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Data warehousing projects benefit from cloud and Hadoop

SN blogs: This week, analysts discuss a new way to network a campus LAN and if ACI and NSX can work well together.

Enterprise Strategy Group analyst Nik Rouda says Teradata needs to broaden its base if it wants to remain a go-to vendor for data warehousing. Rouda says the changing market means the company needs to focus its efforts on big data and the cloud. Some progress has been made. Rouda says the Teradata cloud has customers living in two data centers and the company is aiming to get more midmarket customers who normally wouldn't pay higher prices for Teradata services. Teradata's move to add Hadoop support to its data warehousing projects should also pay dividends. Rouda says this is the obvious and most economical move because of Hadoop's ability to analyze large volumes of data. Bottom line: Rouda says Teradata is proving to be successful at reinventing itself to remain a major player in a rapidly changing market.

Read more about what Teradata is doing to enhance its big data and cloud technology capabilities.

For network agility, Cisco ACI versus VMware NSX

Gartner analyst Andrew Lerner says that one of the questions he gets asked a lot by clients is how to choose between Cisco's Application Centric Infrastructure (ACI) and VMware NSX. The answer isn't straightforward. Each approach is different, with ACI an Ethernet fabric that integrates hardware and software and NSX a network overlay. Lerner says that even though each approach reflects a fundamental networking strategy, when discussing which vendor to use, it's important to talk to more than just the networking team. Anyone involved in the operational process should be included in that discussion. Lerner says Gartner has received a growing number of queries from clients wondering if it is possible to use both Cisco ACI and NSX. It's an expensive option, but because NSX can run over any hardware, it is possible.

Read more about how Lerner says to choose between Cisco ACI and VMware NSX to increase data center network agility.

Hybrid data ecosystem boosts information management strategies

Enterprise Management Associates blogger John Myers details the elements of a hybrid data ecosystem (HDE) and how enterprises can use the framework to support big data initiatives. An HDE, Myers says, spans data management technologies like Hadoop as well as operational support systems such as customer relationship management (CRM) and enterprise resource planning (ERP). The idea is that there are different platforms available for different business and technical requirements. Key to making an HDE effective is the information management layer that links all of the services available. "For example," he writes, "Organizations that are combining multi-structured data from a Hadoop environment with curated data from a data warehouse environment will need to decide when and how to combine the data between those two platforms to provide the business context critical to making that information valuable from either an analytical or operational perspective."

Read more about how Myers says a hybrid data ecosystem is beneficial to enterprise information management initiatives.

Gigabit PON: an alternative to the campus LAN

Packet Pushers blogger Andy Burridge says that after years of implementing standard Cisco campus LAN designs, he came across a new approach that is a little less restrictive. The approach relies on Gigabit passive optical networking (GPON). The technology uses passive optical splitters to divide a single optical connection to serve multiple endpoints. Using splitters creates multiple connections, but it doesn't require power that switches do. One splitter can offer 32 connections. Each connection has an endpoint device on it that can be connected to Ethernet. Burridge saw this implemented in a hotel setting where 1,500 rooms needed to be connected. While this approach isn't ideal for all situations, it is a good option to keep in mind.

Read more of how Burridge implemented a GPON design.

Next Steps

Big data analytics needs to be reassessed

Transitioning away from technology silos

This was last published in June 2015

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