data hub is compelling: unify data from fragmented systems, stream it in real time, and serve it instantly to APIs, dashboards, and downstream services. But turning that vision into reality can be challenging. Many teams jump into implementation without fully understanding the architectural trade-offs, integration limitations, or system behaviors — and the result is an underperforming or fragile system. In this article, we’ll walk through common pitfalls in operational data hub implementation, and show how to avoid them by applying proven best practices — many of which we’ve seen in real-world TapData deployments. Pitfall 1: Confusing a Data Hub with a Data Warehouse One of the first mistakes teams make is treating an operational data hub like a mini data warehouse. They overload it with historical data, complex aggregations, and slow batch workflows — defeating its purpose. Why it’s a problem: ODHs are designed for live, low-latency sync, not long-term storage Treating it like a warehouse leads to stale data, performance issues, and misuse How to avoid it: Scope your data hub to operational data only: what changes frequently and needs to...

Sep 26,2025

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Why Financial Institutions Are Replacing Legacy Integration with Operational Data Hubs

The Financial Industry’s Real-Time Mandate In a sector where milliseconds can matter, data latency, inconsistency, and fragmentation are no longer tolerable. Banks and insurance companies operate dozens — if not hundreds — of core systems: core banking, CRM, payment gateways, risk control, regulatory reporting, customer service platforms, and more. Traditionally, these systems are integrated via point-to-point APIs, ESBs, or batch ETL jobs — methods that introduce delay, complexity, and governance risk. To stay competitive, more financial institutions are replacing these legacy patterns with a modern operational data hub (ODH) — a real-time data integration layer designed for speed, scale, and trust. Unlike traditional data pipelines, this architecture promotes a data hub model: unified, streaming, and event-driven by design. What Is an Operational Data Hub in Financial Services? An operational data hub is a centralized platform that: Captures real-time changes from core systems Transforms and routes data across applications Provides live, queryable views for analytics, dashboards, and APIs Enables observability, lineage, and auditability for compliance In finance, this means: Real-time customer 360 for relationship managers Instant synchronization between trading, risk, and settlement systems Live reporting to meet daily liquidity and credit exposure requirements Supporting regulators with timely, traceable data pipelines Common...

Sep 26,2025

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Batch Is Broken: Time to Think Incremental

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...

Sep 03,2025

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How Public Health Institutions Use Operational Data Hubs to Improve Real-Time Decision-Making

Healthcare Needs Real-Time Data More Than Ever Public health institutions are under growing pressure to respond faster, manage more complex data environments, and serve increasingly digital citizen needs. Yet many still operate with fragmented systems — EMRs, lab systems, billing platforms, public health registries — all siloed, inconsistent, and updated via batch processes. To solve this, forward-thinking hospitals and agencies are turning to a new kind of architecture: the Operational Data Hub (ODH). An operational data hub serves as the real-time backbone of healthcare data integration. It collects, synchronizes, and serves up-to-date operational data from multiple systems to downstream applications, dashboards, and services — with sub-second latency and no manual reconciliation. What Is an Operational Data Hub in Public Health? In this context, an operational data hub enables: Real-time patient data unification (across EMR, LIS, and radiology systems) Synchronized hospital resource tracking (beds, ventilators, supplies) Live dashboards for outbreak monitoring or vaccine distribution Streamlined data delivery to national or municipal public health systems Unlike a traditional data warehouse, which focuses on historical data, an ODH supports live operational decisions: detecting anomalies, monitoring treatment pipelines, or updating patient alerts. Key Use Cases in Healthcare and Public Health Unified Patient View Integrate...

Sep 03,2025