# data-processing The structured transformation and lifecycle management of data to produce usable information. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## OWC Acquires OpenDrives, Adding Atlas, Astraeus, and Edge to the Jellyfish Shared Storage Line DevFeed: [OWC Acquires OpenDrives, Adding Atlas, Astraeus, and Edge to the Jellyfish Shared Storage Line]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-15T16:55:08Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [data-architecture](), [cloud-infrastructure](), [Hardware](), [data-processing]() Tags: [acquisition](), [cloud-native](), [company](), [enterprise](), [enterprise-storage](), [hybrid-cloud](), [infrastructure](), [products](), [storage]() ### AI overview Other World Computing (OWC) has acquired OpenDrives, bringing the Atlas, Astraeus, and Edge platforms into its Jellyfish shared storage portfolio. The companies say the deal expands OWC's capabilities in enterprise data management, hybrid cloud orchestration, and edge workflows; financial terms were not disclosed. ### Source excerpt Other World Computing (OWC) has acquired OpenDrives, the Los Angeles software-defined storage company whose Atlas platform has sat behind Hollywood studios, post houses, and live broadcast networks since 2011. The deal brings OpenDrives' Atlas, Astraeus, and Edge platforms into OWC's shared storage portfolio alongside the Jellyfish line, and OWC says it extends that line into The post OWC Acquires OpenDrives, Adding Atlas, Astraeus, and Edge to the Jellyfish Shared Storage Line appeared first on StorageReview.com. ## Announcing instance preference lists for Amazon SageMaker AI training jobs DevFeed: [Announcing instance preference lists for Amazon SageMaker AI training jobs]() Original publisher: [Read original article]() Author: Kanwaljit Khurmi Published: 2026-09-15T16:01:47Z Content type: release Language: en Sources: [Artificial Intelligence]() Topics: [Amazon SageMaker AI](), [GPU](), [Model Development](), [data-processing]() Tags: [amazon-sagemaker-ai](), [announcements](), [gpu](), [intermediate-200](), [model-development](), [pipelines](), [scheduled](), [training]() ### AI overview Amazon SageMaker AI introduces instance preference lists for training and processing jobs. Users can specify up to five instance types in priority order, and SageMaker AI launches the job on the first option with available capacity, reducing manual retries and capacity monitoring. ### Source excerpt Amazon SageMaker AI now offers instance preference lists for training and processing jobs. Specify an ordered list of up to five instance types, and SageMaker AI automatically launches on the first type with available capacity, eliminating manual retry loops and capacity-watching scripts. ## How Everpure proposes reducing GPU idle time by improving AI data access DevFeed: [How Everpure proposes reducing GPU idle time by improving AI data access]() Original publisher: [Read original article]() Author: Chris Mellor Published: 2026-09-15T08:00:00Z Content type: article Language: en Sources: [www.theregister.com - Articles]() Topics: [AI Infrastructure](), [GPU](), [Large Language Model](), [Retrieval Augmented Generation (RAG)](), [AI Agent](), [data-processing](), [data]() Tags: [agents](), [ai](), [ai-infrastructure](), [ai-ml](), [data-processing](), [gpu](), [hardware](), [rag](), [sponsored]() ### AI overview This sponsored feature describes Everpure's approach to reducing GPU idle time in AI systems by improving access to large-scale insurance data. It discusses central metadata indexing, storage performance, self-describing data, and integration with Nvidia GPU infrastructure for AI agents and retrieval-augmented generation. ### Source excerpt SPONSORED FEATURE: The vendor's AI solutions are dedicated to increasing GPU utilization and avoiding costly GPUs doing nothing while waiting for data ## Second-Gen Single-Rack AWS Outposts Puts 2,688 vCPUs and 100TB of EBS in One 42U Rack DevFeed: [Second-Gen Single-Rack AWS Outposts Puts 2,688 vCPUs and 100TB of EBS in One 42U Rack]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-12T18:24:22Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [AWS Outposts](), [Amazon Web Services](), [cloud-infrastructure](), [Network](), [Latency](), [data-processing](), [Security]() Tags: [5g](), [automation](), [aws](), [aws-outposts](), [cloud](), [compute](), [connectx](), [data-processing](), [enterprise](), [governance](), [latency](), [low-latency](), [networking](), [security]() ### AI overview AWS has generally released a second-generation single-rack AWS Outposts configuration: a self-contained 42U rack combining compute, storage, and networking with up to 2,688 vCPUs and 100 TB of Amazon EBS. The article describes its on-premises cloud compatibility, compact footprint, supported instance families, and accelerated networking options for trading floors and 5G cores. ### Source excerpt AWS has made second-generation single-rack AWS Outposts generally available, a self-contained 42U rack that puts compute, storage, and networking together with up to 2,688 vCPUs and 100 TB of Amazon EBS. It runs the same APIs, console, automation, governance policies, and security controls as the multi-rack second-generation Outposts and the parent AWS Region, so an The post Second-Gen Single-Rack AWS Outposts Puts 2,688 vCPUs and 100TB of EBS in One 42U Rack appeared first on StorageReview.com. ## The 6 best HIPAA-compliant form builders for sensitive information DevFeed: [The 6 best HIPAA-compliant form builders for sensitive information]() Original publisher: [Read original article]() Author: Adam Lehman Published: 2026-09-12T00:00:00Z Content type: comparison Language: en Sources: [Webflow Blog]() Topics: [data](), [data-processing](), [Encryption](), [Software](), [cloud-infrastructure]() Tags: [compliance](), [data](), [encryption](), [enterprise](), [forms](), [healthcare](), [strategy](), [workflows]() ### AI overview A comparison of six HIPAA-compliant form builders for healthcare teams collecting protected health information (PHI). It evaluates Business Associate Agreement requirements, safeguards such as encryption and audit logging, pricing and plans, form capabilities, and suitability for workflows ranging from patient intake to complex clinical questionnaires. ### Source excerpt Compare the 6 best HIPAA-compliant form builders for healthcare teams collecting PHI, from affordable to enterprise options. ## Backblaze B2 and WEKA NeuralMesh Validated as a Two-Tier AI Storage Pipeline, With Snap-to-Object Checkpoints in B2 DevFeed: [Backblaze B2 and WEKA NeuralMesh Validated as a Two-Tier AI Storage Pipeline, With Snap-to-Object Checkpoints in B2]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-10T20:40:57Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [AI Infrastructure](), [cloud-infrastructure](), [Cloud](), [data-processing](), [model-deployment]() Tags: [accelerators](), [ai](), [ai-infrastructure](), [cloud](), [cloud-storage](), [data](), [datasets](), [enterprise](), [inference](), [infrastructure](), [integration](), [performance](), [snapshots](), [storage]() ### AI overview Backblaze and WEKA validated a two-tier AI storage pipeline that uses WEKA NeuralMesh as the high-performance tier for GPU workloads and Backblaze B2 Cloud Storage as the capacity tier. Raw data, training sets, media, source files, checkpoints, and other assets can move between the tiers according to access needs. NeuralMesh's Snap-to-Object feature was also tested with B2 for storing consistent filesystem snapshots and supporting recovery. ### Source excerpt Backblaze and WEKA have validated their two platforms together for AI pipelines, pairing WEKA NeuralMesh as the performance tier that feeds GPUs with Backblaze B2 Cloud Storage as the capacity tier that holds everything else. The integration, sizing, tuning, and testing are already done, so an AI infrastructure team can deploy a proven two-tier layout The post Backblaze B2 and WEKA NeuralMesh Validated as a Two-Tier AI Storage Pipeline, With Snap-to-Object Checkpoints in B2 appeared first on StorageReview.com. ## Announcing 90-minute function timeout on AWS Lambda Managed Instances DevFeed: [Announcing 90-minute function timeout on AWS Lambda Managed Instances]() Original publisher: [Read original article]() Author: Tarun Rai Madan Published: 2026-09-09T19:08:14Z Content type: release Language: en Sources: [AWS Compute Blog]() Topics: [data-processing](), [Transcodings](), [Amazon EC2]() Tags: [ai-inference](), [announcements](), [aws](), [aws-lambda](), [batch](), [data-processing](), [event](), [event-driven](), [foundational-100](), [serverless]() ### AI overview AWS Lambda Managed Instances now support a 90-minute timeout for