# aws-s3 Published articles for aws-s3. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Metering Object Storage and S3 Billing for SaaS DevFeed: [Metering Object Storage and S3 Billing for SaaS]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-05-18T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Amazon S3](), [Software as a service](), [cloud-infrastructure](), [Azure](), [Cloud](), [Google Cloud Platform (GCP)]() Tags: [architecture](), [aws](), [aws-s3](), [azure](), [cloud](), [google-cloud](), [guide](), [how-to](), [object-storage](), [s3](), [saas](), [storage](), [usage-based-billing]() ### AI overview This article explains how SaaS products can meter object storage across uploads, stored capacity, egress, and storage operations. It describes an architecture for handling rolling storage measurements, retries, partial failures, high volume, and different pricing models across AWS S3, Google Cloud Storage, Azure Blob Storage, and compatible object stores. ### Source excerpt Bill customers for storage uploads, capacity, and bandwidth across S3, GCS, and Azure Blob. Architecture for accurate object storage metering. ## Engineering Around Extreme S3 Scale at Scribd DevFeed: [Engineering Around Extreme S3 Scale at Scribd]() Original publisher: [Read original article]() Author: R Tyler Croy Published: 2026-02-10T00:00:00Z Content type: article Language: en Sources: [Scribd Tech]() Topics: [Amazon S3](), [Cloud](), [data](), [Amazon Web Services]() Tags: [aws](), [aws-s3](), [cloud](), [featured](), [s3](), [scale](), [scribd](), [talk](), [youtube]() ### AI overview Scribd discusses engineering challenges at extreme AWS S3 scale, including checking whether files are damaged, controlling costs, and consolidating objects to reduce spending. The discussion is also available as audio and video. ### Source excerpt Scribd has absolutely fascinating data-at-scale type problems, all the way down to the fundamentals of how we use AWS S3. In my previous post I wrote about the design of Content Crush and how Scribd is consolidating objects in S3 to minimize our costs. Related to that work I was fortunate enough to join the (in)famous Corey Quinn to talk about Engineering around Extreme S3 scale: ## How Scribd's Content Crush consolidates S3 objects for storage cost management DevFeed: [How Scribd's Content Crush consolidates S3 objects for storage cost management]() Original publisher: [Read original article]() Author: R Tyler Croy Published: 2026-01-12T00:00:00Z Content type: article Language: en Sources: [Scribd Tech]() Topics: [Amazon S3](), [Amazon Web Services](), [data](), [parquet](), [cloud-infrastructure]() Tags: [apache-parquet](), [aws](), [aws-s3](), [cost](), [data](), [featured](), [object-storage](), [retrieval](), [rust](), [s3](), [storage]() ### AI overview Scribd describes Content Crush, a system that consolidates the many objects generated from uploaded documents and presentations into a single Apache Parquet file in Amazon S3 while preserving virtualized keyspace and retrieval semantics. The system addresses storage-management complexity and the cost of S3 Intelligent-Tiering monitoring at Scribd's scale. ### Source excerpt Scribd and Slideshare have been using AWS S3 for almost twenty years and store hundreds of billions of objects making storage management quite a challenge. My focus at Scribd has generally been around data and storage but only in the past twelve months have I started to really focus on one of our hardest technology problems: cost-effective storage and availability for the hundreds of billions of objects that represent our content library. ## Durable Digest: November 2025 DevFeed: [Durable Digest: November 2025]() Original publisher: [Read original article]() Author: Temporal Technologies Published: 2025-11-25T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Monitoring](), [Prometheus](), [Authorization](), [Large Language Model](), [Model Context Protocol](), [SIEM, Security, Observability](), [Amazon S3](), [configuration](), [account](), [export]() Tags: [announcements](), [aws-s3](), [blog-post](), [llm](), [mcp](), [observability](), [permissions](), [preview](), [prometheus]() ### AI overview Temporal's November 2025 Durable Digest reviews recently shipped Temporal Cloud features, including group-based permissions, SCIM user management, environment configuration, workflow history export to object storage, and an OpenMetrics endpoint. It also highlights a durable stateful agents project using Mistral, Temporal, and MCP. ### Source excerpt Recently shipped