# Cassandra Published articles for Cassandra. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows DevFeed: [Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-11T14:17:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Orchestration](), [Amazon S3](), [Kafka](), [Apache Iceberg]() Tags: [amazon-s3](), [apache-iceberg](), [apache-kafka](), [architecture-design](), [asynchronous-architecture](), [cassandra](), [cloud-architecture](), [concurrency](), [development](), [devops](), [distributed-systems](), [elasticsearch](), [java-operator-sdk](), [kafka](), [latency](), [microservices](), [netflix](), [netflix-conductor-4-workflow](), [news](), [orchestration](), [s3](), [scalability](), [windows-workflow-foundation](), [workflow](), [workflow-bpm](), [workflow-foundation]() ### AI overview Netflix reworked Conductor 4.0 to scale workflow orchestration to roughly 200,000 definitions and 420 million monthly executions. The redesign raises supported workflow size to 30,000 tasks and reports a roughly 40% reduction in p99 evaluation latency by loading only task data needed for each decision. ### Source excerpt Netflix has reworked its Conductor workflow orchestration engine to handle larger workloads, increasing supported workflow size from about 2,500 to 30,000 tasks and reducing p99 workflow evaluation latency by about 40%. Conductor 4.0 separates workflow metadata from task data, moves evaluation to asynchronous processing, and introduces dynamic worker allocation and concurrency controls. By Leela Kumili ## Designing for failure: Building resilient systems on AWS DevFeed: [Designing for failure: Building resilient systems on AWS]() Original publisher: [Read original article]() Author: Dhvani Vora Published: 2026-08-13T20:03:28Z Content type: tutorial Language: en Sources: [AWS Compute Blog]() Topics: [incident](), [Incident response]() Tags: [amazon-ec2](), [aws](), [aws-architecture-blog-resilience](), [aws-management-tools-blog-aws-resilience-hub](), [best-practices](), [cassandra](), [distributed-systems](), [hardware](), [incident](), [intermediate-200](), [kafka](), [observability](), [resilience](), [systems]() ### AI overview An AWS resilience guide examines a correlated EC2 hardware failure that took down three colocated Cassandra nodes. It covers placement groups, alarms, automated recovery, and observability patterns for distributed data systems. ### Source excerpt Learn how to prevent correlated hardware failures in distributed systems on Amazon EC2. This post walks through real incident response patterns, including Partition Placement Groups, composite alarms, automated recovery with Auto Scaling, and observability best practices. ## Build Durable Chat Memory for RAG Using ScyllaDB and LangChain DevFeed: [Build Durable Chat Memory for RAG Using ScyllaDB and LangChain]() Original publisher: [Read original article]() Author: Cynthia Dunlop Published: 2026-07-14T12:54:49Z Content type: tutorial Language: en Sources: [ScyllaDB]() Topics: [AI Chat](), [Language models]() Tags: [2026-2](), [ai](), [ai-ml](), [ann](), [cassandra](), [database](), [ecosystem](), [embeddings](), [history](), [how-to](), [langchain](), [language-models](), [latency](), [memory](), [rag](), [retrieval](), [scalability](), [sentence-transformers](), [vector]() ### AI overview A tutorial on replacing LangChain's in-memory chat history with ScyllaDB for durable RAG chatbot memory across restarts and replicas. ### Source excerpt How to replace LangChain's in-memory chat history with ScyllaDB -- so your RAG chatbot retains context across restarts and scales across replicas ## How ScyllaDB's Trie-Based Index Delivers Up to 3X More Throughput DevFeed: [How ScyllaDB's Trie-Based Index Delivers Up to 3X More Throughput]() Original publisher: [Read original article]() Author: Tzach Livyatan Published: 2026-06-30T13:03:08Z Content type: article Language: en Sources: [ScyllaDB]() Topics: [benchmarking](), [Benchmark](), [Latency](), [Optimization](), [Caching](), [IO](), [Seastar](), [2026.2](), [Apache Cassandra]() Tags: [2026-2](), [apache](), [architecture](), [benchmark](), [benchmarking](), [cache](), [cassandra](), [deep-dive](), [efficiency](), [engineering](), [latency](), [memory](), [optimization](), [performance](), [product](), [seastar](), [storage](), [time]() ### AI