Blog
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...

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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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11Apr

Transferring real-time data stream processed by Apache Flink to Kafka to Druid for analysis

Businesses can react quickly and effectively to user behavior patterns by using real-time analytics. This allows them to take advantage of opportunities that might otherwise pass them by and prevent problems from getting worse. Apache Kafka, a popular event streaming platform, can be used for real-time ingestion of data/events generated from various sources across multiple verticals such as IoT, financial transactions, inventory, etc. This data can then be streamed into multiple downstream applications or engines for further processing and eventual...

By Apache Druid, Apache Kafka, Big Data, Data Ingestion, Data Science, , , , , , , , , , , Comments Off
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Kafka with Flink
13Feb

Why Apache Kafka and Apache Flink work incredibly well together to boost real-time data analytics

When data is analyzed and processed in real-time, it can yield insights and actionable information either instantly or with very little delay from the time the data is collected. The capacity to collect, handle, and retain user-generated data in real-time is crucial for many applications in today’s data-driven environment. There are various ways to emphasize the significance of real-time data analytics like timely decision-making,  IoT and sensor data processing, enhanced customer experience, proactive problem resolution, fraud detection and security,...

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ConsumerLag
23Jan

Integrating rate-limiting and backpressure strategies synergistically to handle and alleviate consumer lag in Apache Kafka

Apache Kafka stands as a robust distributed streaming platform. However, like any system, it is imperative to proficiently oversee and control latency for optimal performance. Kafka Consumer Lag refers to the variance between the most recent message within a Kafka topic and the message that has been processed by a consumer. This lag may arise when the consumer struggles to match the pace at which new messages are generated and appended to the topic. Consumer lag in Kafka may...

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Using Kafka to manage Large Messages
19Dec

Architecture to leverage Apache Kafka for sharing large messages (GB size)

In today's data-driven world, the capability to transport and circulate large amounts of data, especially video files, in real-time is crucial for news media companies. For example, an incident occurred in a specific location, and a news reporter promptly filmed the entire situation. Subsequently, the complete video was distributed for broadcasting across their multiple studios situated in geographically distant locations. To construct or create a comprehensive solution for the given problem statement, we can utilize Apache Kafka in conjunction with...

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Zero Copy Principle
20Nov

The Zero Copy Principle With Apache Kafka

The Apache Kafka, a distributed event streaming technology, can process trillions of events each day and eventually demonstrate its tremendous throughput and low latency. That’s building trust and over 80% of Fortune 100 businesses use and rely on Kafka. To develop high-performance data pipelines, streaming analytics, data integration, etc., thousands of companies presently use Kafka around the globe. By leveraging the zero-copy principle, Kafka improves efficiency in terms of data transfer. In short, when doing computer processes, the zero-copy...

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