惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

V
V2EX
J
Java Code Geeks
月光博客
月光博客
博客园_首页
The GitHub Blog
The GitHub Blog
Vercel News
Vercel News
B
Blog RSS Feed
博客园 - 聂微东
宝玉的分享
宝玉的分享
T
Tailwind CSS Blog
Jina AI
Jina AI
S
SegmentFault 最新的问题
B
Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
有赞技术团队
有赞技术团队
Hugging Face - Blog
Hugging Face - Blog
Google DeepMind News
Google DeepMind News
阮一峰的网络日志
阮一峰的网络日志
The Cloudflare Blog
量子位
Martin Fowler
Martin Fowler
博客园 - Franky
大猫的无限游戏
大猫的无限游戏
博客园 - 叶小钗

Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis
Monitor and optimize S3 storage with Amazon S3 Storage Le...
Ryan Warrier, David M. Lentz · 2021-12-09 · via Datadog | The Monitor blog
Ryan Warrier

Ryan Warrier

David M. Lentz

David M. Lentz

With Amazon S3’s scalable object storage, you can store and manage billions of objects across multiple AWS accounts, regions, and storage classes. S3 Storage Lens provides 29 useful metrics that give you deeper visibility into your S3 usage and activity across your entire organization. We are proud to be a pre-integrated AWS partner using the new CloudWatch publishing option to bring S3 Storage Lens metrics into Datadog for enhanced S3 storage monitoring. In this post, we’ll show you how this integration can help you:

  • Monitor S3 storage trends alongside your other infrastructure and applications

  • Optimize S3 costs

  • Ensure that your organization is following data protection best practices

The S3 Storage Lens out-of-the-box dashboard shows an organization’s total storage, bucket count, object count, and storage breakdown.

Datadog’s out-of-the-box dashboard—shown above—lets you easily visualize and explore all S3 Storage Lens metrics, including the number of objects and bytes you’ve stored in S3 over time. Other widgets on the dashboard help you monitor your S3 cost efficiency, data protection practices, request activity, and errors. You can use the dashboard’s template variables to easily filter your metrics by AWS organization, account, region, bucket, and storage class. This granular view into your S3 Storage Lens metrics can help you understand organization-wide changes and trends in your S3 usage, costs, and security.

And you can customize your dashboard to add context to your S3 Storage Lens metrics. Once you’ve cloned the dashboard, you can add widgets to track the performance of your infrastructure and other AWS services alongside your S3 Storage Lens metrics.

Optimize S3 costs across all of your accounts

S3 Storage Lens metrics provide information about non-current object versions and delete markers, as shown in the screenshot below. These metrics represent opportunities to reduce your S3 storage costs by deleting unused objects. You’ll also see information about incomplete multipart uploads, which can produce unusable data that remains in S3 until you explicitly delete it, for example by using an S3 Lifecycle rule.

The cost efficiency section of the S3 Storage Lens dashboard shows metrics describing versioned objects, delete markers, and incomplete multipart uploads.

Datadog’s S3 Storage Lens integration also helps you understand how your organization accesses data in S3, which can be useful for further optimizing costs. AWS charges for data transfer and retrieval per request and per byte, and the costs vary across storage classes. Some retrieval methods (such as the GetObject SDK action) return an entire object, but you may be able to reduce costs by using Amazon S3 Select to query the object using SQL, allowing you to retrieve only the data you need.

The screenshot below shows an excerpt of the out-of-the-box dashboard that can help you understand your organization’s costs. For example, you can view a toplist of buckets that have the lowest data retrieval rates (indicating that they store infrequently used data), and consider moving them to a lower-cost storage class. You can also see the number of bytes scanned by S3 Select—indicating the amount of data processed by queries—and the number of bytes returned—indicating the aggregate size of all result sets. The buckets with the most bytes scanned might represent an opportunity to improve the performance and reduce the cost of your S3 Select queries by using the ScanRange parameter to scan only a subset of an object’s data.

The S3 Storage Lens dashboard shows bytes downloaded and the percentage of data retrieved, bytes scanned and retrieved by S3 Select queries.

Ensure that your S3 data is protected

AWS best practices prescribe a number of steps you should take to protect your S3 data against risks, including loss, corruption, and unauthorized changes. S3 guarantees durability to guard against data loss, but you still need to configure each bucket properly to take advantage of S3’s features for replication, versioning, and object locks.

The S3 Storage Lens dashboard provides a quick summary of the percentage of total storage that is encrypted, replicated, and protected with an object lock, as shown in the screenshot below. You can also monitor these metrics over time and even create alerts to notify you automatically if your data protection metrics drop below a threshold you specify, allowing you to quickly identify and remediate any incorrectly configured buckets or objects. For example, if you see a steady decrease in the percentage of objects with an object lock enabled, it could indicate that an application became misconfigured and began to create unprotected S3 objects. The toplists at the bottom show the buckets with the greatest risk of data loss, corruption, and exposure, which you can use to prioritize improvements to your S3 data protection.

The data protection section of the S3 Storage Lens dashboard shows the percentage of S3 data that is encrypted, replicated, and protected with an object lock.

And for continuous visibility into the protection and security of your S3 data, you can use Datadog Cloud SIEM’s out-of-the-box rules to monitor S3 permissions automatically.

Get started with S3 Storage Lens monitoring in Datadog

S3 Storage Lens complements our existing S3 integration to provide even deeper visibility into S3. To get started, configure S3 Storage Lens to publish metrics to CloudWatch, then enable the S3 Storage Lens integration. If you’re not already using Datadog, start today with a 14-day free trial.