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Why it matters: This integration solves the persistent challenge of database connection limits in serverless environments. By combining Cloudflare's edge network with PlanetScale's scalable databases via Hyperdrive, engineers can build high-performance, globally distributed apps with minimal latency.
Why it matters: This article details how Netflix scaled a critical OLAP application to handle trillions of rows and complex queries. It showcases practical strategies using approximate distinct counts (HLL) and in-memory precomputed aggregates (Hollow) to achieve high performance and data accuracy.
Why it matters: PlanetScale is bringing its expertise in scaling and managing databases to the Postgres ecosystem. This offers engineers a highly reliable, managed Postgres service with a roadmap for advanced sharding, simplifying the path to scaling complex relational workloads.
Why it matters: This article showcases a successful approach to managing a large, evolving data graph in a service-oriented architecture. It provides insights into how a data-oriented service mesh can simplify developer experience, improve modularity, and scale efficiently.
Why it matters: This article introduces a novel approach to managing complex microservice architectures. By shifting to a data-oriented service mesh with a central GraphQL schema, engineers can significantly improve modularity, simplify dependency management, and enhance data agility in large-scale SOAs.
Why it matters: Postgres's logical replication design creates a tight coupling between CDC consumers and HA failover. Unlike MySQL's GTID approach, Postgres requires active subscriber participation to make replicas failover-ready, potentially stalling maintenance or breaking data pipelines during outages.
Why it matters: This article details Pinterest's approach to building a scalable data processing platform on EKS, covering deployment and critical logging infrastructure. It offers insights into managing large-scale data systems and ensuring observability in cloud-native environments.
Why it matters: This article details how Netflix is innovating data engineering to tackle the unique challenges of media data for advanced ML. It offers insights into building specialized data platforms and roles for multi-modal content, crucial for any company dealing with large-scale unstructured media.
Why it matters: This article highlights how robust ML observability is critical for maintaining reliable, high-performing ML systems in production, especially for sensitive areas like payment processing. It provides a practical framework for implementing effective monitoring and explainability.
Why it matters: Neki brings proven sharding expertise from the Vitess team to the Postgres ecosystem, enabling massive horizontal scaling for Postgres users. This provides a path for high-growth applications to scale without abandoning the Postgres feature set or switching to proprietary solutions.