asynchronous and event source mapping invocations, increasing the previous 15-minute limit by six times. The update targets longer-running data processing, media transcoding, AI inference, and batch workloads. ### Source excerpt AWS Lambda now supports a 90-minute function timeout for asynchronous and event source mapping (ESM) invocations on Lambda Managed Instances, a 6x increase from the previous 15-minute limit. Data processing, media transcoding, AI inference, and batch workloads can now run on Lambda without re-architecting. ## Passkey-themed social engineering leads to identity and cloud compromise DevFeed: [Passkey-themed social engineering leads to identity and cloud compromise]() Original publisher: [Read original article]() Author: Microsoft Security Research, Krithika Ramakrishnan, Bharat Vaghela, Vaibhav Deshmukh, Subhajit Ghosh, Anusha Chakraborty, Akash Chaudhuri, Victor Chingtham and Ivan Macalintal Published: 2026-09-09T17:41:18Z Content type: article Language: en Sources: [Microsoft Security Blog]() Topics: [MFA](), [data-processing]() Tags: [adversary-in-the-middle-aitm](), [apis](), [attacks](), [authentication](), [cloud](), [data](), [identity](), [mfa](), [phishing](), [security](), [social-engineering]() ### AI overview Microsoft Security Research describes a passkey-themed social-engineering campaign that compromises cloud identities through AiTM phishing or device-code flows, establishes authentication persistence, and collects cloud data. It outlines investigation signals and recommends revoking sessions and removing unauthorized authentication methods after confirmed compromise. ### Source excerpt Passkey-themed social engineering is being used to compromise identities and enable broader cloud attacks. Learn how threat actors establish MFA persistence, abuse Microsoft Graph for reconnaissance, and access SharePoint, OneDrive, and email data, along with key detection and mitigation guidance. The post Passkey-themed social engineering leads to identity and cloud compromise appeared first on Microsoft Security Blog. ## A Unified Data Architecture For Sovereign Agentic AI With VMware Tanzu And VMware vSAN DevFeed: [A Unified Data Architecture For Sovereign Agentic AI With VMware Tanzu And VMware vSAN]() Original publisher: [Read original article]() Author: arnab chakraborty Published: 2026-09-03T23:27:50Z Content type: article Language: en Sources: [VMware Blogs]() Topics: [Agentic AI Architecture](), [AI Architecture](), [data-architecture](), [AI Agent](), [cloud-infrastructure](), [Latency](), [real-time](), [Security](), [data-processing]() Tags: [agentic-ai](), [agentic-ai-architecture](), [ai](), [ai-architecture](), [blog](), [cloud](), [data-architecture](), [efficiency](), [infrastructure](), [latency](), [modern-apps](), [real-time](), [security]() ### AI overview The article presents a unified, on-premises data architecture for sovereign agentic AI using VMware Tanzu, VMware Tanzu Greenplum, VMware Cloud Foundation, and VMware vSAN. It argues that placing AI compute close to enterprise data can improve performance and cost while reducing latency, data-transfer fees, and compliance risks. ### Source excerpt By combining VMware Tanzu Greenplum with VMware vSAN, organizations can bring their AI compute directly to their data storage layer for improved cost and latency. The post A Unified Data Architecture For Sovereign Agentic AI With VMware Tanzu And VMware vSAN appeared first on Tanzu. The post A Unified Data Architecture For Sovereign Agentic AI With VMware Tanzu And VMware vSAN appeared first on VMware Blogs. ## Soft deletes affect database constraints, indexes, and application queries DevFeed: [Soft deletes affect database constraints, indexes, and application queries]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-08-20T00:00:00Z Content type: opinion Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [data-processing](), [Database](), [MySQL]() Tags: [data-modelling](), [database-design](), [foreign-keys](), [indexes](), [indexing](), [mysql](), [orm](), [postgresql](), [predicate](), [schema](), [schema-design](), [soft-delete]() ### AI overview The article explains how soft deletion affects database design beyond adding a deleted_at column. It discusses duplicate-key failures, foreign-key behavior, queries that may return deleted rows, and index inefficiency, noting that PostgreSQL