# scalable-architecture Published articles for scalable-architecture. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## "Like night and day": How Auditzy made queries 33x faster by switching from Postgres to ClickHouse DevFeed: ["Like night and day": How Auditzy made queries 33x faster by switching from Postgres to ClickHouse]() Original publisher: [Read original article]() Author: Mayank Joshi, Co-Founder and CTO, Auditzy Published: 2025-10-09T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [data-architecture](), [Latency](), [Back end](), [data](), [real-time](), [datasets](), [Amazon Web Services](), [Azure](), [browser](), [Operating system]() Tags: [architecture](), [aws](), [azure](), [backend](), [browser](), [clickhouse](), [compression](), [data](), [data-architecture](), [datasets](), [geolocation](), [golang](), [india](), [latency](), [metrics](), [performance](), [postgres](), [real-time](), [scalable-architecture](), [switching]() ### AI overview Auditzy migrated from a Postgres-based architecture to ClickHouse after query latency and ingestion problems emerged as data volumes grew. The migration delivered queries that were 33x faster and 10x better compression, supporting scalable, real-time website performance analytics. ### Source excerpt When Mumbai-based startup Auditzy hit Postgres performance limits, they switched to ClickHouse--and saw queries run 33x faster with 10x better compression. ## Building a Real-Time AI Fraud Detection System with Spring Kafka and MongoDB DevFeed: [Building a Real-Time AI Fraud Detection System with Spring Kafka and MongoDB]() Original publisher: [Read original article]() Author: Tim Kelly Published: 2025-04-21T15:05:48Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [MongoDB](), [Tutorial](), [Kafka](), [Embeddings](), [real-time](), [Development]() Tags: [ai](), [anomaly-detection](), [artificial-intelligence](), [building](), [embeddings](), [financial](), [fraud-detection](), [java](), [kafka](), [mongodb](), [pipeline](), [real-time](), [scalable-architecture](), [synthetic](), [tutorial](), [vector-search]() ### AI overview This tutorial builds a real-time fraud detection pipeline with MongoDB Atlas Vector Search, Apache Kafka, AI-generated embeddings, and MongoDB Change Streams. It compares new financial transactions with a user's historical transactions and flags potential fraud when no similar transactions exist or similar transactions are already marked as fraudulent. ### Source excerpt In this tutorial, we'll build a real-time fraud detection system using MongoDB Atlas Vector Search, Apache Kafka, and AI-generated embeddings. We'll demonstrate how MongoDB Atlas Vector Search can be ... The post Building a Real-Time AI Fraud Detection System with Spring Kafka and MongoDB appeared first on DEV. ## Angular Architecture Guide To Building Maintainable Applications at Scale DevFeed: [Angular Architecture Guide To Building Maintainable Applications at Scale]() Original publisher: [Read original article]() Author: Juri Strumpflohner Published: 2025-03-18T00:00:00Z Content type: tutorial Language: en Sources: [Juri Strumpflohner]() Topics: [Angular](), [Architecture & Design](), [Domain-driven design (DDD)](), [Scalability](), [Tooling]() Tags: [angular](), [architecture](), [collaboration](), [design](), [development](), [domain](), [domain-driven-design](), [guide](), [how-to](), [key-angular](), [key-architecture](), [maintainability](), [modular](), [nx](), [scalable-architecture](), [scale](), [software-architecture](), [structure](), [tooling]() ### AI overview A guide to architecting maintainable Angular applications at scale. It explains modular design, clear boundaries, dependency decoupling, layered and vertical architectures, and organizing code around business domains. ### Source excerpt Learn how to build scalable Angular applications using domain-driven design, clear boundaries, and Nx tooling for better maintainability and team collaboration. ## Scaling Scientific Data: Migrating Benchling's Schema Model for Performance at Scale DevFeed: [Scaling Scientific Data: Migrating Benchling's Schema Model for Performance at Scale]() Original publisher: [Read original article]() Author: Melody Ding Published: 2024-12-04T15:01:06Z Content type: article Language: en Sources: [Benchling]() Topics: [data](), [Structured-data](), [data-processing](), [Database](), [PostgreSQL]() Tags: [applications](), [automated](), [benchling](), [collection](), [core](), [customer](), [customers](), [data](), [data-modeling](), [database-optimization](), [model](), [performance](), [postresql](), [product](), [scalable-architecture](), [scale](), [schema](), [science](), [software-engineering](), [speed](), [storage](), [structure](), [tests]() ### AI