features, real-world fixes from the field, a Builder Spotlight straight from the Code Exchange, fresh AI how-tos, and where to meet us live. ## Reducing Infrastructure Complexity: Why I Ditched S3+CloudFront+Lambda DevFeed: [Reducing Infrastructure Complexity: Why I Ditched S3+CloudFront+Lambda]() Original publisher: [Read original article]() Author: Shivam Anand Published: 2025-08-24T07:48:09Z Content type: opinion Language: en Sources: [Shivam Anand - DevOps & Cloud Engineering Blog]() Topics: [Amazon S3](), [migration](), [Optimization](), [Image](), [Cross-origin resource sharing (CORS)]() Tags: [api-keys](), [aws](), [aws-s3](), [blog-performance](), [cloud-infrastructure](), [cloudfront](), [cloudinary](), [cors](), [devops](), [devops-cloud-infrastructure](), [iam](), [image](), [image-transformations](), [lambda](), [lambda-edge](), [media-optimization](), [migration](), [optimization](), [performance](), [strapi]() ### AI overview The author describes migrating a blog's media management from AWS S3, CloudFront, and Lambda to Cloudinary after finding the multi-service setup unnecessarily complex for a small blog. They highlight Cloudinary's simpler onboarding and URL-based image transformations. ### Source excerpt Discover how I simplified media management for my blog by moving from AWS S3, CloudFront, and Lambda to Cloudinary. Learn about URL-based transformations, automatic optimization, and faster performance. ## Terraform module to manage Oxbow Lambda and its components DevFeed: [Terraform module to manage Oxbow Lambda and its components]() Original publisher: [Read original article]() Author: Oleh Motrunych Published: 2025-03-14T00:00:00Z Content type: release Language: en Sources: [Scribd Tech]() Topics: [Terraform](), [AWS Lambda](), [infrastructure as code (IAC)](), [Amazon Web Services](), [AWS Glue](), [Amazon Simple Queue Service (SQS)](), [data-processing](), [event driven](), [DynamoDB](), [Amazon S3]() Tags: [aws](), [aws-glue](), [aws-lambda](), [aws-s3](), [deltalake](), [dynamodb](), [event-driven](), [iac](), [oxbow](), [rust](), [security](), [sqs](), [terraform]() ### AI overview The article introduces terraform-oxbow, an open-source Terraform module for deploying and managing an Oxbow AWS Lambda workflow and its supporting components. It describes configurable integrations including AWS Glue, Kinesis Data Firehose, SQS, DynamoDB, IAM policies, and S3 notifications, while noting AWS notification limits and least-privilege considerations. ### Source excerpt Oxbow is a project to take an existing storage location which contains Apache Parquet files into a Delta Lake table. It is intended to run both as an AWS Lambda or as a command line application. We are excited to introduce terraform-oxbow, an open-source Terraform module that simplifies the deployment and management of AWS Lambda and its supporting components. Whether you're working with AWS Glue, Kinesis Data Firehose, SQS, or DynamoDB, this module provides a streamlined approach to infrastructure as code (IaC) in AWS. ## Could IPFS Have Prevented the Bybit Hack? DevFeed: [Could IPFS Have Prevented the Bybit Hack?]() Original publisher: [Read original article]() Author: Daniel Norman Published: 2025-02-28T00:00:00Z Content type: opinion Language: en Sources: [IPFS]() Topics: [IPFS](), [Security](), [Web](), [smart contract]() Tags: [aws-s3](), [defense-in-depth](), [ipfs](), [object-object](), [s3](), [security](), [smart-contract](), [social-engineering]() ### AI overview The article examines the Bybit hack, in which a malicious Safe frontend led multisig owners to sign a harmful transaction. It argues that IPFS content addressing and local verification could have provided an additional preventive layer for dapp frontends, while noting that the exact method of AWS S3 access was unclear. ### Source excerpt The Bybit hack revealed several security failures, this post examines whether IPFS could have helped prevent the hack and practical solutions for dapp developers. ## Blog: Falco Talon v0.3.0 DevFeed: [Blog: Falco Talon v0.3.0]() Original publisher: [Read original article]() Published: 2025-02-11T00:00:00Z Content type: release Language: en Sources: [Falco - Falco](), [Falco - The Falco blog]() Topics: [Falco](), [Amazon S3](), [MinIO](), [Command-line interface](), [Kubernetes]() Tags: [aws](), [aws-s3](), [cli](), [falco](), [kubernetes](), [minio](), [release](), [talon](), [version]() ### AI overview Falco Talon 0.3.0 adds a kubernetes:sysdig action that captures syscalls when suspicious events occur in a pod and exports the resulting