Yearly Archives - 2025
04Jun

Driving Streaming Intelligence On-Premises: Real-Time ML with Apache Kafka and Flink

Lately, companies, in their efforts to engage in real-time decision-making by exploiting big data, have been inclined to find a suitable architecture for this data as quickly as possible. With many companies, including SaaS users, choosing to deploy their own infrastructures entirely on their own, the combination of Apache Flink and Kafka offers low-latency data pipelines that are built for complete reliability. Particularly due to the financial and technical constraints it brings, small and the medium size enterprises often have...

By Apache Kafka, Artificial Intelligence, Big Data, Data Engineering, Machine Learning0 Comments
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26May

Dark Data Demystified: The Role of Apache Iceberg

Lurking in the shadows of every organization is a silent giant—dark data. Undiscovered log files, unread emails, silent sensor readings, and decades-old documents collecting digital dust are all examples of the vast amount of data that companies unwittingly bury. Not only are these worthless artifacts, but they have the potential to be treasure troves that have been shut down because of antiquated systems, a lack of funding, or just plain negligence. Whether or not this data is structured, it...

By Big Data, Data Engineering, Storage Mechanism0 Comments
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24Feb

The Role of Materialized Views in Modern Data Stream Processing Architectures + RisingWave

Incremental computation in data streaming means updating results as fresh data comes in, without redoing all calculations from the beginning. This method is essential for handling ever-changing information, like real-time sensor readings social media streams, or stock market figures. In a traditional, non-entrepreneurial calculation model, we need to process the entire dataset every time we get a new piece of data. It can be incompetent and slow. In incremental calculations, only the part of the result affected by new...

By Big Data, Data Analysis, Data Engineering, Data Science, Processing Engine, Storage Mechanism, , , , Comments Off
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06Feb

Unlocking the Power of Patterns in Event Stream Processing (ESP): The Critical Role of Apache Flink’s FlinkCEP Library

We call this an event when a button is pressed, a sensor detects a temperature change or a transaction flows through. An event is an action or state change that is important to an application. Event stream processing (ESP) refers to a method or technique to stream the data in real-time as it passes through a system. The main objective of  ESP is to focus on the key goal of taking action on the data as it arrives. This enables real-time analytics...

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23Jan

Real-Time Redefined: Apache Flink and Apache Paimon Influence Data Streaming’s Future

    Apache Paimon is made to function well with constantly flowing data, which is typical of contemporary systems like financial markets, e-commerce sites, and Internet of Things devices. It is a data storage system made to effectively manage massive volumes of data, particularly for systems that deal to analyze data continuously such as streaming data or with changes over time like database updates or deletions. To put it briefly, Apache Paimon functions similarly to a sophisticated librarian for our data....

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