overview ScyllaDB's Trie-based SSTable index replaces separate summary and index files with a prefix tree. The article explains the format change, its storage layout and lookup behavior, and reports benchmark results showing 30% to 230% higher throughput and 31% to 63% lower latency than legacy indexes across four read workloads. ### Source excerpt By transitioning from separate summary and index files to a prefix tree, we optimized cache efficiency, reduced disk I/O, and reduced memory overhead ## Riding the Raft to Strong Consistency in ScyllaDB DevFeed: [Riding the Raft to Strong Consistency in ScyllaDB]() Original publisher: [Read original article]() Author: Cynthia Dunlop Published: 2026-06-24T16:51:27Z Content type: article Language: en Sources: [ScyllaDB]() Topics: [data]() Tags: [2026-2](), [availability](), [cassandra](), [database](), [distributed-systems](), [engineering](), [performance](), [raft]() ### AI overview The article explains ScyllaDB's use of per-tablet Raft groups to provide strong consistency while retaining parallelism. It contrasts this with ScyllaDB's Cassandra-compatible, eventually consistent model and discusses the tradeoffs among latency, availability, and consistency. ### Source excerpt How ScyllaDB is using per-tablet Raft groups to bring strong consistency to data, without sacrificing the parallelism that makes it fast ## How Yelp Upgraded More Than 1,000 Cassandra Nodes from 3.11 to 4.1 Without Downtime DevFeed: [How Yelp Upgraded More Than 1,000 Cassandra Nodes from 3.11 to 4.1 Without Downtime]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-05-11T15:31:13Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [Apache Cassandra](), [distributed-systems](), [Kubernetes](), [Latency](), [Tool]() Tags: [cassandra](), [distributed-systems](), [kubernetes](), [latency](), [migrations](), [stargate]() ### AI overview Yelp's Database Reliability Engineering team upgraded more than 1,000 Cassandra nodes from version 3.11 to 4.1 without downtime. The article describes compatibility work, staged automation, proxy routing, gossip management, and CDC changes used during the migration, and reports up to a 58% reduction in p99 latency and an 11% throughput improvement on key clusters. ### Source excerpt PLUS: OpenAI phone by 2027 πŸ“±, Shadow testing Flink πŸ‘¨πŸ’», Computer use 45x expensive πŸ’° ## Temporal Helm Charts 1.0.0 reaches stability and requires externally provided databases and optional components DevFeed: [Temporal Helm Charts 1.0.0 reaches stability and requires externally provided databases and optional components]() Original publisher: [Read original article]() Author: Tom Wheeler Published: 2026-04-21T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Helm charts](), [Kubernetes](), [configuration](), [Apache Cassandra](), [MySQL](), [PostgreSQL](), [elasticsearch](), [Grafana](), [Prometheus]() Tags: [cassandra](), [configuration](), [elasticsearch](), [grafana](), [helm](), [helm-charts](), [kubernetes](), [migrate](), [mysql](), [product-news](), [prometheus](), [stable]() ### AI overview Temporal's Helm Charts reached version 1.0.0 with a stable API and no expected breaking changes in subsequent releases in the series. The release removes bundled database and optional-component sub-charts, so users must provide those services and configure their connection details. ### Source excerpt Temporal's Helm Charts hit 1.0.0 with a stable API, streamlined architecture, and new configuration options. Learn what changed and how to migrate. ## Zero downtime Upgrade: Yelp's Cassandra 4.x Upgrade Story DevFeed: [Zero downtime Upgrade: Yelp's Cassandra 4.x Upgrade Story]() Original publisher: [Read original article]() Author: Mark Surnin and Muhammad Junaid Muzammil, Software Engineer Published: 2026-04-07T00:00:00Z Content type: article Language: en Sources: [Yelp]() Topics: [Apache Cassandra](), [upgrade](), [NoSQL](), [Database](), [observability](), [Kubernetes]() Tags: [2026](), [apache](), [cassandra](), [clusters](), [downtime](), [kubernetes](), [nosql](), [observability](), [reliability-engineering](), [upgrade]() ### AI overview Yelp's Database Reliability Engineering team describes upgrading more than a thousand Cassandra