# Data Infrastructure Data infrastructure comprises systems, tools, and capabilities for collecting, storing, processing, governing, and using data. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Seagate and WD AI Storage Research Finds Enterprises Rank Storage Above Compute as the AI Bottleneck DevFeed: [Seagate and WD AI Storage Research Finds Enterprises Rank Storage Above Compute as the AI Bottleneck]() Original publisher: [Read original article]() Author: Lyle Smith Published: 2026-09-15T17:23:54Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Data Infrastructure](), [AI Infrastructure](), [idc](), [Inference](), [Retrieval-Augmented Generation](), [synthetic-data]() Tags: [ai](), [compute](), [data-infrastructure](), [datasets](), [enterprise](), [genai](), [hdd](), [idc](), [inference](), [reports](), [research](), [retrieval](), [storage](), [synthetic-data]() ### AI overview Seagate and WD published separate studies indicating that AI is increasing enterprise storage requirements and extending data retention. Although their headline percentages differ because they asked different questions, both reports point to storage becoming a larger part of AI infrastructure planning alongside growing archive and retrieval needs. ### Source excerpt Seagate and WD published separate AI storage studies within days of each other; the headline numbers: Seagate says 99% of enterprises expect AI to increase their storage requirements over the next three years, while WD's IDC research puts the comparable figure at 74%. Read the fine print, and both reports land in the same directional The post Seagate and WD AI Storage Research Finds Enterprises Rank Storage Above Compute as the AI Bottleneck appeared first on StorageReview.com. ## Streamhouse Working Group proposes a vendor-neutral architecture for real-time data infrastructure DevFeed: [Streamhouse Working Group proposes a vendor-neutral architecture for real-time data infrastructure]() Original publisher: [Read original article]() Author: Alexander Gallego Published: 2026-09-15T00:00:00Z Content type: opinion Language: en Sources: [Redpanda]() Topics: [Data Infrastructure](), [real-time](), [Streaming](), [Artificial Intelligence](), [Redpanda-Connect]() Tags: [ai](), [announcement](), [data-infrastructure](), [real-time](), [redpanda-connect](), [streaming]() ### AI overview Redpanda says it has joined Aiven, Confluent, StreamNative, and Ververica to form the Streamhouse Working Group, which proposes a vendor-neutral architecture for real-time operational data infrastructure. The article distinguishes Streamhouse from the lakehouse by focusing on continuously processing current data for production applications and AI agents. ### Source excerpt Redpanda has joined Aiven, Confluent, StreamNative, and Ververica to form the Streamhouse Working Group. Here's what that means for the future of real-time data infrastructure. ## VDURA Deploys High-Performance Storage Platform for AI and HPC at New Mexico State University DevFeed: [VDURA Deploys High-Performance Storage Platform for AI and HPC at New Mexico State University]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-02T10:00:00Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Artificial Intelligence](), [High-Performance Computing](), [Data Infrastructure](), [InfiniBand](), [Post-quantum cryptography](), [Deployment](), [Training AI Models](), [Hardware](), [Low Latency](), [Quantum Computing]() Tags: [ai](), [data-infrastructure](), [deployment](), [enterprise](), [enterprise-storage](), [hardware](), [high-performance-computing](), [infiniband](), [low-latency](), [post-quantum-cryptography-pqc](), [quantum](), [storage]() ### AI overview VDURA has moved its storage platform at New Mexico State University into full production to support AI and high-performance computing research. The deployment combines NVMe flash and high-density HDD tiers through a global namespace over InfiniBand, allowing separate scaling of performance and capacity. It also supports NMSU's post-quantum cryptography research and large-scale data pipeline projects. ### Source excerpt VDURA has completed the deployment of its data platform at New Mexico State University (NMSU), moving the system into full production. The infrastructure is designed to serve the university's research community with a high-durability, high-throughput storage environment tailored specifically for artificial intelligence and high-performance computing (HPC) workloads. NMSU, which holds Carnegie R1 status and manages The post VDURA Deploys High-Performance Storage Platform for AI and HPC at New Mexico State University appeared first on StorageReview.com. ## Core Banking Modernization with Temenos Core and CockroachDB DevFeed: [Core Banking Modernization with Temenos Core and CockroachDB]() Original publisher: [Read original article]() Author: Nanda Badrappan,Muruga Balakrishnan Published: 2026-08-24T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [Cockroach Labs](), [Data