Apache Kafka
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...

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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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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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Druid Kafka Supervisor
13Oct

Understanding Apache Druid Supervisor and its specification for real-time data ingestion from Apache Kafka

Although both Apache Druid and Apache Kafka are potent open-source data processing tools, they have diverse uses. While Druid is a high-performance, column-store, real-time analytical database, Kafka is a distributed platform for event streaming. However, they can work together in a typical data pipeline scenario where Kafka is used as a messaging system to ingest and store data/events, and Druid is used to perform real-time analytics on that data. In short, the indexing is the process of loading data in Druid...

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Causes and remedies of poison pill in Apache Kafka
25Sep

Causes and remedies of poison pill in Apache Kafka

A poison pill is a message deliberately sent to a Kafka topic, designed to consistently fail when consumed, regardless of the number of consumption attempts. Poison Pill scenarios are frequently underestimated and can arise if not properly accounted for. Neglecting to address them can result in severe disruptions to the seamless operation of an event-driven system. The poison pill for various reasons: The failure of deserialization of the consumed bytes from the Kafka topic on the consumer side. Incompatible serializer and deserializer...

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How Kafka Works?
02Aug

How Kafka Works?

#realtimedata streaming with #kafka is a popular and powerful approach to handle large volumes of data and facilitate communication between different systems and applications. #apachekafka is an open-source distributed event streaming platform that allows you to publish, subscribe, store, and process streams of records. Here's a general overview of how real-time data streaming with Kafka works: Topic and Message Model: Data is organized into topics, which are essentially log-like data streams. Each message within a topic consists of a key, value, and timestamp. Publishers...

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Forging Apache Druid with Apache Kafka for real-time streaming analytics
16Jun

Forging Apache Druid with Apache Kafka for real-time streaming analytics

A real-time analytics database called Apache Druid is developed for quick slice-and-dice analysis on massive data volumes. The best data for Apache Druid is event-oriented and frequently utilized as the database backend for analytical application GUIs and for highly concurrent APIs that require quick aggregations. Druid can be leveraged very effectively where real-time ingestion, fast query performance, and high uptime are crucial. At the other end, Apache Kafka is gaining outstanding momentum as a distributed event streaming platform with excellent...

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01Jun

Knowing and valuing Apache Kafka’s ISR (In-Sync Replicas)

To get more clarity about ISR in Apache Kafka, We should first carefully examine the replication process in the Kafka broker. In short, replication means having multiple copies of our data spread across multiple brokers. Maintaining the same copies of data in different brokers makes possible the high availability in case one or more brokers go down or are untraceable in a multi-node Kafka cluster to server the requests. Because of this reason, it is mandatory to mention how...

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