Tag Archives: CPU

Hitting the Batch Wall, Part 2: Hardware Scaling

This is the second installment of my multi-part blog series on “hitting the batch wall.” Well, it’s not so much about hitting the batch wall, but what you can do to avoid hitting the wall. Today’s topic is “throwing hardware” at the problem (a.k.a. hardware scaling). I’ll discuss the common approaches and the tradeoffs of hardware scaling with Informatica software.

Before I can begin to discuss hardware scaling, I start with this warning: faster hardware only improves the load window situation when it resolves a bottleneck. Data integration jobs are a lot like rush hour traffic, they can only run as fast as the slowest component. It doesn’t make any sense to buy a Ferrari if you will always be driving behind a garbage truck. In other words, if your ETL jobs are constrained by the source/target systems or I/O or even just memory, then faster/more CPUs will rarely improve the situation. Understand your bottlenecks before you start throwing hardware at them! (more…)

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Posted in Data Integration | Tagged , , , , , | 2 Comments

Managing Your SAP BW Growth With a Nearline Strategy

SAP’s data warehouse solution (SAP BW) provides enterprises the ability to easily build a warehouse over their existing operational systems with pre-defined extraction and reporting objects and methods. Data that is loaded into SAP BW is stored in a layered architecture which encourages reusability of data throughout the system in a standardized way. SAP’s implementation also enables easy audits of data delivery mechanisms that are used to produce various reports within the system.

To allow enterprises to achieve this level of standardization and auditability, SAP BW must persistently store large amounts of data within different layers of their architecture. Managing the size of the objects within these layers will become increasingly important as the system grows to insure high levels of performance for end-user queries and data delivery. (more…)

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Simplistic Approaches to Data Federation Solve (Only) Part of the Puzzle – We Need Data Virtualization (Part 2 of 3)

In my last post, I introduced the concept of data federation, which for now I would like to differentiate from data virtualization – a term that I’ll bring into focus in a bit. But first, we explored two issues: data accessibility and data latency. Within recent times, the sophistication of data accessibility services has matured greatly, to the point where one can somewhat abstract those accessibility services from the downstream consumer (or “reuser”) of data. (more…)

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IPC Performance Report Shows Ultra-low Latencies Of 660 Nanoseconds

Experts agree that in the future CPU scaling will go out, not up. Clock cycles seem to have plateaued around 3 Gigahertz, and while five years ago people got excited about dual-core machines, today the major commodity CPU manufacturers deliver 4, 6 and 8-core processors and system vendors link these CPUs together in multi-socket servers, as Cisco does in their UCS line.

Our Ultra Messaging customers - financial players like exchanges and investment banks – are always interested in gaining every possible performance advantage in the ultra competitive world of high-frequency electronic trading. (more…)

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Posted in Customers, Data Integration, Financial Services, News & Announcements, Partners, Ultra Messaging, Vertical | Tagged , , , , , , | Leave a comment