Category Archives: Big Data Analytics

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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4 ways location-based pricing can improve retail volumes and margins

One element that is often neglected by brick & mortar retailers, for lack of sufficient analytical capabilities to take it into account, is that the competitive environment varies greatly from one store location to another. While any pricing strategy typically …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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Who wants a Real-Time Supply Chain?

Real-Time Supply ChainEveryone, it seems. Take Procter & Gamble. In a recent talk, Procter & Gamble’s SVP of Product Supply, mentioned they have created a “real-time instrumented supply chain,” which they believe could achieve an upside of 1-2% sales increase, 2-5% margin improvement, and 5-10% improvement in asset utilization.

Only several years ago companies updated their supply chain plans approximately once a month, whereas today forecasts and plans are adjusted twice a day for some product categories. Such frequent updates enable responding much faster to changing demand and allow implementing a more accurate resupply of products to stores.Read more

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How long can CCPs afford to wait?

Clearing houseTwo months ago the Basel Committee decided that banks will have to set aside less capital against trades through central clearing houses in a bid to encourage them to use their services. The aim is to make banks use the central counterparties (CCPs), making it easier for regulators to follow the flow of banks’ trades and exposures to each other.Read more

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Tackling the Look-through Challenges with In‑Memory Computing

Look-Through ApproachThis post delves into the implications of the look-through approach for asset managers, building the case for the use of in-memory aggregation technology to process massive amounts of highly granular data.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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