Infrastructure](), [Availability](), [real-time](), [Resilience](), [Transactions](), [legacy](), [SQL](), [Cloud](), [systems](), [on-prem]() Tags: [availability](), [cloud](), [cockroach-labs](), [cockroachdb](), [data-infrastructure](), [legacy](), [on-prem](), [real-time](), [resilience](), [sql](), [transactions]() ### AI overview This article explains why banks are modernizing legacy core banking infrastructure. It describes how Temenos Core and CockroachDB are positioned to support real-time transaction processing, continuous availability, distributed operations, resilience, and regulatory requirements. ### Source excerpt Banking has become an always-on business. Customers expect instant payments, accurate balances, and continuous access to financial services... ## Data Engineering Weekly #280 DevFeed: [Data Engineering Weekly #280]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-07-27T03:37:19Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [Data Infrastructure](), [Open Source](), [Data Quality](), [DuckDB](), [Database]() Tags: [data](), [data-engineering](), [data-infrastructure](), [data-quality](), [database](), [duckdb](), [engineering](), [open-source](), [python]() ### AI overview Data Engineering Weekly #280 is a newsletter roundup covering updates to leetdata.ai and aidataengineer.io, agent-oriented data infrastructure, open-source modern data stack tools, data-tool landscapes, metric certification, and data quality in the AI era. ### Source excerpt The Weekly Data Engineering Newsletter ## The data platform 1Password needed didn't exist. So we built it. DevFeed: [The data platform 1Password needed didn't exist. So we built it.]() Original publisher: [Read original article]() Author: info@1password.com (Wayne Duso; Mandy Gu; and Amie Bright) Published: 2026-07-22T00:00:00Z Content type: article Language: en Sources: [Blog on 1Password Blog]() Topics: [data](), [Data Infrastructure](), [data-governance](), [real-time](), [Unified Access](), [Artificial Intelligence]() Tags: [agentic](), [ai](), [building-1password](), [data](), [data-governance](), [data-infrastructure](), [developers](), [real-time](), [sql](), [systems](), [unified-access](), [workflows]() ### AI overview 1Password describes building an internal data platform to make business data accessible, trustworthy, and available in real time across product, finance, engineering, analytics, SQL, APIs, dashboards, and AI workflows. The effort addresses bottlenecks caused by a centralized data lake, bespoke pipelines, and tightly coupled storage, governance, and compute. ### Source excerpt Consider a few tasks that take place across every business, every day: A product team ships a feature and wants to know if customers are using it successfully. A finance team needs customer and account information for planning. An analyst needs definitions to create a report. An AI assistant needs operational context to answer a business question. Those sound like different workflows, but they all rely on the same underlying data. And each one of these actors, across each of these teams, needs that data to be both accessible and trustworthy. At 1Password, trust is at the center of everything we build. Millions of people and businesses rely on us to protect the credentials, secrets, and access workflows that power modern work. The same principle applies to our own internal data. As our products, systems, and use of AI evolved, data became a shared dependency across the business. It powers everything from Unified Access andsecure agentic access patterns for customers, to the workflows used by product, finance, and engineering teams. As those systems grew, so did the number of people and applications that depended on our internal data. But our data infrastructure did not respond well to this. We had built a centralized data lake supported by a growing collection of bespoke data pipelines and one-off solutions. Each new use case required another integration or transformation. Over time, the data platform became a bottleneck: teams turned to CSVs to move faster, and data engineers spent more time maintaining pipelines than enabling new capabilities. What we learned was that manually moving data was no longer enough. Data needs to be available in real time, accessible wherever it's needed, and trusted through the forms our customers need, whether that is through SQL, APIs, dashboards or AI workflows. Privacy and governance need to be built into data the moment it's created so every downstream use remains secure and unambiguous by design. This is the foundation we're build ## Core dump epidemiology: fixing an 18-year-old bug DevFeed: [Core dump epidemiology: fixing an 18-year-old bug]() Original publisher: [Read original article]() Published: 2026-06-30T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [bug](), [OpenAI](), [Rockset](), [C++](), [Data Infrastructure](), [Azure](), [Hardware](), [race-condition](), [Open Source](), [Streaming]() Tags: [azure](), [bug](), [c-plus-plus](), [data-infrastructure](), [engineering](), [hardware](), [open-source](), [openai](), [race-condition](), [software]() ### AI overview OpenAI engineers analyzed a large population of core dumps to investigate rare crashes in the Rockset service. The investigation uncovered two unrelated causes: silent CPU corruption on an Azure host and an 18-year-old race condition in GNU libunwind. ### Source excerpt OpenAI engineers used large-scale core dump analysis to debug rare infrastructure crashes, uncovering both a hardware fault and a long-standing software bug. ## Customer Analysis for SaaS: A Framework for Real Decisions DevFeed: [Customer Analysis for SaaS: A Framework for Real Decisions]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-06-13T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [data](), [Data Infrastructure](), [Framework]() Tags: [analytics](), [churn](), [data](), [data-infrastructure](), [growth](), [guide](), [how-to](), [retention](), [saas]() ### AI overview This guide presents a framework for SaaS customer analysis, covering segmentation, customer behavior, willingness to pay, retention drivers, and the data infrastructure needed to turn customer data into actionable business decisions. ### Source excerpt Customer analysis framework for SaaS founders. Segmentation, behavior, willingness-to-pay, retention drivers, and the data infrastructure to make it actionable. ## Scaling beyond one: How Airbnb evolved its data architecture for a multi-product world DevFeed: [Scaling beyond one: How Airbnb evolved its data architecture for a multi-product world]() Original publisher: [Read original article]() Author: Patrick Lam Published: 2026-06-09T17:01:02Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [data-architecture](), [Data Infrastructure](), [data-modeling](), [data]() Tags: [analytics](), [architecture](), [data](), [data-architecture](), [data-engineering](), [data-infrastructure](), [data-modeling](), [engineering](), [offline](), [post](), [technology]() ### AI overview Airbnb's data and analytics engineering teams evolved a decade-old offline data warehouse to support Homes, Experiences, and Services. The article examines the trade-offs between separate product-specific data models and a unified monolithic model while describing the need for a consistent, flexible, and scalable data foundation. ### Source excerpt How Airbnb's data engineers and analytics engineers built a consistent and flexible data modeling framework to support the expansion into Homes, Experiences, and Services. By: Patrick Lam, Namrata Lamba, Jamie Stober With the May 2025 Summer Release, Airbnb redesigned its app, relaunched Experiences, and debuted Services, pushing us beyond our traditional Homes focus. For the data teams, this meant rapidly evolving a decade-old infrastructure to integrate two brand-new product pillars. Our data engineers and analytics engineers rose to the challenge by building a consistent and flexible framework to serve as a robust and scalable data foundation for the next decade of growth. But getting there wasn't straightforward. This fundamental shift surfaced a critical question for our data organization: How do you evolve your offline data architecture to support new product lines without introducing disorder in vital analytics services? We knew the approach we took would have long-lasting implications. A fragmented strategy risked creating data silos, inconsistent analytics, and a tangled web of technical debt that would likely slow down future innovation. In this post, we'll take you behind the scenes to share key decisions that we made, the framework that emerged, and the lessons that helped reshape our offline data warehouse for the future. Note that we focus specifically on our offline data warehouse (the analytics-oriented data infrastructure owned by our data engineers and analytics engineers) rather than the online data systems that serve the app directly, as the two domains have fundamentally different requirements, constraints, and design philosophies that warrant separate treatment. The core dilemma: separate vs. monolithic The first and most critical question was how to structure offline data for the new, three-product world, with Homes, a refreshed Experiences product, and the new Services offering. This involved a trade-off between two main approaches: Separate ## ClickHouse appoints new leader for Asia Pacific and expands global go-to-market leadership team DevFeed: [ClickHouse appoints new leader for Asia Pacific and expands global go-to-market leadership team]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-06-08T00:00:00Z Content type: news Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Data Infrastructure](), [real-time](), [databricks](), [Amazon Web Services](), [amazon](), [Microsoft]() Tags: [2026](), [amazon](), [clickhouse](), [cloud](), [company](), [databricks](), [global](), [leadership](), [leadership-team](), [microsoft](), [revenue](), [vmware]() ### AI overview ClickHouse announced