nodes from 3.11 to 4.1 on Kubernetes without downtime. The article covers the motivation, expected reliability and performance improvements, operational guardrails, certificate handling, repairs, logging, and compatibility work for related components. ### Source excerpt The Database Reliability Engineering team at Yelp seamlessly upgraded more than a thousand Cassandra nodes with zero downtime. This post takes you behind the scenes of our upgrade strategy, from planning sessions to flawless rollouts. Background Motivation Apache Cassandra is a distributed wide-column NoSQL datastore and is used widely at Yelp for storing both primary and derived data. Yelp orchestrates Cassandra clusters on Kubernetes with the help of operators, as explained in our operator overview post. Upgrading from Cassandra 3.11 to 4.1 offered several observability and reliability improvements, in addition to performance gains. Based on public benchmarks, we expected to... ## The RUM Conjecture: You Cannot Optimize Reads, Updates, and Memory at Once DevFeed: [The RUM Conjecture: You Cannot Optimize Reads, Updates, and Memory at Once]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-17T00:00:00Z Content type: article Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [systems](), [Databases](), [Apache Cassandra](), [rocksdb](), [MySQL](), [PostgreSQL]() Tags: [b-tree](), [capacity](), [cassandra](), [database](), [databases](), [dram](), [indexing](), [latency](), [memory](), [mysql](), [node](), [performance](), [rocksdb](), [storage-engine](), [system-design]() ### AI overview The article explains the RUM Conjecture, which describes a tradeoff among read overhead, update overhead, and memory overhead in database indexes and storage engines. It compares B-Trees, LSM-Trees, and hash indexes to show how each optimizes different tradeoffs. ### Source excerpt You can strictly optimise at most two of read overhead, update overhead, and memory overhead; the third will be expensive. B-Trees optimise reads and memory and pay on writes. LSM-Trees optimise writes and memory and pay on reads. Hash indexes optimise reads and writes and pay in RAM. The useful question is not whether a database is good but which corner it optimises and what you are willing to pay for the other two. ## Engineering Features at Scale: Inside the Darwin Feature Store DevFeed: [Engineering Features at Scale: Inside the Darwin Feature Store]() Original publisher: [Read original article]() Author: Dream Blog Published: 2026-01-27T15:38:25Z Content type: article Language: en Sources: [Dream11 Engineering]() Topics: [Feature Engineering](), [Low Latency](), [Apache Cassandra](), [Machine learning](), [Open Source]() Tags: [cassandra](), [data](), [engineering](), [feature-engineering](), [feature-store](), [inference](), [kafka](), [latency](), [machine-learning](), [offline](), [open-source](), [pipelines](), [software-engineering](), [tech]() ### AI overview Dream Horizon's Darwin Feature Store is an open-source, low-latency feature platform designed to support real-time machine learning and offline training at Dream11. The article describes the challenges that led to its development, including batch-only pipelines, manual schema changes, and scaling issues. It reports serving more than 200 million feature requests per minute with p99 read latency below 5 milliseconds and no production incidents over the preceding year. ### Source excerpt By Mohit Jain and Ujjwal Bagrania Dream Horizon, our open-source effort to make Dream11's battle-tested tech available to every developer, brings you the Darwin Feature Store -- a unified, low-latency feature platform built to power real-time ML at scale, and shaped to help teams build, manage, and trust features in production. Explore the Darwin Feature Store here. At Dream11, data and ML have always powered how millions of users experience sports in real time. From personalisation and relevance to ensuring efficiency under peak match traffic, ML sits deep in the critical path of the product. But models are only half the story. The real challenge is managing features -- how they're defined, versioned, and accessed consistently across training and live traffic, especially under peak sports pressure. And at the scale we operate