DataHub: Popular metadata architectures explained
Authored byShirshanka Das Co-founder and CTO @ Acryl | Founder DataHub project | Ex-LinkedIn December 7, 2020 When I started my journey at LinkedIn ten years ago, the company was just beginning to experience extreme growth in the volume, variety, and velocity of our data. Over the next few years, my colleagues and I in LinkedIn’s data infrastructure team built out foundational technology like Espr
Kafka Ecosystem at LinkedIn
Apache Kafka is a highly scalable messaging system that plays a critical role as LinkedIn’s central data pipeline. Kafka was developed at LinkedIn back in 2010, and it currently handles more than 1.4 trillion messages per day across over 1400 brokers. Kafka’s strong durability and low latency have enabled us to use Kafka to power a number of newer mission-critical use cases at LinkedIn. These incl
The Log: What every software engineer should know about real-time data's unifying abstraction | LinkedIn Engineering
The Log: What every software engineer should know about real-time data's unifying abstraction I joined LinkedIn about six years ago at a particularly interesting time. We were just beginning to run up against the limits of our monolithic, centralized database and needed to start the transition to a portfolio of specialized distributed systems. This has been an interesting experience: we built, dep
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https://www.linkedin.com/pulse/microservices-architecture-retail-rajesh-gundapaneni
Data Infrastructure at LinkedIn