the appointment of Ed Lenta as Vice President for Asia Pacific and Japan and expanded its global go-to-market leadership team. Lenta will lead regional efforts to grow ClickHouse Cloud and the open-source database, while Takeshi Kaneko has joined as Country Manager of ClickHouse Japan. The appointments support ClickHouse's plans to scale its global organization and address increasing demand for real-time analytics and data infrastructure. ### Source excerpt ClickHouse, a leader in real-time analytics, data warehousing, observability, and AI/ML, today announced an expansion of its global go-to-market (GTM) leadership team, including the appointment of Ed Lenta as Vice President, Asia Pacific and Japan (APJ). ## How to Choose a Database for an AI-Powered Product DevFeed: [How to Choose a Database for an AI-Powered Product]() Original publisher: [Read original article]() Author: David Weiss Published: 2026-06-01T00:00:00Z Content type: tutorial Language: en Sources: [Cockroach Labs]() Topics: [Database](), [AI Infrastructure](), [AI Platform](), [Data Infrastructure](), [autoscaling]() Tags: [ai](), [ai-infrastructure](), [capacity](), [concurrency](), [data](), [database](), [how-to](), [product](), [scale]() ### AI overview This article presents seven criteria for choosing a database for an AI-powered product in production. It emphasizes evaluating elastic scalability, concurrency, correctness, workload demands, global distribution, and access by autonomous agents rather than selecting a database solely for model support or vector search. ### Source excerpt Organizations across fintech, healthcare, retail, gaming, SaaS, and scores more verticals are embedding AI into business-critical offerings. ## Real-time streaming for the agentic era with NVIDIA DevFeed: [Real-time streaming for the agentic era with NVIDIA]() Original publisher: [Read original article]() Author: Melissa Czapiga Published: 2026-06-01T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Vera CPU](), [streaming-data-processing](), [Latency](), [Data Infrastructure](), [agent observability](), [systems]() Tags: [agentic](), [agents](), [announcement](), [compute](), [data-infrastructure](), [latency](), [nvidia](), [nvidia-vera](), [performance](), [streaming](), [systems]() ### AI overview Redpanda describes its collaboration with NVIDIA to run streaming workloads on NVIDIA Vera CPUs, reporting 5.5x lower latency for AI agents in mission-critical environments. The article discusses testing on workloads processing billions of messages daily, real-time data infrastructure, and potential benefits for enterprise AI and agentic applications. ### Source excerpt NVIDIA Vera launches today with Redpanda as part of the ecosystem, delivering 5.5x lower latencies for agents running in mission-critical environments. ## AI Agents Need Context to Reason, Not Just Data DevFeed: [AI Agents Need Context to Reason, Not Just Data]() Original publisher: [Read original article]() Author: Quentin Packard Published: 2026-05-28T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [Artificial Intelligence](), [context](), [AI Agent](), [Agentic AI Architecture](), [Data Infrastructure]() Tags: [agentic-ai](), [agents](), [ai](), [ai-architecture](), [context](), [data-infrastructure](), [database]() ### AI overview The article argues that production failures in AI agents often stem from inadequate context management rather than the model itself. Reliable agent behavior requires current data, memory, permissions, observability, and awareness of system constraints, making context management a data infrastructure problem beyond basic retrieval or prompt engineering. ### Source excerpt When your AI agent makes a bad decision in production, what do you blame? ## How We Built a Zero-Downtime Database Migration Service at Wix DevFeed: [How We Built a Zero-Downtime Database Migration Service at Wix]() Original publisher: [Read original article]() Author: Wix Engineering Published: 2026-05-14T07:19:17Z Content type: article Language: en Sources: [Wix Engineering]() Topics: [Databases](), [migration](), [Data Infrastructure](), [MySQL](), [Scalability]() Tags: [amazon-msk](), [data-infrastructure](), [database](), [database-migration](), [databases](), [db-topology](), [debezium-connector](), [good-tools-doesn-t-fit](), [how-db-mover-works](), [infrastructure](), [migration](), [mysql](), [scalability](), [summary-6-key-focal-points](), [the-challenge](), [the-python-service](), [the-result]() ### AI overview Wix describes DB Mover, an internal service for transparent, zero-downtime database migrations between shared and dedicated MySQL clusters. The article explains the operational risks of shared clusters, migration requirements, and the topology managed by Wix's Data Infrastructure team. ### Source excerpt The Challenge At Wix, multiple applications share the same DB cluster. The reasons vary: consolidating apps from the same domain, grouping several small apps that don't justify a dedicated cluster, or simply