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Uber’s Big Data Platform: 100+ Petabytes with Minute Latency

Uber Engineering

To accomplish this, Uber relies heavily on making data-driven decisions at every level, from forecasting rider demand during high traffic events to identifying and addressing bottlenecks … The post Uber’s Big Data Platform: 100+ Petabytes with Minute Latency appeared first on Uber Engineering Blog.

Big Data 109
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Faster time to value with enhanced handling of OneAgent runtime data

Dynatrace

This blog post highlights a group of improvements that were released with OneAgent version 1.199. Recent improvements in OneAgent runtime-data handling. Storage mount points in a system might be larger or smaller, local or remote, with high or low latency, and various speeds. Customizable location of large runtime files.

Storage 146
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Build systems more reliably with Dynatrace: Chaos Engineering

Dynatrace

The previous blog post in this series discussed the benefits of implementing early observability and orchestration of the CI/CD pipeline using Dynatrace. This blog post explores the Reliability metric , which measures modern operational practices. While it is powerful, it presents several challenges that affect its adoption.

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Improved Alerting with Atlas Streaming Eval

The Netflix TechBlog

Atlas is an in-memory time-series database that ingests multiple billions of time-series per day and retains the last two weeks of data. To serve the increasing number of push down queries, the in-memory storage layer would need to scale up as well, and it became clear that this would push the already expensive storage costs far higher.

Storage 291
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Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

This blog series will examine the tools, techniques, and strategies we have utilized to achieve this goal. It can happen on an edge API system servicing customer devices, between the edge and mid-tier services, or from mid-tiers to data stores.

Traffic 342
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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

This blog post lists the important database metrics to monitor. Key Takeaways Critical performance indicators such as latency, CPU usage, memory utilization, hit rate, and number of connected clients/slaves/evictions must be monitored to maintain Redis’s high throughput and low latency capabilities.

Metrics 130
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Reducing Your Database Hosting Costs: DigitalOcean vs. AWS vs. Azure

Scalegrid

Since database hosting is more dependent on memory (RAM) than storage, we are going to compare various instance sizes ranging from just 1GB of RAM up to 64GB of RAM so you can see how costs vary across different application workloads. Does it affect latency? Yes, you can see an increase in latency. EC2 instances. VM instances.

Azure 344