Top 3 Modern Data Architecture Mistakes

Transforming legacy data warehouse architecture to support real-time data processing is a massive undertaking, and it's not unusual for modernization efforts to hit some bumps along the way. To make creating a modern data architecture easier, learn from others' mistakes. Here are three of the most common mistakes companies make with their data warehouse modernization efforts:
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What does a successful modern data architecture look like?

The promise of advanced analytics capabilities is incredibly tantalizing, but actually reaching that level of sophistication can be an uphill battle. So much focus goes into acquiring analytics tools and data science expertise that it's easy for organizations to overlook a critical component in the equation: the actual architectural foundation.
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How to Choose the Right Data Visualization Tools

on Jun 29, 2017 8:15:00 AM | By Clarity Insights | Data Visualization data architecture
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Data & Analytics Programs: Part 2-Getting Strategic About Your Agile

The agile method of software development has helped organizations improve software quality and team productivity. This blog series is geared towards helping you make agile work for your data and analytics initiatives.
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Decoupling Big Data Compute and Storage

on Dec 1, 2016 3:42:07 PM | By Tripp Smith | Hadoop Big Data data architecture Analytics SQL
Once upon a time, if you wanted a multipurpose platform for analytics, SQL databases were the way to go. This was doubly true for big data, where volumes swelled beyond the capacity of a single machine to handle analytics on such a scale. Big data was simply too big to pass across the network, and data proximity was a fundamental assumption for MPP architecture. The concept of randomized distribution without consideration for the most common access paths was unthinkable.
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Data Architecture Practices: Aligning Business With Data Strategy

In our previous post we explained the need to drive greater business clarity in business data and information capabilities. Here we describe our own practices so you can see if your data architecture practice is truly business focused—or if your organization is merely creating “boxes” to put your data in.
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