at, feature access doesn't mean the occasional lookup; it means hundreds of millions of requests per minute, spanning both real-time inference and offline training pipelines. As Dream11 grew, feature engineering stopped being something we could manage with pipelines and tables, and became infrastructure that everything else depended on. That shift forced us to build the Darwin Feature Store: a system designed not just to tackle sports-scale traffic, but to make feature engineering predictable, reliable, and developer-friendly in production. Today, that translates into serving 200M+ feature requests per minute, delivering p99 read latencies under 5 ms, and doing so reliably through the most demanding live sports moments, without a single production incident over the last year. In this post, we'll walk through how we built the Feature Store, and what it takes to serve features reliably. The Early Days: Batch-Only, Fragile, and Manual Before the Darwin Feature Store existed, our feature engineering was mostly a few pipelines, a lot of Cassandra tables, and an increasing number of engineers building ML models. Here's what the first version looked li ## Fork yeah: We're adding ten new open source projects to EmeritOSS DevFeed: [Fork yeah: We're adding ten new open source projects to EmeritOSS]() Original publisher: [Read original article]() Published: 2026-01-15T00:00:00Z Content type: news Language: en Sources: [Chainguard: Unchained]() Topics: [Open Source](), [Prometheus](), [Apache Cassandra](), [Amazon S3](), [observability](), [Grafana](), [data-processing]() Tags: [amazon-s3](), [cassandra](), [chainguard-containers](), [chainguard-emeritoss](), [chainguard-images](), [chainguard-open-source](), [dashboards](), [maintenance](), [minio](), [observability](), [open-source](), [open-source-chainguard-images](), [pgcat](), [prometheus](), [security](), [source]() ### AI overview Chainguard announces ten additional open source projects joining EmeritOSS, a program intended to provide long-term maintenance and stability for mature projects. The supplied text highlights MinIO, Prometheus-related exporters, and integrations involving Apache Cassandra and RabbitMQ, covering object storage, monitoring, metrics, and observability. ### Source excerpt We added 10 open source projects to EmeritOSS--including MinIO, Prometheus exporters, and PgCat--to provide long-term, stability-focused maintenance and security. ## ВрСйсинг Π² hh.ru: ΠΊΠ°ΠΊ ΠΌΡ‹ выросли ΠΎΡ‚ 1 тысячи Π΄ΠΎ 1 ΠΌΠΈΠ»Π»ΠΈΠΎΠ½Π° событий Π² сСкунду Π±Π΅Π· сСмплирования DevFeed: [ВрСйсинг Π² hh.ru: ΠΊΠ°ΠΊ ΠΌΡ‹ выросли ΠΎΡ‚ 1 тысячи Π΄ΠΎ 1 ΠΌΠΈΠ»Π»ΠΈΠΎΠ½Π° событий Π² сСкунду Π±Π΅Π· сСмплирования]() Original publisher: [Read original article]() Author: Heruvimka (hh.ru, ΠšΠΎΠ½Ρ„Π΅Ρ€Π΅Π½Ρ†ΠΈΠΈ ОлСга Π‘ΡƒΠ½ΠΈΠ½Π° (ΠžΠ½Ρ‚ΠΈΠΊΠΎ)) Published: 2025-09-16T09:00:45Z Content type: article Language: ru Sources: [HeadHunter RU]() Topics: [observability](), [telemetry](), [SRE](), [Kafka](), [nginx](), [Apache Cassandra](), [OpenTelemetry](), [RabbitMQ]() Tags: [cassandra](), [devops](), [jaeger](), [kafka](), [nginx](), [observability](), [opentelemetry](), [operational-intelligence](), [rabbitmq](), [request](), [sre](), [tag-1e4ee1f65f5a](), [tag-68e701e78517](), [tag-73eb9b712998](), [tag-75f84211cab4](), [tag-8f8626975338](), [tag-9abb13e52060](), [tag-b92bf5906bbd](), [tag-d9df843a1803](), [tag-dfac9042ce7b](), [tracing]() ### AI overview This Russian developer article describes how hh.ru built and rebuilt its tracing and observability architecture as its data volume increased. The supplied text states that the system handles 24,000 RPS, one million spans per second, and 5,000 service instances, and introduces a log-based tracing design using Request-Id propagation. ### Source excerpt Π’ ΠΊΠ°ΠΆΠ΄ΠΎΠΉ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΈ Π΅ΡΡ‚ΡŒ Π½Π΅ΠΎΠ±Ρ…ΠΎΠ΄ΠΈΠΌΠΎΡΡ‚ΡŒ Π²Ρ‹ΡΡ‚Ρ€ΠΎΠΈΡ‚ΡŒ систСму observability. Π’ hh.ru ΠΌΡ‹ пСрСстраивали Π°Ρ€Ρ…ΠΈΡ‚Π΅ΠΊΡ‚ΡƒΡ€Ρƒ ΠΏΠΎΠ΄ большСС количСство Π΄Π°Π½Π½Ρ‹Ρ… нСсколько Ρ€Π°Π· -- сСйчас ΠΈΠΌΠ΅Π΅ΠΌ Π½Π° Π²Ρ…ΠΎΠ΄Π΅ 24ΠΊ RPS, 1 ΠΌΠΈΠ»Π»ΠΈΠΎΠ½ спанов Π² сСкунду, 5ΠΊ инстансов сСрвисов. Если Π²Ρ‹ -- ΠΈΠ½ΠΆΠ΅Π½Π΅Ρ€, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹ΠΉ находится Π² процСссС построСния ΠΈΠ»ΠΈ пСрСстройки