Databases/Big Data
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An Early Look At The Features Of PostgreSQL 9.6
PostgreSQL 9.6 isn't being released until later this year, but with it moving along, the release notes are starting to be assembled for this next major update to this open-source SQL server implementation.
This week added to PostgreSQL Git was the start of the 9.6 release notes. Among the prominent items to mention are the parallel query support, synchronous replication now supports multiple standby servers, full-text search for phrases, support for remote joins/sorts/updates, "substantial" performance improvements (especially for many-core servers), no more repetitive scans of old data by auto vacuum, and much more.
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Log analytics talk at Apache: Big Data
As I mentioned earlier, I’ll be talking about feature engineering and outlier detection for infrastructure log data at Apache: Big Data next week. Consider this post a virtual handout for that talk. (I’ll also be presenting another talk on scalable log data analysis later this summer. That talk is also inspired by my recent work with logs but will focus on different parts of the problem, so stay tuned if you’re interested in the domain!)As I mentioned earlier, I’ll be talking about feature engineering and outlier detection for infrastructure log data at Apache: Big Data next week. Consider this post a virtual handout for that talk. (I’ll also be presenting another talk on scalable log data analysis later this summer. That talk is also inspired by my recent work with logs but will focus on different parts of the problem, so stay tuned if you’re interested in the domain!)
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Hadoop: Can the Tortoise be a Hare?
As early as 2012, writers, industry critics, and big data companies such as Cloudera predicted Hadoop’s demise as the de facto standard for big data analytics. Hadoop’s future as a viable real-time big data analytics platform seemed questioned at the height of its hype and adoption.
And indeed, many businesses that manage large data sets have looked elsewhere to find something better to use. In the view of some, Hadoop’s complexity and management requirements make it a technology that cannot survive long-term in business.
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