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How Real-Time Stream Processing Makes Machine Learning More Powerful

In the data-driven world of 2025, machine learning (ML) powers everything from business insights to customer experiences. However, the effectiveness of ML depends on having up-to-date data—a challenge solved by real-time stream processing. Platforms like Tapdata play a key role in this by delivering real-time data to the data sources ML models depend on, ensuring predictions are not only accurate but also relevant when needed most. This blog explores how real-time stream processing improves machine learning by keeping data fresh and accessible. Tapdata makes this possible by syncing data to the data sources ML models use. From fraud detection to predictive maintenance, we’ll look at why this connection matters and how Tapdata helps bridge the gap between data generation and ML-powered results. The Evolution of Data in Machine Learning Machine learning used to rely on batch processing: data was collected over time, processed in batches, and used to train models based on past patterns. This worked for static analysis, but with the data landscape of 2025 exceeding 180 zettabytes—much of it coming from IoT, transactions, and online platforms—batch methods are no longer enough. Real-time stream processing changes everything, and Tapdata ensures this live data flows into the sources ML models...

Feb 26,2025

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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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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 Fresh is Your Data? Rethinking Change Data Capture for Real-Time Systems

Introduction The Hadoop ecosystem, born in 2006, fueled the big data boom for more than a decade. But times have changed—so have the scenarios and the technologies. The industry’s understanding of data has moved beyond T+1 batch processing and high-throughput, high-latency systems. In today’s real-world applications, real-time, accurate, and dynamic data is more important than ever. To meet these emerging needs, new frameworks and middleware have proliferated like mushrooms after rain. Hive brought SQL-like accessibility to the otherwise rigid Hadoop ecosystem. HBase and Impala tried to make it faster. Spark and Flink emerged as real-time processing frameworks, enabling data to flow closer to business in real time. Presto and Dremio virtualized real-time access to multiple sources. New OLAP databases like ClickHouse began providing near real-time analysis for massive datasets. Specialized solutions also popped up in areas like time-series and feature data processing. Unlike traditional commercial software, the real-time data ecosystem has embraced open source. In this world, talk is cheap—show me the code. At TapData, our own journey implementing real-time solutions made us feel that existing tools often fell short in subtle but critical ways. After delivering many real-world projects and speaking with countless customers, we gradually formed the...

Aug 20,2025

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From Batch to Instant: The 2025 Shift to Real-Time Data Replication

In the not-so-distant past, batch processing was the backbone of data management—a reliable, if slow, workhorse that powered everything from payroll systems to inventory updates. Data was collected, processed, and stored in scheduled chunks, often overnight or during off-peak hours. But as we step deeper into 2025, the world has changed. Businesses now operate in a 24/7 digital economy where decisions must be made in the blink of an eye, and customers expect instant responses. This seismic shift has propelled real-time data replication to the forefront, transforming how organizations manage, synchronize, and leverage their data. At Tapdata, we’re witnessing this evolution firsthand—and helping companies navigate it. The move from batch to instant isn’t just a trend; it’s a necessity for survival in today’s hypercompetitive landscape. In this blog, we’ll explore why real-time data replication is defining 2025, the challenges it addresses, and how Tapdata’s cutting-edge platform is empowering businesses to make the leap with confidence. The Decline of Batch Processing Batch processing served its purpose in an era when data volumes were manageable, and latency wasn’t a dealbreaker. Retailers could update stock levels overnight, banks could reconcile transactions at day’s end, and manufacturers could analyze production data in weekly reports....

Feb 25,2025

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Build Real-Time Materialized Views with CDC in Just 10 Lines of Code

What is a Real-Time Updating Materialized View? A materialized view is a data structure in database management systems that stores the results of a query as a physical table. This eliminates the need to re-run the query each time the view is accessed, improving query performance. Materialized views are especially useful for scenarios that involve frequent aggregation or complex joins, making them an effective data architecture pattern for improving performance and reducing resource usage. Based on the update strategy, materialized views can be categorized into two types: full updates and real-time (incremental) updates. Full Updates The full update strategy clears all existing data in the materialized view during each update and reinserts the latest query result set. This process can be understood as a combination of TRUNCATE TABLE and INSERT INTO SELECT operations. While full updates are straightforward, they may become inefficient and resource-intensive in scenarios involving large data volumes or high-frequency updates. Real-Time (Incremental) Updates The incremental update strategy is more efficient, as it calculates only the differences in the data that have changed since the last update and applies these changes to the materialized view. Incremental updates consume fewer resources while providing a more real-time data experience....

Dec 18,2024

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How Change Data Capture Powers Real-Time Data Pipelines

Change Data Capture (CDC) transforms how you handle data by capturing changes as they happen. This method ensures you access the most current information without delay. By eliminating the need for bulk data loads, CDC reduces processing time and operational overhead. You gain accurate and reliable data, enhancing decision-making and operational efficiency. With CDC, you maintain data consistency across systems, fueling real-time analytics and improving data integration. This approach empowers your data team, providing valuable insights and supporting continuous synchronization of streaming data. Understanding Change Data Capture (CDC) Definition of CDC Change Data Capture, often abbreviated as CDC, is a method that identifies and captures changes made to data in a source system. This technique allows you to track every modification, addition, or deletion in real-time. By doing so, CDC ensures that you always have the most current data at your fingertips. Unlike traditional methods that require full data loads, CDC focuses only on the changes. This approach reduces the time and resources needed for data processing. You can think of CDC as a real-time update mechanism that keeps your data fresh and relevant. Importance of CDC in Data Management CDC plays a crucial role in modern data management. It...

Dec 03,2024

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Implementing CDC for Real-Time Data Replication

Change data capture (CDC) is pivotal in modern data workflows by facilitating real-time data integration. CDC acts as a method that identifies and tracks changes in your database, enabling seamless data replication across platforms. This process ensures that your data remains consistent and up-to-date, which is essential for businesses aiming to make data-driven decisions. By implementing CDC, you can achieve near-zero downtime during migrations to the cloud, enhancing both flexibility and efficiency in your data management strategies. Understanding Change Data Capture (CDC) What is Change Data Capture (CDC)? Change Data Capture, or CDC, is a process that identifies and tracks changes in your database. It allows you to capture these changes in real-time, enabling seamless data replication across different platforms. By using CDC, you can ensure that your data remains consistent and up-to-date. This process is crucial for businesses that rely on accurate and timely data to make informed decisions. CDC works by monitoring changes in your database and then capturing these changes as they occur. You can think of it as a method that transforms changes into events. These events can then be published to an event stream for further processing and analysis. This approach minimizes the impact on...

Nov 18,2024