partial indexes help while MySQL requires a workaround. ### Source excerpt A deleted_at column turns every future query into a conditional one, and the cost is not the extra predicate. Unique constraints stop working because the deleted row still occupies the key, foreign keys start pointing at rows the application considers gone, and any query written by someone who does not know about the column silently returns deleted data. Soft delete is a data lifecycle decision, and treating it as a boolean column is what makes it expensive. ## Modeling Device Capabilities for Analytics DevFeed: [Modeling Device Capabilities for Analytics]() Original publisher: [Read original article]() Author: Netflix Technology Blog Published: 2026-07-31T16:01:02Z Content type: article Language: en Sources: [Netflix](), [Netflix TechBlog - Medium]() Topics: [Netflix](), [data](), [data-processing](), [datasets](), [Hardware](), [cpu](), [Streaming](), [Software]() Tags: [analytics](), [cloud-gaming](), [cpu](), [data](), [data-engineering](), [data-modeling](), [datasets](), [devices](), [hardware](), [netflix](), [streaming]() ### AI overview Netflix describes a device-capability data model for analytics across a diverse ecosystem of streaming devices. Cumulative and histogram tables capture device capabilities, active device counts, software versions, and feature support, helping teams measure feature reach and make more granular enablement decisions. ### Source excerpt by Aarti Laddha, Richard Diaz-Cool, Rishika Idnani, Venkatesh Selveraj Netflix supports a vast and evolving set of features and content types, ranging from 4K streaming and immersive audio to live streaming and cloud gaming, across a diverse ecosystem of devices. However, not all devices are created equal. Hardware limitations such as available RAM, CPU cores, display capabilities, or platform support mean that some features cannot be supported on certain device models. To ensure the best possible user experience, we rely on a deep understanding of device capabilities. We have invested in building a comprehensive device capability data model and integrating feature flags from internal systems, paving the way for smarter, more granular feature management across our global device landscape. This approach helps us identify bottlenecks in feature penetration and accelerates the pace of innovation. We have designed our data storage and modeling strategies to efficiently support analytics at scale. We use a cumulative table to process information about the device's capabilities. This table is structured to efficiently capture the latest state of each device and its associated capabilities (like Screen resolutions, Video Profiles Supported, Surround Sound, RAM size etc) making it ideal for analytics and reporting use cases. { "Screen Height": ["720"], "Screen Width": ["1280"], "Video Profiles": [ "playready", "hevc", ], } For aggregate analytics, we leverage a histogram table that captures active device counts over the past 28 days, broken down by device model and software version. This table also records the number of devices supporting specific capabilities, enabling detailed distribution analysis. One use case for this histogram data is to analyze the distribution of external display capabilities attached to streaming sticks. For example, the histogram below shows that out of total X number of devices, all supported the HD profile (playready), while only 20% devices sup ## The continuous validation framework for data pipelines. DevFeed: [The continuous validation framework for data pipelines.]() Original publisher: [Read original article]() Author: Niruta Talwekar Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [data](), [DataOps](), [Data Quality](), [data observability](), [CI/CD](), [data-processing](), [DevOps](), [Platform Engineering]() Tags: [ci-cd](), [data](), [data-quality](), [devops](), [platform-engineering](), [provenance](), [software-testing](), [streaming]() ### AI overview The article introduces the Continuous Validation Framework (CVF), an end-to-end methodology for validating data pipelines through architectural isolation, configuration-driven data quality management, and continuous automation based on lineage-driven impact analysis. It reports production results including a 50% reduction in incidents and an 80% improvement in detecting data quality