How LinkedIn Uses Oracle

The article discusses LinkedIn's journey in evolving its professional community policies enforcement at scale, focusing on the development of its anti-abuse platform and account restriction systems.

The article discusses LinkedIn's strategy to upscale its profile datastore while reducing operational costs.

The article details LinkedIn's migration journey from Java 8 to Java 11, emphasizing the performance improvements and challenges faced during the transition.

The article discusses Bef's journey at LinkedIn, highlighting his transition from a backend engineer to an engineering director while emphasizing the importance of mentorship and building an inclus...

The article discusses Opal, a system developed at LinkedIn to manage mutable datasets within a data lake.

The article discusses Hodor, a framework developed by LinkedIn to detect and address service overload in their microservices architecture.

This article discusses the evolution of LinkedIn's Daily Executive Dashboard (DED) from a simple dashboard to a robust enterprise-grade data pipeline.

The article introduces FastIngest, a new evolution of Apache Gobblin designed to enable low-latency data ingestion from Kafka to HDFS using the ORC file format and Apache Iceberg for metadata manag...

The article discusses the evolution of metadata architectures, focusing on three generations of data discovery tools.

The article discusses LIquid, a new graph database, focusing on its design and implementation.

The article discusses the redesign of LinkedIn's messaging system, detailing the challenges faced with the original architecture and the requirements for a new system.

The article discusses LinkedIn's open sourcing of DataHub, a metadata search and discovery platform, detailing its development journey from WhereHows to DataHub.

This article provides an in-depth look at LinkedIn's data pipeline monitoring system, focusing on the challenges faced with traditional monitoring methods and how they have evolved to improve visib...

The article discusses the importance of fairness, privacy, and transparency in AI/ML systems, emphasizing their role in building trust and user engagement.

The article discusses the open-sourcing of Brooklin, a distributed service for near real-time data streaming at scale, which has been in production at LinkedIn since 2016.

The article discusses the importance of data management at LinkedIn, focusing on expediting data fixes and migrations through a centralized, scalable self-service platform.

The article discusses the development of a centralized and scalable settings platform at LinkedIn aimed at enhancing member trust through improved data privacy and user control.

The article discusses LinkedIn's approach to privacy-preserving analytics and reporting through the PriPeARL framework.

The article discusses a community meetup held at LinkedIn focused on Apache Hadoop, highlighting contributions from various organizations and key presentations on topics like TensorFlow on YARN, Ha...

The article discusses the comprehensive overhaul of the LinkedIn Groups experience, focusing on integrating existing LinkedIn infrastructure to enhance functionality and user experience.

The article discusses the re-architecture of LinkedIn's contacts and calendar ecosystem, focusing on the migration to a single source of truth for contact data.

The article discusses the statistical modeling system that powers LinkedIn Salary, focusing on how it collects and processes compensation data while addressing privacy concerns.

The article discusses the evolution of incremental data capture for Oracle databases at LinkedIn, highlighting the transition from a batch processing model to a near-real-time system.

The article discusses Brooklin, a data ingestion service developed by LinkedIn to facilitate streaming data from various sources to multiple destinations.

The article discusses the migration of LinkedIn's internal service, Babylonia, from Oracle to Espresso, a distributed NoSQL database.

The article 'What Gets Measured Gets Fixed' discusses the importance of measurement in engineering, illustrating this principle through two case studies: a database migration failure and the establ...

This article discusses the challenges of data access in high-scale stream processing, particularly focusing on the read/write and read-only data access patterns.

The article introduces Ambry, LinkedIn's newly open-sourced distributed object store optimized for media storage and serving.

The article introduces LinkedIn Platform as a Service (LPS), a new private cloud solution designed to streamline service deployment and enhance developer productivity.

The article discusses Gobblin, a unified data ingestion framework developed by LinkedIn, designed to bridge batch and streaming data ingestion.

The article discusses the graduation of Apache Samza from the Apache Incubator to a top-level Apache project, highlighting its significance in stream processing and the community growth during its ...

Espresso is LinkedIn's distributed, fault-tolerant NoSQL database that supports various applications, including Member Profile and InMail.

The article discusses LinkedIn's efforts to simplify big data ingestion for Hadoop-based warehouses using a framework called Gobblin.

Apache Helix is a framework designed for developing distributed systems, addressing challenges such as scalability, fault tolerance, and partition management.

The article discusses the development and implementation of Pinot, a distributed real-time analytics engine created at LinkedIn to handle massive data scales and provide real-time insights.

The article discusses the optimization of garbage collection (GC) for high-throughput and low-latency Java applications, particularly in the context of LinkedIn's feed data platform.

The article announces the release of Voldemort 1. 6. 0, a distributed key-value storage system developed at LinkedIn.

The article discusses the significance of the log as a fundamental abstraction in real-time data systems, emphasizing its role in distributed systems, data integration, and stream processing.

LinkedIn is hosting the first San Francisco Bay Area MicroStrategy Meetup on December 4, 2013, providing an opportunity for the user community to share insights and learn from each other.

The article announces the release of Voldemort 1. 3. 0, detailing significant performance improvements, new features, and enhanced operability.

The article discusses the open-source release of Databus, LinkedIn's low latency change data capture system, which has been in production since 2011.

The article discusses the challenges and solutions in ranking live streams of data on LinkedIn, particularly focusing on group discussions.

This article serves as an introduction to MongoDB, highlighting its growing popularity as a NoSQL database for web applications.