optimizing cost and operational overhead. However, this setup comes with a significant risk: every application on the cluster has the potential to impact all the others. One real example: we had a critical service related to user authentication sharing a DB cluster with several other... ## Gala supercharges analytics performance with ClickHouse on AWS DevFeed: [Gala supercharges analytics performance with ClickHouse on AWS]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-05-04T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Amazon Web Services (AWS)](), [data analytics](), [Data Infrastructure](), [data-platforms](), [telemetry](), [databricks](), [Blockchain]() Tags: [aws](), [blockchain](), [clickhouse](), [data-analytics](), [data-infrastructure](), [data-platforms](), [databricks](), [telemetry]() ### AI overview Gala migrated from Databricks to ClickHouse on AWS to address growing data volumes and improve analytics performance. The article reports a threefold increase in analytics capacity, a 30% cost reduction, and query times reduced from minutes to sub-second. ### Source excerpt Learn how Gala migrated to the ClickHouse to improve analytics performance and cut costs ## Building Resilient Fintech Infrastructure for Scale DevFeed: [Building Resilient Fintech Infrastructure for Scale]() Original publisher: [Read original article]() Author: David Weiss Published: 2026-04-02T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [Resilience](), [Data Infrastructure](), [Database](), [CockroachDB](), [legacy]() Tags: [cockroach-labs](), [cockroachdb](), [compliance](), [fintech](), [fraud-detection](), [infrastructure](), [legacy](), [outages](), [postgresql](), [resilience](), [uptime]() ### AI overview The article argues that fintech and quant firms need resilient data infrastructure to handle growth, real-time payments, instant settlement, AI-driven fraud detection, and cross-border compliance. It describes the limits of legacy database architectures and presents SumUp's migration from PostgreSQL to CockroachDB as an example of improved resilience and near-zero downtime. ### Source excerpt The fintech companies and quant firms that define the next decade aren't just building better products. They're succeeding with more resilient fintech infrastructure. ## How to Sell Datasets and Data Products Online DevFeed: [How to Sell Datasets and Data Products Online]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-03-30T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [datasets](), [API Monetization](), [Data Infrastructure](), [API](), [CSV](), [JSON](), [Training AI Models]() Tags: [api](), [api-monetization](), [article](), [data-infrastructure](), [datasets](), [digital-products](), [guide](), [how-to](), [payments](), [technical]() ### AI overview This guide explains how to sell datasets and data products online, covering static CSV, JSON, and SQL datasets, real-time APIs, and machine-learning training data. It discusses data preparation, intellectual property, pricing models, billing, taxes, secure delivery, and using Dodo Payments as a merchant of record. ### Source excerpt A comprehensive guide on monetizing CSV/JSON datasets, API data feeds, and ML training data using Dodo Payments and modern data infrastructure. ## How to Charge for Webhooks and Real-Time Data Feeds DevFeed: [How to Charge for Webhooks and Real-Time Data Feeds]() Original publisher: [Read original article]() Author: Aarthi Poonia Published: 2026-03-28T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [real-time](), [API Monetization](), [Data Infrastructure](), [Software as a service](), [API]() Tags: [api](), [api-monetization](), [billing](), [data-infrastructure](), [how-to](), [payments](), [real-time](), [saas](), [streaming](), [usage-based-billing](), [webhooks]() ### AI overview A guide to monetizing webhooks and real-time data feeds with pricing models tied to delivered events, endpoints, feeds, bandwidth, and usage. It also covers high-volume metering and usage-based billing using Dodo Payments. ### Source excerpt Monetize your real-time data infrastructure. Learn how to implement per-event and bandwidth-based billing for webhooks using Dodo Payments. ## A million events per second: How Lago scales usage-based billing with ClickHouse Cloud DevFeed: [A million events per second: How Lago scales usage-based billing with ClickHouse Cloud]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-03-05T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Cloud](), [Data Infrastructure](), [Critical Infrastructure](), [Open Source](), [Security]() Tags: [billing](), [clickhouse](), [cloud](), [critical-infrastructure](), [data-infrastructure](), [latency](), [open-source](), [security](), [usage-based-billing]() ### AI overview Lago uses ClickHouse Cloud and ClickPipes to ingest, store, and query usage events for real-time, usage-based billing. The article describes scaling from 10,000 to 1 million events per second while supporting complex enterprise pricing