issues. ### Source excerpt A framework for automated, end-to-end data pipeline validation using isolation, declarative quality checks, and lineage-driven impact analysis. ## Security incident disclosure -- July 2026 DevFeed: [Security incident disclosure -- July 2026]() Original publisher: [Read original article]() Author: system Published: 2026-07-16T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [incident](), [Security](), [Cybersecurity](), [vulnerability](), [supply-chain-security](), [data-processing](), [AI Platform](), [datasets](), [Threat Research]() Tags: [agentic](), [ai](), [cybersecurity](), [incident](), [llm](), [secrets](), [security](), [supply-chain](), [tokens](), [vulnerability]() ### AI overview Hugging Face discloses a July 2026 security incident involving unauthorized access to limited internal datasets and service credentials. The intrusion began through code-execution paths in dataset processing, enabled lateral movement across internal clusters, and involved an autonomous agent framework executing numerous actions across short-lived sandboxes. Hugging Face reports that public models, datasets, Spaces, container images, and published packages showed no evidence of tampering, and describes remediation including vulnerability fixes, credential rotation, cluster rebuilding, stronger controls, and improved detection. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## How Trigger.dev is using ClickHouse to scale observability for long-running AI workflows DevFeed: [How Trigger.dev is using ClickHouse to scale observability for long-running AI workflows]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-07-09T13:07:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [observability](), [telemetry](), [Serverless](), [observability ai agents](), [data-processing](), [Databases](), [Open Source]() Tags: [ai](), [ai-agent](), [clickhouse](), [logs](), [observability](), [open-source](), [serverless](), [telemetry](), [traces](), [video]() ### AI overview Trigger.dev migrated observability workloads for its serverless asynchronous workflow platform from Postgres to ClickHouse. The move addressed scaling bottlenecks while enabling fast analysis of logs, traces, and execution telemetry, with P95 queries around 200 ms and substantially lower telemetry storage requirements. ### Source excerpt Trigger.dev migrated from Postgres to ClickHouse to scale observability for its serverless AI workflow platform, delivering ~200 ms P95 query performance and a dramatically smaller telemetry storage footprint. ## NVIDIA Vera CPU Boosts AI Factory Throughput to Accelerate Agentic Workloads DevFeed: [NVIDIA Vera CPU Boosts AI Factory Throughput to Accelerate Agentic Workloads]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-07-07T18:10:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Vera CPU](), [AI Factory](), [Artificial Intelligence](), [systems](), [Inference](), [Orchestration](), [model-deployment](), [Cache](), [data-processing](), [Neural Network](), [PyTorch]() Tags: [agentic](), [agentic-ai-generative-ai](), [ai](), [ai-agent](), [ai-factory](), [cache](), [cpu](), [data-processing](), [featured](), [inference](), [model](), [nvidia-vera](), [orchestration](), [performance](), [rl](), [top-stories](), [vera-cpu]() ### AI overview This NVIDIA developer article explains how the NVIDIA Vera CPU can improve AI factory throughput for agentic workloads. It emphasizes sustained per-core performance for CPU tasks between model steps, including tool calls, code execution, sandbox evaluations, data processing, orchestration, KV-cache coordination, and result handling. The article also describes how CPU performance affects reinforcement learning rollouts, user response time, and cached-context efficiency. ### Source excerpt Agentic systems turn model reasoning into action through multi-step workflows that combine inference, tool use, code execution, retrieval, orchestration, and... ## Vercel Sandbox can now run for up to 24 hours DevFeed: [Vercel Sandbox can now run for up to 24 hours]() Original publisher: [Read original article]() Author: Rob Herley Published: 2026-06-16T00:01:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [data-processing](), [Persistence]() Tags: [agentic](), [data-processing](), [documentation](), [e2e-testing](), [enterprise](), [persistence](), [sandboxes](), [scale](), [vercel]() ### AI