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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 214
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Dynatrace Managed now available on all major cloud platforms

Dynatrace

Dynatrace Managed now available on the Google Cloud Platform. You’re then presented with the Dynatrace Managed cluster deployment page, which contains basic information about Dynatrace, the solution itself, and a link to our documentation. This will open the launcher page of the solution, as shown below. Select LAUNCH.

Cloud 222
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What is a Distributed Storage System

Scalegrid

A distributed storage system is foundational in today’s data-driven landscape, ensuring data spread over multiple servers is reliable, accessible, and manageable. Understanding distributed storage is imperative as data volumes and the need for robust storage solutions rise.

Storage 130
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Using JSONB in PostgreSQL: How to Effectively Store & Index JSON Data in PostgreSQL

Scalegrid

Note: If a particular key is always present in your document, it might make sense to store it as a first class column. JSONB storage has some drawbacks vs. traditional columns: PostreSQL does not store column statistics for JSONB columns. JSONB storage results in a larger storage footprint.

Storage 321
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Netflix’s Distributed Counter Abstraction

The Netflix TechBlog

Today, we’re excited to present the Distributed Counter Abstraction. In this context, they refer to a count very close to accurate, presented with minimal delays. Both categories share common requirements, such as high throughput and high availability. Let’s take a closer look at the structure and functionality of the API.

Latency 253
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Improved Alerting with Atlas Streaming Eval

The Netflix TechBlog

While Atlas is architected around compute & storage separation, and we could theoretically just scale the query layer to meet the increased query demand, every query, regardless of its type, has a data component that needs to be pushed down to the storage layer. This is one of the reasons it has taken us years to get here.

Storage 300
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Building an elastic query engine on disaggregated storage

The Morning Paper

Building an elastic query engine on disaggregated storage , Vuppalapati, NSDI’20. This paper presents Snowflake design and implementation along with a discussion on how recent changes in cloud infrastructure (emerging hardware, fine-grained billing, etc.) But the ephemeral storage service for intermediate data is not based on S3.

Storage 112