artifact to AWS S3 or MinIO. The release also describes configuring capture duration and buffer size, and exploring captures with the sysdig CLI or Stratoshark. ### Source excerpt Today, we announce the release of Falco Talon 0.3.0 🦅! Three updates in a row, after Falco and Falcosidekick, it's time for Falco Talon to know a new version. What's new? The key feature this release brings is the new actionner kubernetes:sysdig. For those who are not familiar with sysdig, it's a CLI tool that allows to capture and record the syscalls, like tcpdump does for the network packets. Old brother of Falco, they share the same libs and filters. With this new integration, when a suspicious event occurs in a pod, Talon triggers a capture and then exports the created artifact to AWS S3 or Minio. You can configure the duration and the amount of bytes captured for each syscall. Check out the docs to discover more settings. See this example rule: - action: Capture the syscalls actionner: kubernetes:sysdig parameters: buffer_size: 2048 duration: 20 output: target: minio:s3 parameters: bucket: falco-talon prefix: /sysdig/ After the action has been completed, you'll find the capture in Minio: And you can run the CLI tool sysdig to explore it: ❯ sysdig -r 2025-01-23T13-26-41Z_default_cncf-597d69dbd4-h9fcb_sysdig.scap.gz evt.type=execve and evt.dir=">" 18563 14:26:38.376178286 0 bash (616444.616444) > execve filename=/usr/bin/apt 19163 14:26:38.394972623 0 apt (616445.616445) > execve filename=/usr/bin/dpkg 19599 14:26:38.399546432 0 apt (616446.616446) > execve filename=/usr/lib/apt/methods/http 20319 14:26:38.408846350 0 apt (616447.616447) > execve filename=/usr/lib/apt/methods/http 21775 14:26:38.453363037 0 apt (616448.616448) > execve filename=/usr/lib/apt/methods/gpgv 22335 14:26:38.461330752 0 apt (616449.616449) > execve filename=/usr/lib/apt/methods/gpgv 29434 14:26:38.481292691 0 gpgv (616451.616451) > execve filename=/usr/bin/apt-key 29604 14:26:38.486522901 0 apt-key (616453.616453) > execve filename=/usr/bin/apt-config 30183 14:26:38.494442117 0 apt-config (616454.616454) > execve filename=/usr/bin/dpkg 30422 14:26:38.497278722 0 apt-key (616455.616455) ## Deploy a Nanoc Static Site to S3 with GitHub Actions DevFeed: [Deploy a Nanoc Static Site to S3 with GitHub Actions]() Original publisher: [Read original article]() Author: ratneshmaurya2311@gmail.com (Ratnesh Maurya) Published: 2024-11-23T00:00:00Z Content type: tutorial Language: en Sources: [Ratn Labs]() Topics: [Amazon S3](), [GitHub Actions](), [Deployment](), [AWS IAM](), [Amazon Web Services]() Tags: [aws](), [aws-s3](), [cloud-devops](), [complete](), [deploy](), [deployment](), [github-actions](), [guide](), [iam](), [policy](), [s3](), [setup](), [static](), [static-site](), [using](), [website](), [workflow](), [yaml]() ### AI overview A step-by-step tutorial explains how to automate deployment of a Nanoc static website to AWS S3 with GitHub Actions. It covers the S3 bucket policy, IAM setup, and the complete workflow YAML. ### Source excerpt A step-by-step guide to automating Nanoc website deployment to AWS S3 using GitHub Actions, including S3 bucket policy, IAM setup, and the complete workflow YAML. ## Secure your terraform.state file with Terraform Vault Backend DevFeed: [Secure your terraform.state file with Terraform Vault Backend]() Original publisher: [Read original article]() Author: Zalan Blenessy Published: 2024-10-29T08:45:07Z Content type: tutorial Language: en Sources: [Volvo Cars Engineering - Medium]() Topics: [Terraform](), [HashiCorp Vault](), [Amazon S3](), [DevOps](), [HTTP](), [CI/CD]() Tags: [automotive-industry](), [aws-s3](), [backend-development](), [ci-cd](), [devops](), [hashicorp-vault](), [http](), [s3](), [security](), [software-development](), [terraform](), [vault]() ### AI overview This article introduces the Terraform Vault Backend, a specialized Terraform HTTP backend for storing terraform.state files in HashiCorp Vault. It explains why shared remote state is needed, how Terraform state works, and why state files containing secrets may require more secure storage than ordinary blob stores such as AWS S3. ### Source excerpt tl;dr: The Terraform Vault Backend is the missing complement to the Terraform Vault Provider. Volvo Cars has since 2017 strategically grown its software engineering organisation. Today, we have thousands of software engineers and hundreds of DevOps engineers to facilitate efficient tooling and workflows for software development and release. In my role as a CI Architect at the Complete Software Factory (CSwF) department, I am accountable for the CI/CD systems at CSwF. Most of our services are hosted in "the Cloud" and terraform is the most popular tool used by our DevOps engineers to provision Cloud infrastructure. This article introduces the Terraform Vault Backend, a specialised Terraform HTTP Backend which allows you to store your terraform.state file in HashiCorp Vault. This is relevant if your terraform.state contains secrets, too sensitive to be stored in ordinary blob stores like AWS S3. Terraform is a tool that generates infrastructure from code. Given some configuration, running terraform apply makes Terraform intelligently carry out the actions needed to create the desired infrastructure. For example, you can put the following configuration in main.tf: resource "local_file" "somefile" { content = "foo bar baz" filename = "${path.module}/somefile.txt" } Then apply it with: $ terraform init $ terraform apply Out comes a file called somefile.txt with the content foo bar baz. You can inspect it with: $ cat somefile.txt foo bar baz It also creates a bunch of other files in your folder, most notably the terraform.state file. This file reflects the current state of the "infrastructure" (somefile.txt), which has been provisioned for you. Terraform is idempotent, which means that running terraform apply a second time, with the same input configuration, will not change anything as somefile.txt has already been added to terraform.state . Sharing Terraform State It is not enough to share your Terraform configuration files with your colleagues, you also need to share th ## How to create an AWS S3 bucket for Postgres backups DevFeed: [How to create an AWS S3 bucket for Postgres backups]() Original publisher: [Read original article]() Author: Paul Scanlon Published: 2024-10-28T18:26:13Z Content type: tutorial Language: en Sources: [Blog -- Neon Docs]() Topics: [Amazon S3](), [Amazon Web Services](), [AWS IAM](), [GitHub Actions](), [OpenID connect (OIDC)](), [Authorization]() Tags: [aws](), [aws-s3](), [backups](), [github-actions](), [iam](), [oidc](), [openid-connect](), [postgres](), [product](), [s3](), [security]() ### AI overview A tutorial explaining how to configure AWS for storing Postgres backups in an S3 bucket as part of an automated backup workflow for multiple Neon projects. It covers creating a GitHub OIDC identity provider, an IAM role with temporary credentials, and an S3 bucket with an appropriate bucket policy. ### Source excerpt In this post, I'll walk you through setting up an AWS S3 bucket to store Postgres backups. This is part 1 of a 2-part series on automating nightly backups for multiple Neon projects--a helpful approach if you're managing hundreds or even thousands of Neon projects (e.g. in multi-t... ## Behind AWS S3's Massive Scale DevFeed: [Behind AWS S3's Massive Scale]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2024-03-06T16:15:44Z Content type: article Language: en Sources: [High Scalability]() Topics: [Amazon S3](), [Amazon Web Services](), [cloud-infrastructure](), [Data Infrastructure](), [Microservices](), [Architecture & Design]() Tags: [api](), [aws](), [aws-s3](), [cloud-native](), [machine](), [microservices](), [replication](), [s3](), [service](), [storage]() ### AI overview This guest article examines how AWS S3 grew from a storage service into a massive-scale backbone for analytics, machine learning, backups, and other cloud-native data infrastructure. It outlines S3's growth, major feature milestones, and a high-level architecture involving front-end, namespace, storage, and storage-management services. ### Source excerpt This is a guest article by Stanislav Kozlovski, an Apache Kafka Committer. If you would like to connect with Stanislav, you can do so on Twitter and LinkedIn. AWS S3 is a service every engineer is familiar with. It's the service that popularized the notion of cold-storage ## Introducing Workflow History Export DevFeed: [Introducing Workflow History Export]() Original publisher: [Read original article]() Author: Nikitha Suryadevara Published: 2024-03-01T07:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Amazon S3](), [AWS CloudFormation](), [Amazon Web Services](), [Cloud](), [audit](), [Command-line interface]() Tags: [audit](), [aws](), [aws-cloudformation](), [aws-s3](), [cli](), [cloud-storage](), [export](), [preview](), [product-news](), [production](), [release](), [s3](), [workflow]() ### AI overview Temporal Cloud has introduced Workflow History Export, allowing users to export closed workflow histories by namespace to an AWS S3 bucket. The