overview Benchling describes migrating its Schema Model to a more compact structure to improve data-ingestion performance as scientific data volumes grow. The phased transition aimed to balance speed and flexibility while avoiding PostgreSQL scaling limitations, including the need for sharding. ### Source excerpt Benchling is a unified platform for scientific data. It allows scientists to collaborate on complex science, automate work, and power AI. Customers store large volumes of data on our platform, leveraging it across many applications both within Benchling and in their own infrastructure. It's critical that customer data is accessible in a performant and scalable way. In this article, we'll explore a recent shift in how we store and retrieve customer data. By migrating to a more compact structure, we've tackled key performance challenges associated with increased data volumes. This transition has required a careful balance between speed and flexibility, as well as a phased approach that minimized disruption for users. Benchling Schemas At the core of Benchling's system is Schemas, a product that allows both Benchling internal teams and customers to configure the various shapes of data, defining fields, attributes, and constraints that entities must follow. These data structures represent entities like equipment, storage, biological molecules, workflows, tasks, lab notes, and recorded results from scientific tests. Schemas reside in what we refer to as the definition layer. An example schema for defining the data structure of a molecule Each instance of a schema, referred to as a schematizable item, represents the actual data input by scientists. We call this the instance layer. These items are populated with field values conforming to the schema's defined fields. As Benchling's user base grows and the amount of schematized data ingested into the platform increases every year, optimizing the storage of field values has become crucial. Relationship between actual instances of a molecule and its defined schemaThe Challenge: Scale and Performance Historically, Benchling saw a shift from manual data upload by scientists to integrations with lab equipment, leading to automated data collection. This significantly increased the speed and volume of data ingestion. Assay results ## A Guide to Running an Engineering Program DevFeed: [A Guide to Running an Engineering Program]() Original publisher: [Read original article]() Author: Carla Wright Published: 2021-07-21T20:00:01Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Development](), [Shopify](), [Tooling](), [coding]() Tags: [architecture](), [complexity](), [engineering](), [guide](), [organizational](), [scalable-architecture](), [scope](), [team](), [teams](), [technical](), [tooling]() ### AI overview Shopify shares a playbook for running large engineering programs across a complex, rapidly evolving platform. The guide emphasizes a clearly defined outcome, stakeholder alignment, documented planning, and structured leadership through program stakeholders and program leads. ### Source excerpt Shopify has a playbook for huge engineering programs in the past that cross over multiple areas of complexity on the platform, and we'd like to share it with you. ## Designing a Workflow engine from first principles DevFeed: [Designing a Workflow engine from first principles]() Original publisher: [Read original article]() Author: Shawn Wang Published: 2021-04-05T07:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Architecture & Design](), [Scalability](), [Messaging](), [sharding](), [Routing (disambiguation)](), [consistency](), [Availability](), [Programming](), [Open Source]() Tags: [architecture](), [consistency](), [hashicorp](), [messaging](), [microservices](), [open-source](), [queues](), [routing](), [scalability](), [scalable-architecture](), [temporal](), [temporal-concepts](), [workflow](), [workflow-engine]() ### AI overview This article explains workflow engine design principles from first principles, including task queues, timers, consistency, scalability, sharding, routing, system workflows, and external implementations. It describes how Temporal addresses these requirements and outlines Temporal's history through related systems and projects such as SWF, Durable Task Framework, Cherami, and Cadence. ### Source excerpt Learn key workflow engine design principles and how Temporal's scalable architecture solves challenges like task queues and scalability.