собствСнной систСмы трСйсинга, этот Π΄ΠΎΠΊΠ»Π°Π΄ -- для вас. ΠŸΡ€ΠΈΠ²Π΅Ρ‚, Π₯Π°Π±Ρ€! Π― -- АлСксандр ΠšΠ°Π·Π°Π½Ρ†Π΅Π², ΡƒΠΆΠ΅ Π±ΠΎΠ»Π΅Π΅ дСсяти Π»Π΅Ρ‚ Π² Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ΅. Когда-Ρ‚ΠΎ Π±Ρ‹Π» ΠΈΠ½ΠΆΠ΅Π½Π΅Ρ€ΠΎΠΌ Π½Π° ΠΏΠΈΠ²Π·Π°Π²ΠΎΠ΄Π΅ ΠΈ ΠΌΠΎΠ³Ρƒ Ρ€Π°ΡΡΠΊΠ°Π·Π°Ρ‚ΡŒ, ΠΈΠ· Ρ‡Π΅Π³ΠΎ Π΄Π΅Π»Π°ΡŽΡ‚ ΠΏΠ΅Π½Π½ΠΎΠ΅; Π½ΠΎ сСгодня -- ΠΎ Π΄Ρ€ΡƒΠ³ΠΎΠΌ. Π§ΠΈΡ‚Π°Ρ‚ΡŒ Π΄Π°Π»Π΅Π΅ ## How Klaviyo uses a 192-node ClickHouse cluster for real-time segmentation and personalization DevFeed: [How Klaviyo uses a 192-node ClickHouse cluster for real-time segmentation and personalization]() Original publisher: [Read original article]() Author: ClickHouse Published: 2025-08-05T16:55:05Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [personalization](), [real-time](), [data](), [Apache Cassandra](), [Python](), [MySQL]() Tags: [analytics](), [backend](), [cassandra](), [clickhouse](), [data](), [databases](), [mysql](), [personalization](), [python](), [real-time]() ### AI overview This article describes how Klaviyo uses a 192-node ClickHouse cluster to power real-time customer segmentation and personalization. It covers the scale of Klaviyo's event and profile data, the role of its backend data platform, and limitations in the previous architecture involving Python, MySQL, and Cassandra. ### Source excerpt "Our customers want to identify trends in their customer base and interact meaningfully with their customers. ClickHouse powers the engine for that." Patrick McGrath, Lead Software Engineer ## How Wallarm's API security platform relies on ClickHouse Cloud to detect and block attacks DevFeed: [How Wallarm's API security platform relies on ClickHouse Cloud to detect and block attacks]() Original publisher: [Read original article]() Author: ClickHouse Published: 2025-04-30T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [API](), [clickhouse](), [Security](), [threat detection](), [real-time](), [Apache Cassandra](), [vulnerability](), [Cloud]() Tags: [api](), [api-security](), [cassandra](), [clickhouse](), [real-time](), [security](), [self-hosted](), [threat-detection](), [vulnerability]() ### AI overview The article describes how Wallarm's API security platform uses ClickHouse to support real-time analysis, threat detection, and attack blocking. It explains that Cassandra's eventual-consistency model was insufficient for real-time security and that Wallarm moved to a self-hosted ClickHouse deployment to reduce latency and analyze API sessions more effectively. ### Source excerpt "We need our platform to operate in real time. The moment we detect suspicious activity, we aim to block the API user before they can attack the site or exploit a vulnerability." - Slava Yudanov, VP of Engineering, Wallarm ## The simplest way to count 100 billion unique IDs: Part 1 DevFeed: [The simplest way to count 100 billion unique IDs: Part 1]() Original publisher: [Read original article]() Author: Ariel PΓ©rez Published: 2025-03-25T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [real-time](), [Apache Cassandra](), [Kafka](), [Redis](), [Reddit]() Tags: [cassandra](), [i-built-this](), [kafka](), [real-time](), [reddit](), [redis]() ### AI overview A tutorial on building a simpler real-time system for counting unique IDs at very large scale, using Kafka, Redis, and Cassandra as part of the design. ### Source excerpt How to build a simpler, real-time version of Reddit's complex system for counting unique IDs, involving Kafka, Redis, and Cassandra. ## FIPS-ing the Un-FIPS-able: Apache Cassandra DevFeed: [FIPS-ing the Un-FIPS-able: Apache Cassandra]() Original publisher: [Read original article]() Published: 2025-03-05T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Apache Cassandra](), [Cryptography](), [container images](), [Encryption](), [chainguard]() Tags: [apache](), [cassandra](), [chainguard](), [compliance](), [container-images](), [cryptography](), [encryption](), [fedramp](), [fips](), [security]() ### AI overview Chainguard announces FIPS-validated container images for Apache Cassandra 4.0, 4.1, and 5.0, built entirely from source. The work makes Cassandra deployable with FIPS-approved cryptography in regulated industries and government environments, helping customers strengthen software supply chains