feature supports longer-term retention for compliance, auditing, and analysis and is available to all users in Public Preview for production use. ### Source excerpt Workflow History Export in Temporal Cloud allows users to export closed workflow histories on a per-namespace basis to an AWS S3 cloud storage bucket. ## Delivering a Near Real-Time Single View into Operations with a Federated Database DevFeed: [Delivering a Near Real-Time Single View into Operations with a Federated Database]() Original publisher: [Read original article]() Author: Nic Raboy Published: 2023-04-17T14:00:00Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [MongoDB](), [Tutorial](), [Amazon S3](), [Node.js](), [API]() Tags: [api](), [aws](), [aws-s3](), [data-federation](), [industries](), [mongodb](), [node-js](), [s3](), [supply-chain](), [tutorial]() ### AI overview This tutorial demonstrates how to use MongoDB Data Federation to query, transform, and aggregate data across MongoDB, AWS S3, and HTTP API endpoints. It uses customer, vendor, and transaction data to create custom operational views. ### Source excerpt So the data within your organization spans across multiple databases, database platforms, and even storage types, but you need to bring it together and make sense of the data that's dispersed. This is... The post Delivering a Near Real-Time Single View into Operations with a Federated Database appeared first on MongoDB. ## Learnings From Creating a Guest Book App DevFeed: [Learnings From Creating a Guest Book App]() Original publisher: [Read original article]() Author: Eevis Panula Published: 2025-06-11T04:30:21.524000Z Content type: article Language: en Sources: [Eevis Blog]() Topics: [web applications](), [Amazon S3](), [App](), [TypeScript](), [Usability](), [Content Management System](), [GraphQL]() Tags: [app](), [aws](), [aws-s3](), [blog-post](), [cms](), [graphql](), [learnings](), [typescript](), [web]() ### AI overview The article describes building a simple guest book web app for a one-night party, designed to let users submit photos, names, and messages without downloading an app or creating an account. It outlines the app structure and the use of NextJS, TypeScript, GraphQL, GraphCMS, and AWS S3, then introduces lessons from the implementation, including difficulties uploading photos to S3. ### Source excerpt So it seems my sister is currently my biggest muse for side projects. She was the reason I created Neule.art, from which you can read more in the blog post "How I created Neule.art". A while back, she asked me if I could create a guest book app for a party they had with their friends. The idea was simple - there should be a possibility to add a photo, some text, and the name(s) of the sender(s). They tried to search for a readymade app, but every one of them had some problems. I also know, from experience, that these kinds of apps can be... how to put this... Not so good usability-wise. For example, no one wants to download an app for one night to be able to take one or two pictures. No one wants to create an account for that one night (and then forget that they have it). So I wanted to try out if I could build a simple enough, non-account-needing web app. In this blog post, I won't share the code for that app. It was a bit hacky, as the app needed to work only for that one night. Aaand I was a bit in a hurry. However, I will share some learnings from creating that app. Let's first have a look at what the app was like. The Guest Book App The guest book app consisted of two pages: Login, which had this one input field for writing the password, and a page for guest book entries. In addition, there was a modal for adding a new entry. That had a form with the possibility of taking a photo (a file-type input field with accept="image/*" to open the camera) and adding names and a message. As for the tech stack, I used NextJS with TypeScript, GraphQL, and GraphCMS (at the time, they changed their name just after I finished the project to Hygraph) for CMS. As for the photos, I stored them in an AWS S3 bucket and their URL to the CMS. I've worked with all the other technologies before, but AWS was something new. However, I was pretty sure that because it's a fairly common use case to store things in AWS S3 buckets, and as React is so popular, there must be an easy solution fo ## Replaying events using AWS S3 DevFeed: [Replaying events using AWS S3]() Original publisher: [Read original article]() Author: Marko Savic Published: 2022-04-08T08:40:24Z Content type: tutorial Language: en Sources: [Homegate Engineering