Author Archives: Antoine Chambille

The 10 best cloud instances for In-Memory Computing

The best cloud instancesA list of the 10 best public cloud instances to operate on-demand in-memory computing as of December 2017 and some tips to get the most out of themRead more

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ActivePivot on Microsoft Azure Ms-Series Instances with 2TB of memory

Parallel LoadingMicrosoft Azure now offers instances with 2TB of memory and 128 cores – what it means for resource-intensive applications in financeRead more

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ActiveViam Webinar – April 26 2017: “Cloud deployment and scale-out architecture in ActivePivot 5.5”

ActivePivot CloudOn April, 26 we will host two 30-minute live webinars where we will unveil two key new innovations introduced in the latest version of ActivePivot.Read more

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Advanced In-Memory Analytics in Azure with the ActiveViam platform

ActivePivot & AzureThis blog explores new possibilities for in-memory analytics in Azure. We fire on demand a cluster of 128 G5 instances and load in memory a 40TiB dataset, right from blob storage. The ActiveViam analytical platform puts those 4,096 cores and 60TiB of RAM into the hands of business users.Read more

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Partnering with Atos to build a Big Data appliance

Quartet FS & AtosQuartet FS and Atos have created a big data appliance designed to tackle the present and future regulatory challenges banks face. Read blog post.Read more

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How to make historical analysis work on real-time data

‘As of’ root cause analysis at any point in the pastHistorical data analysis is typically enabled using data duplication technologies. But is this method still valid today when users need to analyze historical data that’s moving fast and changing rapidly throughout the day? All we know is that in ActivePivot, we practically had to re-invent our core database to support the requirements of our customers who wanted to travel back in time and analyze large volumes of dynamic data.Read more

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From Multi-Core to Many-Core: Do not delay in Making Your Java Application “NUMA-Aware”

NUMA Aware ApplicationIn the last post, I explained the difference between SMP and NUMA architectures as we enter the “many-core” era. I also asked the following question: “Is it reasonable to expect massive performance improvements when you run an existing application on new NUMA-enabled hardware?” The answer is yes. However, improved performance is not guaranteed and you must be prepared to rewrite the code of your application to get the best out of many-core hardware. Read more

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To NUMA or not to NUMA

Symmetric Multi-Processing (SMP)When your business is analyzing big data with the goal of providing answers in split seconds, you find yourself trying to squeeze every bit of speed into your solution. Among other things, this also includes finding the optimal processor architecture. This is why we’ve spent quite a lot of efforts studying the memory architecture alternatives – NUMA (non-uniform memory access) and SMP (symmetric multiprocessing) – to see which one could provide us with the best results.Read more

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In-memory analytics: doing things radically different

Think differentIn our previous post, How In-Memory Computing is Accelerating Business Performance, we explained the disruptive potential of in-memory computing. Performance gains resulting from faster execution of queries were one of the top benefits mentioned. However, in-memory computing goes way beyond performance gains, allowing organizations to do things differently and achieve new levels of competitiveness. This post illustrates this with a few examples.Read more

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From 24 hours to 24 seconds – How In-Memory Computing is Accelerating Business Performance

simulationsCountless articles are written about Big Data every day. Beyond the hype, the Big Data phenomenon is a real change agent delivering capabilities that were never thought of before. Financial institutes and banks, for example, can calculate and asses their risk in near-real time, throughout the day.Read more

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