overview Vercel Sandboxes now support uninterrupted sessions of up to 24 hours, increased from 5 hours. The longer runtime supports large-scale data processing, end-to-end testing pipelines, and long-lived agentic workflows, with persistent sandboxes preserving state across extended runs. ### Source excerpt Vercel Sandboxes can run uninterrupted sessions for up to 24 hours (up from 5 hours). This new max duration unlocks workloads that require longer runtimes, such as large-scale data processing, E2E testing pipelines, and long-lived agentic workflows. Pair with persistent sandboxes to maintain durable state across extended runs. The 24 hour max duration is available on all Pro and Enterprise plans. Learn more about limits in the documentation and see how Vercel Sandbox duration and persistence work. Read more ## How an astrophysicist uses Codex to help simulate black holes DevFeed: [How an astrophysicist uses Codex to help simulate black holes]() Original publisher: [Read original article]() Published: 2026-06-11T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [codex](), [Algorithms](), [data-processing](), [Programming](), [Simulation and Design]() Tags: [algorithms](), [applied-ai](), [codex](), [data](), [data-processing](), [event](), [images](), [model](), [relativity](), [scale](), [space](), [time](), [tools](), [workflows]() ### AI overview Astrophysicist Chi-kwan Chan uses Codex to refine and test algorithms for simulating electrons and ions around black holes. The article explains how these simulations, data processing, and large-scale computing workflows help interpret Event Horizon Telescope observations and study extreme physics and general relativity. ### Source excerpt Discover how astrophysicist Chi-kwan Chan uses Codex to build black hole simulations, helping scientists study extreme physics and test Einstein's theory of general relativity. ## Introducing Apache Arrow Support in mssql-python DevFeed: [Introducing Apache Arrow Support in mssql-python]() Original publisher: [Read original article]() Author: Saumya Garg Published: 2026-05-04T04:33:00Z Content type: release Language: en Sources: [Microsoft Python Engineering]() Topics: [sql-server](), [data-processing](), [interoperability](), [data](), [pandas]() Tags: [apache-arrow](), [arrow](), [azure](), [azure-sql](), [client-driver](), [data-processing](), [interoperability](), [pandas](), [python](), [python-driver-for-azure-sql](), [python-driver-for-sql-server](), [sql-server](), [sql-server-2025](), [zero-copy]() ### AI overview Microsoft introduces Apache Arrow support in mssql-python, enabling SQL Server data to be fetched directly into Arrow structures for Polars, Pandas, DuckDB, and other Arrow-native libraries. The approach is intended to reduce Python object creation and memory overhead during data processing. ### Source excerpt Reviewed by Sumit Sarabhai Fetching a million rows from SQL Server into a Polars DataFrame used to mean a million Python objects, a million GC allocations, and then throwing it all away to build a DataFrame. Not anymore. mssql-python now supports fetching SQL Server data directly as Apache Arrow structures - a faster and more [...] The post Introducing Apache Arrow Support in mssql-python appeared first on Microsoft for Python Developers Blog. ## Payment Compliance: GDPR and PSD2 Obligations for SaaS DevFeed: [Payment Compliance: GDPR and PSD2 Obligations for SaaS]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-15T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [data-processing](), [Security, Privacy and Abuse Prevention](), [Authentication](), [tokenization]() Tags: [authentication](), [compliance](), [data-processing](), [gdpr](), [merchant-of-record](), [payment](), [saas](), [tokenization]() ### AI overview A guide to GDPR and PSD2 obligations for SaaS companies handling payments for European customers. It covers personal-data handling, Strong Customer Authentication, data minimization, retention, breach notification, tokenization, and merchant-of-record arrangements. ### Source excerpt Understand how GDPR and PSD2 affect your SaaS payment flows. Covers data handling obligations, Strong Customer Authentication, and how a merchant of record simplifies compliance. ## ClickHouse at FOSDEM 2026 DevFeed: [ClickHouse at FOSDEM 2026]() Original publisher: [Read original article]() Author: Tyler Hannan