and meet compliance requirements. ### Source excerpt Chainguard was able to create FIPS container images for Apache Cassandra 4.0, 4.1, and 5.0. See how we did it. ## Rain: A key-value store for Strava's scale DevFeed: [Rain: A key-value store for Strava's scale]() Original publisher: [Read original article]() Author: Derick Yang Published: 2025-01-24T18:02:11Z Content type: article Language: en Sources: [Strava Engineering]() Topics: [data](), [datasets](), [Amazon S3](), [Apache Cassandra](), [Deployment](), [Databases]() Tags: [caching](), [cassandra](), [data](), [data-pipeline](), [databases](), [deployment](), [key-value-store](), [maps](), [memory](), [performance](), [production](), [s3](), [scale](), [spark]() ### AI overview Strava's Geo and Metro teams work with large, immutable datasets derived from map, GPS, and third-party data. The article describes the challenges of serving these datasets and the limitations of using PalDB and Cassandra, including slow deployments, duplicated memory usage, cost constraints, and schema inflexibility. ### Source excerpt Much of our heatmaps are built on batch data outputs stored in Rain At Strava, we love maps -- some of our most loved features are nestled on map surfaces. My team, the Geo team, is focused on building and improving these products. On the Geo and Metro teams, we tend to work with large datasets: aggregations of open source map data via OpenStreetMaps, GPS data points from uploaded activities, third-party datasets for properties like elevation, and beyond. This aggregated dataset eventually turns into Geo features we know and love, like the global heatmap, Strava Metro, the routing product, route suggestions, elevation profiles, and points of interest. We perform these data aggregations in a rather hefty data pipeline, run on a regular cadence to ensure we're serving up-to-date geo data. One of the Geo team's key challenges is efficiently serving large, immutable (write-once, read-many) datasets produced by our pipeline. This is particularly hard for compute-intensive use cases like routing, where: Write-Optimized vs. Read-Optimized Conflict: Traditional read-optimized data stores struggle with large batch writes without impacting read performance or introducing significant operational complexity. Cost Constraints: Storing rarely accessed datasets in production databases can be prohibitively expensive -- especially for projects like Strava Metro, which are accessed sporadically. Schema Complexity: Defining schemas externally from the service that uses them can be costly and inflexible for developers. Previously Our previous solution to large writes used a combination of datastores: PalDB and Cassandra. PalDB is a binary data format ideal for small datasets. The README states that it is optimized for "side data", relatively small datasets you read "in passing" on your service. PalDB is unideal, however, for larger datasets. In our case, since each service deployment required downloading the key-value file from S3, our deployments were taking upwards of twenty minutes. S ## Keycloak DevDay 2024 Videos published DevFeed: [Keycloak DevDay 2024 Videos published]() Original publisher: [Read original article]() Author: Niko KΓΆbler Published: 2024-06-14T00:00:00Z Content type: release Language: en Sources: [Keycloak Blog]() Topics: [Keycloak](), [IAM](), [Multi-tenancy](), [opa](), [Apache Cassandra](), [Extension](), [Testcontainers]() Tags: [cassandra](), [community](), [conference](), [event](), [extension](), [idm](), [kerberos](), [keycloak](), [ldap](), [multi-tenancy](), [opa](), [openid-connect](), [saml](), [sso](), [testcontainers](), [videos]() ### AI overview This announcement says that recorded and live-streamed sessions from the first Keycloak DevDay conference were published online. It lists sessions covering Keycloak, including multi-tenancy, access management with OPA, extension development, Testcontainers, IAM, configuration, and Cassandra. ### Source excerpt Back in February this year, we (Sebastian and me (Niko)) hosted the very first edition of Keycloak DevDay - a one-day, community-driven conference - in Frankfurt/Main, Germany. The event was a blast and completely sold-out, plus many additional participants online in the two parallel