Remove 2016 Remove Database Remove Network Remove Scalability
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SQL Server 2016 – It Just Runs Faster: Always On Availability Groups Turbocharged

SQL Server According to Bob

Performance issues surrounding Availability Groups typically were related to disk I/O or network speeds. While we were confident in the design of SQL Server 2012, several customers reported to us performance problems that did not appear to be with disk subsystems, CPU, or networks. Now disk I/O and CPU capacity were no longer an issue.

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Expanding the AWS Cloud: Introducing the AWS US East (Ohio) Region

All Things Distributed

In the remainder of 2016 and inq 2017, we will launch another four AWS regions in Canada, China, the United Kingdom, and France, adding another nine AZs to our global infrastructure footprint. The Ohio Region launches with three AZs so that customers can create high-availability environments and architect for fault tolerance and scalability.

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Is MongoDB Open Source? Is Planet Earth Flat?

Percona

They came up with a horizontally scalable NoSQL database. Instead of relational (SQL) databases defined primarily through a hierarchy of related sets via tables and columns, their non-relational structure used a system of collections and documents. 2016: The company adds service-loaded MongoDB Professional to its mix.

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Expanding the Cloud – An AWS Region is coming to Hong Kong

All Things Distributed

After the launch of the AWS APAC (Hong Kong) Region, there will be 19 Availability Zones in Asia Pacific for customers to build flexible, scalable, secure, and highly available applications. As well as AWS Regions, we also have 21 AWS Edge Network Locations in Asia Pacific. In 2013, AWS opened an office in Hong Kong.

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Should You Use ClickHouse as a Main Operational Database?

Percona

What if we use ClickHouse (which is a columnar analytical database) as our main datastore? Well, typically, an analytical database is not a replacement for a transactional or key/value datastore. However, ClickHouse is super efficient for timeseries and provides “sharding” out of the box (scalability beyond one node).

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5 tips for architecting fast data applications

O'Reilly Software

Google was among the pioneers that created “web scale” architectures to analyze the massive data sets that resulted from “crawling” the web that gave birth to Apache Hadoop, MapReduce, and NoSQL databases. The data shape will dictate capacity planning, tuning of the backbone, and scalability analysis for individual components.