SEC expands data warehousing capability
WASHINGTON, DC -- The US Securities and Exchange Commission (SEC) has expanded its use of a database geared for data warehouses so that its investigations can continue in the event of disasters, officials say.
The SEC sought to explore the disaster recovery capabilities of its data warehouse for its Division of Enforcement "because of federal requirements regarding business continuity concerns", says Lewis Walker, associate director at SEC headquarters in Washington, DC. The division, which investigates securities firms, "needs to access the data even if there is a disruption in service", Walker says.
To better access securities trading data, the SEC division has been using Sybase Intelligent Query
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact info@risk.net or view our subscription options here: http://subscriptions.risk.net/subscribe
You are currently unable to print this content. Please contact info@risk.net to find out more.
You are currently unable to copy this content. Please contact info@risk.net to find out more.
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@risk.net
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@risk.net
More on Technology
FX options: rising activity puts post-trade in focus
A surge in electronic FX options trading is among the factors fuelling demand for efficiencies across the entire trade lifecycle, says OSTTRA’s commercial lead, FX and securities
Dismantling the zeal and the hype: the real GenAI use cases in risk management
Chartis explores the advantages and drawbacks of GenAI applications in risk management – firmly within the well-established and continuously evolving AI landscape
Chartis RiskTech100® 2024
The latest iteration of the Chartis RiskTech100®, a comprehensive independent study of the world’s major players in risk and compliance technology, is acknowledged as the go-to for clear, accurate analysis of the risk technology marketplace. With its…
T+1: complacency before the storm?
This paper, created by WatersTechnology in association with Gresham Technologies, outlines what the move to T+1 (next-day settlement) of broker/dealer-executed trades in the US and Canadian markets means for buy-side and sell-side firms
Empowering risk management with AI
This webinar explores how artificial intelligence (AI) can strip out the overheads and effort of rapidly modelling, monitoring and mitigating risk
Core-Payments for business leaders: why real-time access to payment data is key to long‑term business success
Business leaders require easy access to timely, reliable and complete information across post-trade processes. Aside from the usual requirements of senior managers to optimise for risk, revenues and costs, they increasingly need to demonstrate to their…
Risk applications and the cloud: driving better value and performance from key risk management architecture
Today's financial services organisations are increasingly looking to move their financial risk management applications to the cloud. But, according to a recent survey by Risk.net and SS&C Algorithmics, many risk professionals believe there is room for…
Machine learning models: the validation challenge
Machine learning models are seeing increasing demand across the capital markets spectrum. But how can firms improve their chances of gaining internal and regulatory approval for these type of models?