live streams. We were able to welcome attendees from all over Europe. Thank you all for being part of this incredible event! πŸ™ For all of you who couldn't attend, we have published all the recorded and live streamed sessions online on my YouTube channel: Keynote: What's Next in Keycloak (Alexander Schwartz, Red Hat) Multi-Tenancy in Keycloak (Garth Patil, Phase Two) Flexible Access Management with Keycloak & OPA (Thomas Darimont, codecentric) Best Practices for Extension Development (Sven-Torben Janus, Conciso) Extensions Development with Testcontainers (Niko KΓΆbler, @dasniko) A Look at Keycloak from the IAM Point of View (Robert Bauer, intension) Automating Keycloak Configuration (Sophie Tauchert, Relaxdays) Ask the Maintainers Session (A. Schwartz, T. Darimont, S. Schuster) IAM Doomsday Prepper (Maik Kingma) Keycloak Cassandra Datastore (Dominik Schlosser) The complete playlist can be found here. We are currently preparing the next edition "Keycloak DeveloperDay 2025". If you want to contribute, please get in touch with us (Sebastian & Niko)! Expect more information in the next weeks and months. Looking forward to have you and your colleagues & team members as attendees! ## Evolving the Backend Storage for Platform Metrics DevFeed: [Evolving the Backend Storage for Platform Metrics]() Original publisher: [Read original article]() Author: David Murray Published: 2024-05-09T22:00:00Z Content type: article Language: en Sources: [Heroku]() Topics: [Heroku](), [Back end](), [Time Series](), [Apache Cassandra](), [Kafka](), [stream-processing](), [autoscaling]() Tags: [apache-kafka](), [app-architecture](), [autoscaling](), [aws](), [backend](), [cassandra](), [cloud-infrastructure](), [data-analytics](), [database](), [developer-tools](), [engineering](), [heroku](), [kafka](), [metrics](), [performance-optimization](), [platform](), [platform-updates](), [stream-processing](), [time-series]() ### AI overview Heroku describes an infrastructure upgrade that migrated the storage backend for platform metrics. The article explains how MetaaS collects application observations, aggregates them into time-series statistics, and uses Kafka, stream-processing jobs, and Cassandra to support dashboard metrics, alerting, autoscaling, retention, and querying. ### Source excerpt One of our most important goals at Heroku is to be boring. Don't get us wrong, we certainly hope that you're excited about the Heroku developer experience -- as heavy users of Heroku ourselves, we certainly are! But, even more so, we hope that you don't have to spend all that much time thinking about [...] The post Evolving the Backend Storage for Platform Metrics appeared first on Heroku. ## How Apple built iCloud to store billions of databases DevFeed: [How Apple built iCloud to store billions of databases]() Original publisher: [Read original article]() Author: Engineer's Codex Published: 2024-01-14T21:53:05Z Content type: article Language: en Sources: [Engineer's Codex]() Topics: [Apache Cassandra](), [Database](), [Multitenancy](), [Cloud](), [NoSQL](), [Architecture & Design](), [Scalability]() Tags: [architecture](), [cassandra](), [cloud](), [database](), [nosql](), [scalability](), [separate]() ### AI overview This article examines how Apple built iCloud and CloudKit using FoundationDB and Cassandra. It discusses the systems' extreme multi-tenant architecture and engineering choices such as asynchronous processing, stateless design, resource isolation, and layered abstractions to support scalability, reliability, availability, and developer experience. ### Source excerpt Apple uses Cassandra and FoundationDB for CloudKit, their cloud backend service. We take a look into how exactly each is used within their cloud and the problems they've solved. ## Find Fermyon at AI.dev for Our Last Conference of 2023 DevFeed: [Find Fermyon at AI.dev for Our Last Conference of 2023]() Original publisher: [Read original article]() Author: Matt Butcher Published: 2023-12-08T12:00:00Z Content type: news Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [Serverless](), [Artificial Intelligence](), [Apache Cassandra]() Tags: [ai](), [cassandra](), [conference](), [serverless](), [sponsor]() ### AI overview Fermyon announces its participation in AI.dev, co-located