Published: 2026-04-08T18:00:47Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [data-processing](), [real-time](), [debugging](), [LLVM](), [log management](), [Shell](), [SQL](), [dashboards](), [observability](), [Parser](), [Tooling]() Tags: [bugs](), [clickhouse](), [community](), [conference](), [data](), [debugging](), [llvm](), [logging](), [performance](), [real-time](), [sql](), [visualization]() ### AI overview ClickHouse at FOSDEM 2026 recaps the ClickHouse community dinner and technical talks on real-time visualization, production hotpatching with LLVM XRay, and inverted database indexes. ### Source excerpt FOSDEM 2026 took place on 31 January and 1 February in Brussels, and it was a great weekend for the ClickHouse community, both inside the conference rooms and out. ## Kubeflow SDK v0.4.0: Model Registry, SparkConnect, and Enhanced Developer Experience DevFeed: [Kubeflow SDK v0.4.0: Model Registry, SparkConnect, and Enhanced Developer Experience]() Original publisher: [Read original article]() Author: Kubeflow SDK Team Published: 2026-03-19T05:00:00Z Content type: release Language: en Sources: [Kubeflow]() Topics: [SDKs](), [MLOps](), [Apache Spark](), [data-processing](), [Hyperparameter optimization](), [Machine learning](), [Kubernetes](), [Python]() Tags: [apache-spark](), [data-processing](), [hyperparameter-optimization](), [kubernetes](), [mlops](), [python](), [release](), [sdk]() ### AI overview Kubeflow SDK v0.4.0 introduces a Model Registry Client, SparkClient with SparkConnect support, namespaced TrainingRuntimes, dataset and model initializers, and new documentation. The release targets a unified Python interface for AI workloads on Kubernetes across data processing, model management, and ML pipelines. ### Source excerpt Explore the full documentation at sdk.kubeflow.org ## How Socialpruf built a faster, more reliable data stack by replacing Neon with Postgres managed by ClickHouse DevFeed: [How Socialpruf built a faster, more reliable data stack by replacing Neon with Postgres managed by ClickHouse]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-03-17T12:18:28Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [dashboards](), [data-processing](), [Database](), [Node.js](), [event driven]() Tags: [analytics](), [clickhouse](), [dashboards](), [data](), [data-processing](), [event-driven](), [node](), [postgres]() ### AI overview Socialpruf migrated its analytics workload from Neon to Postgres managed by ClickHouse Cloud. The platform uses ClickHouse for customer-facing social analytics, aggregating millions of rows in milliseconds to power near-instant dashboards, while Node.js and Python components collect and process data from multiple social platforms. ### Source excerpt Socialpruf migrated from Neon to Postgres managed by ClickHouse, eliminating network transfer costs and achieving up to 5x faster query performance while powering real-time social analytics dashboards that aggregate millions of rows in milliseconds. ## How Agoda Load Balanced Kafka DevFeed: [How Agoda Load Balanced Kafka]() Original publisher: [Read original article]() Author: Saurabh Dashora Published: 2026-03-10T08:01:37Z Content type: article Language: en Sources: [System Design Codex]() Topics: [Kafka](), [data-processing](), [systems]() Tags: [data-centers](), [data-processing](), [kafka](), [parallelism](), [partitioning](), [round-robin]() ### AI overview A deep dive into how Agoda uses Kafka to process large volumes of real-time supplier price updates. It explains the distributor, processor, and Kafka components, then examines how partitions, partitioners, and consumer assignors support parallel processing and expose challenges when workloads and consumer capabilities are uneven. ### Source excerpt Deep Dive ## Astro 5.17 DevFeed: [Astro 5.17]() Original publisher: [Read original article]() Author: Erika Published: 2026-01-29T00:00:00Z Content type: release Language: en Sources: [The Astro Blog]() Topics: [configuration](), [data-processing]() Tags: [astro](), [data-processing](), [privacy](), [release]() ### AI overview Astro 5.17 adds configurable dev-toolbar placement, asynchronous loader parsing, and partitioned-cookie support for embedded contexts. ### Source excerpt Astro 5.17 brings configurable dev toolbar placement, support for partitioned cookies, and powerful new image optimization options. [Next page]()