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.

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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.

To a large degree, the phenomenal performance and interactive analysis capabilities of Big Data projects are enabled by in memory computing. In-memory databases have become the foundation of a new generation of business applications that bring the power of analytics to the hands of decision makers. Continue reading

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Dynamic Data Bucketing: A Use Case for Complex Aggregation

Fast & Dynamic Data BucketingComplex aggregation has become a common requirement for business users looking to analyze sophisticated metrics across multiple dimensions. There are numerous use cases for complex aggregation such as cross-currency aggregation, which was explored in our last post. Dynamic bucketing is another use case example.

This blog takes a deep look at the technical considerations for a successful implementation of dynamic bucketing.

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Real-Time Cross-Currency Analytics: A Use Case for Complex Aggregation

SQL vs Post Processors: Currency Aggregation ExampleIn previous posts, we’ve delved into the principles of multidimensional databases. Among all the benefits that a multidimensional database delivers is complex aggregation, a process by which KPIs are written once and are immediately available across any dimensions, through any filtering, letting the user follow his train of thought.

But how does complex aggregation actually work? This post explores a concrete use case, articulates the technology challenges behind complex aggregation and demonstrates why ETL and SQL relational databases are not a fit.

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Why Complex Event Processing (CEP) is not enough for real-time decision making

Real-time decision making is commonly linked with Complex Event Processing (CEP). Indeed, CEP systems can extract and alert about meaningful events from streams of data. However, for decision makers to have context and turn a notification into a meaningful, actionable event, CEP must be supplemented with mixed workload and multidimensional capabilities.

Let’s take a look at what it means.

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Mixed Workload databases: A Primer

In a previous post comparing multidimensional and relational databases we mentioned that the decision making imperatives in the Big Data era were disrupting the clear-cut border between OLTP and OLAP, enabling a new type of mixed workload database that addresses both needs.

This post takes a closer look at mixed workload systems – what they are, how they work, and what are they useful for. Continue reading

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Multidimensional or relational? What’s the right system for you?

Relational and multidimensional databases differ on almost any possible dimension:  tables, columns and rows vs. cubes, measures and dimensions; queries across joint tables vs. pre-calculated aggregations across dimensions; Structured Query Language (SQL) vs. Multidimensional Expressions MDX.  And the list of goes on and on.

Nevertheless, talk to a vendor from any of these two camps, and he will argue that the system can successfully perform any task. Cross-dimensional analytics, for instance, can be performed using both types of systems. Continue reading

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Forbes highlights the scope of ActivePivot in the move to real-time risk management

Tom Groenfeldt, financial technology blogger for the internationally renowned business and finance publication, Forbes, outlines the benefits of using real-time risk management in the face of the Big Data conundrum: www.forbes.com/sites/tomgroenfeldt
/2012/12/28/quartet-helps-financial
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Comparisons are drawn between the trend monitoring capabilities of ActivePivot and the analytical approach used by intelligence agencies. Georges Bory, MD and co-founder at Quartet FS highlights the breadth of the technology: “Whether it’s Homeland Security or fraud detection or operational risk or control in a trading room, you are trying to apply statistics to huge amounts of data.” Continue reading

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CVA – a Big Data problem

In recent years regulatory demands have permeated all types of financial institutions. Banks are finding themselves under increasing scrutiny to account for their actions with never seen before speed and accuracy. The likes of Dodd Frank, Basel III, and EMIR are necessitating an increasing need to assess, on a pre-trade basis, the credit impact resulting from new OTC transactions. Continue reading

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Solving the collateral optimisation challenge

The collateral world is changing, and changing fast. The transition of the derivatives market from OTC to an exchange-traded, centrally cleared environment, as framed by the Dodd-Frank Act and European Market Infrastructure Regulation (EMIR) regulatory reforms, is a game changer for all market participants – dealers, prime brokers, custodians, asset managers and hedge funds alike.
The need for financial institutions to have real-time access to their exposures, pledged collateral and collateral requirements across all asset classes and counterparties is no trivial matter. Continue reading

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