Blog - Medium]() Topics: [Amazon S3](), [event driven](), [Microservices](), [Amazon Web Services](), [AWS CloudFormation](), [Serverless]() Tags: [aws](), [aws-lambda](), [aws-s3](), [cloudformation](), [debugging](), [event-driven](), [event-sourcing](), [lambda](), [microservices](), [s3](), [serverless](), [sns](), [software-architecture](), [versioning]() ### AI overview This tutorial describes replaying failed events in serverless, event-driven microservices by storing AWS Step Functions events in versioned Amazon S3 objects. It also outlines using a dead-letter queue and Lambda to retrieve the latest event and trigger the workflow again. ### Source excerpt In the world of event-driven microservices one of the important tasks is to have a mechanism to recover from failed events, as well as to have possibility to replay certain events. We at Homegate deal with serverless microservices. If, at any of these services, we encounter a failure during processing of an resource, we might need to recreate the last event or series of events for given resource. Usecase The central part of ingestion process is AWS StepFunctions that lives in a, so called, importer service. It is responsible for various enrichments and conditional actions on resource. The last step of the step function is sending an event to the AWS SNS which is consumed by many downstream services as you can see in the following diagram. Step functions as central part of ingestion process Here, many open questions can be identified: How to deal with failure within AWS StepFunctions? How to recreate SNS event if needed for downstream system? Would it be possible to replay the whole step function? Solution Possible answer to these questions would be to store events that goes through AWS StepFunctions into some kind of event storage, in Homegate case, we chose AWS S3. In order to support the whole history of events for given resource, S3 bucket versioning is used. This is quite handy for replaying series of events or even for debugging purposes. Here is a sample CloudFormation which enables that functionality: EventLogBucket: Type: 'AWS::S3::Bucket' Properties: BucketName: 'sample-unnique-bucket-name' AccessControl: Private PublicAccessBlockConfiguration: BlockPublicAcls: true BlockPublicPolicy: true IgnorePublicAcls: true RestrictPublicBuckets: true Tags: - Key: service Value: sample-service - Key: env Value: !FindInMap [ Stages, !Ref Stage , Env ] VersioningConfiguration: Status: Enabled That way we can replay the whole step functions flow using last logged event for given resource. That functionality could be available from different places, like DLQ or via api end ## Плагин Big Data Tools теперь поддерживает IntelliJ IDEA Ultimate, PyCharm Professional, DataGrip 2021.3 EAP и DataSpell DevFeed: [Плагин Big Data Tools теперь поддерживает IntelliJ IDEA Ultimate, PyCharm Professional, DataGrip 2021.3 EAP и DataSpell]() Original publisher: [Read original article]() Author: olegchir (JetBrains) Published: 2021-09-28T06:17:21Z Content type: release Language: ru Sources: [JetBrains RU]() Topics: [Apache Spark](), [ide](), [IntelliJ IDEA](), [PyCharm](), [Data Science](), [Amazon S3](), [Kafka](), [Python]() Tags: [amazon](), [apache-kafka](), [apache-spark](), [apache-zeppelin](), [aws](), [aws-s3](), [configuration](), [hadoop](), [ide](), [intellij-idea](), [kafka](), [pycharm](), [python](), [s3](), [spark](), [zeppelin]() ### AI overview JetBrains released a new Big Data Tools plugin build compatible with IntelliJ IDEA Ultimate and PyCharm Professional 2021.3, with planned support for DataGrip 2021.3 EAP and support for running in DataSpell. The update adds features including Spark Submit run configurations, Kafka monitoring, AWS S3 named profiles, Zeppelin notebook search, and Python interpreter selection. ### Source excerpt Недавно мы выпустили новую сборку плагина Big Data Tools, совместимую со свежими (2021.3) версиями IntelliJ IDEA Ultimate и PyCharm Professional. Когда в октябре выйдет DataGrip 2021.3, эта сборка тоже будет с ним работать. Более того, теперь мы умеем запускаться в DataSpell -- новой IDE для Data Science. Если вы используете старые версии Big Data Tools, сейчас самое время обновиться и попробовать новую версию плагина вместе со свежей версией IDE! В этом году мы много чего улучшили и добавили совершенно новые фичи (например, запуск Spark Submit в виде Run Configuration). Вот небольшой список изменений за этот год. Этот список -- лишь небольшая капля в море того, что изменилось с прошлого года. Читать далее ## Анонсируем версию Datalore