Sachin Kamath
Bio
Condense provides an enterprise-grade, fully managed Kafka service designed to simplify and enhance real-time data streaming. It automates deployment, scaling, and maintenance of Kafka clusters.
https://www.zeliot.in/our-products/condense
Stories (8)
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5 Reasons to Modernize Your Kafka Stack in 2025
Introduction Apache Kafka has remained the backbone of event-driven architectures for over a decade. Its immutable log abstraction, scalable broker design, and stream-first philosophy have powered countless real-time systems—from fraud detection and e-commerce analytics to telematics ingestion and industrial automation.
By Sachin Kamath7 months ago in Futurism
Choosing the Right Kafka Platform: Condense vs Confluent vs Redpanda
Introduction Apache Kafka has become the backbone of modern real-time systems — enabling everything from sensor telemetry and financial transactions to personalized customer experiences. But Kafka, by itself, is complex to deploy, scale, and operate at production-grade levels. That’s why companies increasingly turn to managed or enhanced Kafka platforms to accelerate their streaming initiatives.
By Sachin Kamath7 months ago in Futurism
Benefits of Using Kafka for Real-Time Streaming Events
Why Kafka Became the Backbone of Real-Time Data In today’s event-driven world, data no longer arrives in scheduled batches. It moves continuously — from app interactions, payment systems, vehicle telemetry, sensors, APIs, user sessions, and infrastructure events. Responding to this data in real-time is now a requirement across various industries, including mobility, finance, healthcare, manufacturing, and media.
By Sachin Kamath7 months ago in Futurism
The Hidden Business Costs of Managing Open-Source Kafka at Scale.
Introduction Apache Kafka is the backbone of modern real-time data architectures. It powers everything from user activity tracking to IoT telemetry, fraud detection, and microservices communication. As an open-source distributed log system, it promises high throughput, durability, and fault tolerance—making it an easy choice for engineering teams.
By Sachin Kamath7 months ago in Futurism
How can a Media streaming application handle millions of users?
When Viewers Flood In — What Breaks First Isn’t the Video When a high-stakes event, such as a major sports final or breaking news, occurs, media platforms can experience a sudden surge in users, jumping from thousands to millions in seconds. The video stream may be flawless, but the experience still crashes.
By Sachin Kamath8 months ago in Futurism
Streaming ETL with Condense: A Faster, Smarter Alternative to Batch Processing
Introduction From Batch ETL to Real-Time Streaming — and Why Kafka Changed Everything For decades, enterprises relied on batch-oriented ETL (Extract, Transform, Load) processes to move and prepare data for analysis. Batch ETL was designed in an era where data volumes were modest, real-time decision-making was rare, and overnight data refresh cycles were acceptable.
By Sachin Kamath8 months ago in Futurism
How Condense Optimizes Kafka Performance: Managing Data Streams
Introduction Modern enterprises increasingly operate in environments defined by continuous, high-volume event generation. Applications across industries — from financial services to connected vehicles, smart factories to media platforms — demand the ability to ingest, process, and respond to millions of streaming events per second, often with sub-second latencies.
By Sachin Kamath8 months ago in Futurism
