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Web Performance Bookshelf

Rigor

Take, for example, The Web Almanac , the golden collection of Big Data combined with the collective intelligence from most of the authors listed below, brilliantly spearheaded by Google’s @rick_viscomi. Web Performance Tuning. Even Faster Websites. High Performance Mobile Web. Professional Website Performance. Still good.

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USENIX LISA 2018: CFP Now Open

Brendan Gregg

In addition to our passion for the important role that USENIX plays in our community – a vendor-neutral 501(c)(3) non-profit computing association that advances our industry with conferences, awards, inclusion initiatives, and grants – we also have a strong affection for the LISA conference community.

DevOps 43
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USENIX LISA 2018: CFP Now Open

Brendan Gregg

In addition to our passion for the important role that USENIX plays in our community – a vendor-neutral 501(c)(3) non-profit computing association that advances our industry with conferences, awards, inclusion initiatives, and grants – we also have a strong affection for the LISA conference community.

DevOps 40
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Should You Use ClickHouse as a Main Operational Database?

Percona

Currently, an issue has been opened to make the “tailing” based on the primary key much faster: slow order by primary key with small limit on big data. Text analysis. database: reddit table: rc_2017 mutation_id: mutation_858.txt ClickHouse does not offer full text search, however we can use some text functions.

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Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and…

The Netflix TechBlog

In the rest of this blog, we will a) touch on the complexity of Netflix cloud landscape, b) discuss lineage design goals, ingestion architecture and the corresponding data model, c) share the challenges we faced and the learnings we picked up along the way, and d) close it out with “what’s next” on this journey.

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Structural Evolutions in Data

O'Reilly

Each time, the underlying implementation changed a bit while still staying true to the larger phenomenon of “Analyzing Data for Fun and Profit.” ” They weren’t quite sure what this “data” substance was, but they’d convinced themselves that they had tons of it that they could monetize.