with Cassandra Summit in San Jose, where attendees can visit its sponsor booth or watch a session about its Serverless AI offering. ### Source excerpt Fermyon's last conference of 2023 will be AI.dev, co-located with Cassandra Summit in San Jose, California. Come by our sponsor booth or watch our session to learn about our Serverless AI offering. ## Legba: An Asynchronous Rust Framework for Authentication Bruteforcing DevFeed: [Legba: An Asynchronous Rust Framework for Authentication Bruteforcing]() Original publisher: [Read original article]() Author: Simone Margaritelli Published: 2023-11-02T16:46:32Z Content type: opinion Language: en Sources: [evilsocket]() Topics: [Tool](), [Rust](), [Tokio](), [Authentication](), [Code]() Tags: [amqp](), [async](), [asynchronous](), [authentication](), [benchmark](), [bruteforce](), [cassandra](), [csrf-token](), [dns-subdomain-enumeration](), [http](), [hydra](), [imap](), [kerberos](), [kerberos-pre-authentication](), [ldap](), [legba](), [mongodb](), [mssql](), [multi-protocol](), [mysql](), [ntlm](), [offensive-tools](), [oracle](), [password](), [pgsql](), [pop3](), [project-release](), [rdp](), [red-team](), [rust](), [tokio](), [tool](), [wordlist]() ### AI overview The article presents Legba, a Rust-based authentication bruteforcing tool developed as an asynchronous and modular alternative to tools such as THC Hydra. It discusses the motivation, design objectives, supported features, and a limited localhost benchmark comparing Legba with Hydra. ### Source excerpt During the last few weeks I've been working on a new tool that started as a way for me to become more familiar with Rust and its ## Durable Digest: March 2023 DevFeed: [Durable Digest: March 2023]() Original publisher: [Read original article]() Author: Temporal Technologies Published: 2023-03-30T00:00:00Z Content type: news Language: en Sources: [Temporal Blog]() Topics: [Development](), [Command-line interface](), [SDKs](), [Replication](), [MySQL](), [Apache Cassandra](), [PostgreSQL]() Tags: [announcements](), [cassandra](), [cli](), [mysql](), [postgresql](), [release](), [replication](), [sdk](), [updates](), [v1]() ### AI overview Temporal's March 2023 Durable Digest summarizes Temporal v1.20 updates, including the Temporal CLI, Scheduled Actions, SQL-based Advanced Visibility, archival changes, Workflow Update, cross-shard replication, authorizer improvements, and Batch API support for DeleteWorkflow. It also highlights SDK updates, events, and community content. ### Source excerpt Explore Temporal v1.20 updates, new SDK features, upcoming events, and community highlights in our March newsletter. Stay informed and engaged! ## Trino at Apple DevFeed: [Trino at Apple]() Original publisher: [Read original article]() Author: Vinitha Gankidi, Yathi Peddyshetty, Brian Olsen Published: 2022-11-28T00:00:00Z Content type: article Language: en Sources: [Trino Blog]() Topics: [Apache Iceberg](), [Kubernetes](), [autoscaling](), [interoperability](), [A/B Testing](), [Apache Cassandra](), [Routing (disambiguation)]() Tags: [apple](), [autoscaling](), [cassandra](), [development](), [features](), [interoperability](), [kubernetes](), [operator](), [post](), [posts](), [scale](), [spark](), [summit](), [technical](), [testing](), [use-cases]() ### AI overview This post recaps how Apple deploys and operates Trino as a self-service service for analytics, federated querying, and A/B testing. Apple uses a Kubernetes operator and custom orchestrator to manage cluster lifecycles, provisioning, autoscaling, and related operational complexity. The article highlights Apple's use of Apache Iceberg, Hive, and Cassandra connectors, contributions improving Trino and Spark interoperability and Iceberg API coverage, current challenges, and planned community contributions. ### Source excerpt This post continues a larger series of posts on the Trino Summit 2022 sessions. Following the Keynote: State of Trino session, engineers from Apple shared the current usage of Trino at Apple. They discuss how they support Trino as a service for multiple end-users, and the critical features that drew Apple to Trino. They wrap up with some challenges they have faced and some development they have planned to contribute to Trino. [Next page]()