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Now worth $200 million, Sarah Jessica Parker credits being ‘one of eight kids that struggled financially’ for her hunger, ambition, and work ethic

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Zoomdata Adds Amazon Data Sources to Its Menu

Barb Darrow
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Barb Darrow
Barb Darrow
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Barb Darrow
By
Barb Darrow
Barb Darrow
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March 14, 2016, 8:10 AM ET
Zoomdata
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Zoomdata, which presents messy data from Hadoop and other sources in a more understandable graphical way, now supports various Amazon Web Services data sources.

The Redwood Shores, Calif., company now offers connectors that tap into Amazon Redshift data warehouse; Elastic Map Reduce (EMR); Kinesis streaming data; and Aurora relational databases. It already offers connectors to Cloudera and Hortonworks (HDP) Hadoop as well as Spark, Solr, MongoDB (MONGODB), and the usual roster of relational databases, including Oracle, MySQL, Microsoft (MSFT) SQL Server.

Since many companies that have long used Oracle (ORCL) or SQL Server relational databases, which deal with the traditional rows-and-columns of data, are also turning to nonrelational (NoSQL) databases to handle other data types, having a single tool that can tap into all those sources is of interest.

Steady Progress for Business Analytics Even in a Tough Market

It’s worth noting that Amazon (AMZN) itself already announced QuickSight, a data analysis and visualization tool for its own sets of data. What’s more interesting is that QuickSight itself is reportedly a relabeled version of Zoomdata’s technology. Zoomdata had no comment.

Customers can pay $1.91 per hour to use Zoomdata for AWS—the charge is in addition to the hourly Amazon charges, which are billed separately. Or they can pay an annual fee that includes support.

How the U.S. is Using Big Data to Innovate Policing:

Last month, Zoomdata announced a new $25 million funding round, bringing total investment to $47.2 million.

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Zoomdata competes with companies like Tableau and QliqTech (QLIK), although Zoomdata proponents says it handles a wider variety of data sources and can query all of them directly without first having to clean up the data, a job traditionally handled by extract, transform, and load (ETL) tools like Informatica (INFA).

 

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