Enterprise для команд Data Science DevFeed: [Анонсируем версию Datalore Enterprise для команд Data Science]() Original publisher: [Read original article]() Author: alenaguzharina (JetBrains) Published: 2021-07-06T15:00:01Z Content type: release Language: ru Sources: [JetBrains RU]() Topics: [Data Science](), [jupyter](), [Python](), [Amazon S3]() Tags: [aws](), [aws-s3](), [data-science](), [datalore](), [jetbrains](), [jupyter](), [jupyter-notebooks](), [jupyterhub](), [jupyterlab](), [python](), [tag-055aee430837](), [tag-752bfc3f8269](), [tag-baf5012a40ff]() ### AI overview JetBrains announces Datalore Enterprise, a version of Datalore designed for Data Science teams. It adds team-oriented capabilities to Jupyter notebooks, including PyCharm code assistance, persistent data storage, authentication, configurable environments, compute-resource management, versioning, real-time collaboration, and AWS S3 integration. ### Source excerpt Привет, Хабр! Jupyter-ноутбуки хоть и вызывают споры, однако все равно являются самым часто используемым инструментом для решения задач Data Science. Ноутбуки просты в личном использовании. Но если вы когда-либо пробовали организовать командную работу с ними, то наверняка сталкивались со сложностями. Именно поэтому мы в JetBrains решили выпустить специальную версию Datalore Enterprise для команд Data Science! Читать далее ## Setting Up AWS S3 Buckets + CloudFront CDN for your Assets DevFeed: [Setting Up AWS S3 Buckets + CloudFront CDN for your Assets]() Original publisher: [Read original article]() Author: andrew@nystudio107.com (Andrew Welch) Published: 2020-01-23T23:01:00Z Content type: tutorial Language: en Sources: [nystudio107 | Articles on modern web development.]() Topics: [Amazon S3](), [Amazon Web Services](), [Cloud](), [Content Management System](), [cloud-infrastructure](), [Caching](), [Website]() Tags: [assets](), [aws](), [aws-s3](), [cdn](), [cloud](), [cloud-storage](), [cloudfront](), [cms](), [convenient](), [distribution](), [here-s](), [how-to](), [inexpensive](), [insights](), [latency](), [like](), [right](), [s3](), [storage](), [store](), [using]() ### AI overview A tutorial on storing website and CMS assets in Amazon S3 and serving them through CloudFront. It explains the benefits of cloud storage, recommends using CloudFront rather than serving files directly from S3 buckets, and describes how cached assets are delivered from nearby edge locations. ### Source excerpt Using a cloud storage system like AWS S3 with a CDN distribution can be a convenient and inexpensive way to store your assets. Here's how to set it up right. ## Dynamic image resizing with Node.js and the Serverless Framework DevFeed: [Dynamic image resizing with Node.js and the Serverless Framework]() Original publisher: [Read original article]() Author: Nick Gottlieb Published: 2019-03-14T00:00:00Z Content type: tutorial Language: en Sources: [Serverless Blog]() Topics: [serverless framework](), [Serverless](), [Node.js](), [Amazon S3](), [API](), [Amazon Web Services]() Tags: [api](), [aws](), [aws-lambda](), [aws-s3](), [cloud-computing](), [engineering](), [faas](), [function-as-a-service](), [how-to](), [image](), [images](), [infrastructure-as-code](), [lambda](), [node-js](), [serverless](), [serverless-architecture](), [serverless-framework]() ### AI overview A tutorial on creating an API for dynamically resizing images with Node.js, AWS, Amazon S3, Lambda, and the Serverless Framework. ### Source excerpt Learn how to create an API that allows you to resize images dynamically using AWS, S3, Lambda & Serverless Framework. ## Teleport 2.6 Released DevFeed: [Teleport 2.6 Released]() Original publisher: [Read original article]() Author: ev@goteleport.com (Ev Kontsevoy) Published: 2018-05-31T00:00:00Z Content type: release Language: en Sources: [Teleport]() Topics: [OpenSSH](), [Security](), [Amazon S3](), [DynamoDB](), [Amazon Web Services](), [certificates](), [Authentication](), [audit]() Tags: [audit](), [authentication](), [aws](), [aws-s3](), [certificates](), [dynamodb](), [openssh](), [release](), [security]() ### AI overview Teleport 2.6 is a major release for the privileged SSH access manager. It adds DynamoDB audit-log storage, AWS S3 session-recording storage, certificate authority rotation, and PAM integration. The release source code also underwent a Cure53 security audit, whose report was expected to be published publicly. ### Source excerpt Announcing Teleport v2.6, a major release with important new features whose source code also passed a security audit performed by Cure53.