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Optimizing data warehouse storage

The Netflix TechBlog

At this scale, we can gain a significant amount of performance and cost benefits by optimizing the storage layout (records, objects, partitions) as the data lands into our warehouse. We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits.

Storage 203
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Netflix Cloud Packaging in the Terabyte Era

The Netflix TechBlog

Our previous tech blog Packaging award-winning shows with award-winning technology detailed our packaging technology deployed on the streaming side. From chunk encoding to assembly and packaging, the result of each previous processing step must be uploaded to cloud storage and then downloaded by the next processing step.

Cloud 237
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Mayastor: Lightning Fast Storage for Kubernetes

Percona Community

In this blog post we’re going to see those technologies at work to give us awesome block storage performance with flexibility and simple operations. It’s a new generation in storage software, designed for super high speed low latency NVMe devices. Why is SPDK exciting?

Storage 52
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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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Compression Methods in MongoDB: Snappy vs. Zstd

Percona

Compression in any database is necessary as it has many advantages, like storage reduction, data transmission time, etc. Storage reduction alone results in significant cost savings, and we can save more data in the same space. In this blog, we will discuss both data and network-level compression offered in MongoDB.

Storage 108
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Data ingestion pipeline with Operation Management

The Netflix TechBlog

These media focused machine learning algorithms as well as other teams generate a lot of data from the media files, which we described in our previous blog , are stored as annotations in Marken. But we cannot search or present low latency retrievals from files Etc. We refer the reader to our previous blog article for details.

Media 264
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Consistent caching mechanism in Titus Gateway

The Netflix TechBlog

This blog post presents how our current iteration of Titus deals with high API call volumes by scaling out horizontally. When a new leader is elected it loads all data from external storage. We started seeing increased response latencies and leader servers running at dangerously high utilization.

Cache 224