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5 data integration trends that will define the future of ETL in 2018

Abhishek Tiwari

There are several emerging data trends that will define the future of ETL in 2018. In 2018, we anticipate that ETL will either lose relevance or the ETL process will disintegrate and be consumed by new data architectures. Unified data management architecture. Event-driven data flow architecture.

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USENIX SREcon APAC 2022: Computing Performance: What's on the Horizon

Brendan Gregg

Make sure your system can handle next-generation DRAM,” [link] Nov 2011 - [Hruska 12] Joel Hruska, “The future of CPU scaling: Exploring options on the cutting edge,” [link] Feb 2012 - [Gregg 13] Brendan Gregg, “Blazing Performance with Flame Graphs,” [link] 2013 - [Shimpi 13] Anand Lal Shimpi, “Seagate to Ship 5TB HDD in 2014 using Shingled Magnetic (..)

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Aurora vs RDS: How to Choose the Right AWS Database Solution

Percona

This post was originally published in July 2018 and was updated in July 2023. Understanding DBaaS DBaaS cloud services allow users to use databases without configuring physical hardware and infrastructure or installing software. What are the differences between Aurora and RDS? How do I choose which one to use?

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USENIX SREcon APAC 2022: Computing Performance: What's on the Horizon

Brendan Gregg

Make sure your system can handle next-generation DRAM,” [link] , Nov 2011 [Hruska 12] Joel Hruska, “The future of CPU scaling: Exploring options on the cutting edge,” [link] , Feb 2012 [Gregg 13] Brendan Gregg, “Blazing Performance with Flame Graphs,” [link] , 2013 [Shimpi 13] Anand Lal Shimpi, “Seagate to Ship 5TB HDD in 2014 using Shingled Magnetic (..)

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HammerDB MySQL and MariaDB Best Practice for Performance and Scalability

HammerDB

As is also the case this limitation is at the database level (especially the storage engine) rather than the hardware level. InnoDB is the storage engine that will deliver the best OLTP throughput and should be chosen for this test. . driver: intel_pstate CPUs which run at the same hardware frequency: 0 .

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Kubernetes for Big Data Workloads

Abhishek Tiwari

In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. faster access to external storage and data locality (I/O, bandwidth). For instance, scatter/gather pattern can be used to implement a MapReduce like batch processing architecture on top of Kubernetes. Storage provisioning.

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The Performance Inequality Gap, 2021

Alex Russell

A then-representative $200USD device had 4-8 slow (in-order, low-cache) cores, ~2GiB of RAM, and relatively slow MLC NAND flash storage. India became a 4G-centric market sometime in 2018. Hardware Past As Performance Prologue. But the hardware future is not evenly distributed, and web workloads aren't heavily parallel.