models without operating its own data infrastructure. ### Source excerpt "We tried so many databases. The only one that worked really well and that was really easy to understand was ClickHouse." "We're the only billing solution that can provide one million events per second ingestion. Without ClickHouse, this wouldn't be poss ## Hello, Agent! A podcast for the agentic enterprise DevFeed: [Hello, Agent! A podcast for the agentic enterprise]() Original publisher: [Read original article]() Author: Redpanda Published: 2026-03-04T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [AI Agent](), [Cybersecurity](), [Data Infrastructure](), [Databases]() Tags: [2026](), [agentic](), [agents](), [ai](), [ai-agents](), [announcement](), [cybersecurity](), [data-infrastructure](), [databases](), [enterprise](), [podcast]() ### AI overview Hello, Agent! is a podcast about building, deploying, and scaling secure autonomous AI systems for enterprise use. The article introduces the podcast and previews an episode on secure architectures for enterprise AI agents, including sensitive-data protection and confidential vector databases. ### Source excerpt Cut through the hype and learn how enterprise leaders are designing, deploying, and scaling AI agents in the real world. ## How Layered Data Systems Create Complexity and Sprawl DevFeed: [How Layered Data Systems Create Complexity and Sprawl]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-03-02T22:56:11Z Content type: opinion Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [data](), [systems](), [Data Infrastructure](), [Development](), [databricks](), [dashboards]() Tags: [architecture](), [article](), [dashboards](), [data](), [data-infrastructure](), [databricks](), [development](), [systems]() ### AI overview This opinion article examines how data stacks accumulate layers of roles, tools, and platforms. It argues that although these layers can simplify development and speed experimentation, they can also create BI, pipeline, model, agent, cost, and system sprawl. ### Source excerpt How data stacks turn into fractals ## Exploring Data Systems and Building with Rust After Leaving Google DevFeed: [Exploring Data Systems and Building with Rust After Leaving Google]() Original publisher: [Read original article]() Author: Nevin Zheng Published: 2026-02-27T00:00:00Z Content type: opinion Language: en Sources: [nevzheng]() Topics: [Data Infrastructure](), [BigQuery](), [Google](), [Rust](), [systems]() Tags: [bigquery](), [data-infrastructure](), [google](), [rust](), [systems]() ### AI overview A former BigQuery engineer describes leaving Google after four years, exploring storage engines and Rust, and looking for a smaller team working on challenging data-infrastructure problems. ### Source excerpt Left Google, exploring data systems, building things for fun, and figuring out what's next. ## Branches are GA: data infrastructure for agents DevFeed: [Branches are GA: data infrastructure for agents]() Original publisher: [Read original article]() Author: Jorge Sancha Published: 2026-02-24T12:00:00Z Content type: release Language: en Sources: [Tinybird]() Topics: [Data Infrastructure](), [Kafka](), [Amazon S3](), [ci](), [Development]() Tags: [agentic-development](), [agents](), [ci](), [connectors](), [data](), [data-infrastructure](), [development](), [kafka](), [product-updates](), [s3](), [workflows]() ### AI overview Tinybird announces general availability for Branches, adding Kafka, S3, and GCS connectors, CI preview deployments, and agentic development workflows. ### Source excerpt Branches now support Kafka, S3, and GCS connectors, preview deployments from CI, and agentic development workflows. Branches are how agents develop with Tinybird. ## How Wix Built AI-Driven Incident Response at Scale with ClickHouse and Wild Moose DevFeed: [How Wix Built AI-Driven Incident Response at Scale with ClickHouse and Wild Moose]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-02-10T12:16:20Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Incident response](), [Artificial Intelligence](), [AI Bots](), [Automation](), [log management](), [telemetry](), [dashboards](), [Microservices](), [Data Infrastructure](), [Resilience]() Tags: [ai](), [ai-agents](), [automation](), [clickhouse](), [complexity](), [cost](), [dashboards](), [incident](), [incident-response](), [infrastructure](), [logging](), [logs](), [microservices](), [resilience]() ### AI overview Wix built AI-driven incident response on ClickHouse and Wild Moose to investigate large volumes of production alerts while preserving access to logs and dashboards. The article describes how ClickHouse supports Wix's high-scale, centralized logging foundation and agentic workloads, with the approach achieving 90% root cause accuracy across more than 30,000 monthly alerts. ### Source excerpt Wix paired ClickHouse with Wild Moose's AI agents to automate incident response at scale, achieving 90% root cause accuracy across 30,000+ monthly alerts. [Next page]()