Data Lake vs Data Warehouse: The New “Rule of Thumb” in Insurance Claims Management

on Jun 11, 2019 11:48:19 AM | By Jon Walkenford | Data Lake Data Warehouse Modernization
Data Lake vs Data Warehouse? In our experience, people often confuse the two approaches to data management. They either conflate them or misunderstand how they are alike, yet different. Rather than deal in the abstract, we thought it would make sense to have the data lakes vs. data warehouses discussion using the example of data analytics in insurance claims management. It’s a category we know well.
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Benefits of a Cloud Data Warehouse

on May 2, 2019 10:39:34 AM | By Ali Sajanlal | Cloud Data Warehouse Modernization
The data warehouse (DW) has proven to be the main ingredient for business intelligence (BI) and advanced analytics. Its very success, though, has led to a situation where companies are constantly struggling to meet Service Level Agreements (SLA), and scale and adapt to additional, large internal and external data sources. Just running a DW on-premise has become costly and requires constant maintenance and monitoring, despite the business advantages of having an integrated view of data across the enterprise. The cloud data warehouse emerges as an attractive alternative. The cloud data warehouse is easier to manage, scale and modify. It allows the organization to focus on data and business value instead of maintenance and performance. However, not all cloud data warehouses are equal.
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The Data Model in The Era of Snowflake Computing’s Data Warehouse: SMP and MPP

on Mar 5, 2019 7:48:00 AM | By Don Gooldy | Data Warehouse Modernization
Three-Part Series: Article 3 In the first article of this series I questioned whether the continued comparison of Relational Models to Star Schema models is still relevant with the entrance of Snowflake Computing’s data warehouse/data lake service into the market place. And we began by exploring the origins of the relational model.
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The Data Model in The Era of Snowflake Computing’s Data Warehouse: Business Architecture and Star Schema

on Feb 28, 2019 7:48:00 AM | By Don Gooldy | Data Warehouse Modernization
Three-Part Series: Article 2 In the prior article I questioned whether the continued comparison of Relational models to Star Schema models is still relevant with the advent of Snowflake Computing’s cloud-built data warehouse. We began to explore the origins of the relational model, based on Codd’s recognition that database change anomalies would be eliminated if database tables mirrored individual business functions that data describe.
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The Data Model in The Era of Snowflake Computing’s Data Warehouse: 3NF vs Star Schema

on Feb 27, 2019 8:53:34 AM | By Don Gooldy | Data Warehouse Modernization
Three-Part Series: Article 1 A colleague recently asked me to author an article on the differences between a “3NF” model and a star schema model on Snowflake Computing’s cloud-built data warehouse . We should instead examine whether this comparison is still relevant, with Snowflake’s entry into the data warehouse and analytics market.
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For Healthcare Organizations, a Modern Data Architecture Is a Necessity

on Dec 12, 2018 8:06:52 AM | By Robert Fuller | Healthcare Data Warehouse Modernization
Healthcare Partner Bob Fuller and Healthcare Chief Technology Officer Ramu Kalvakuntla make a compelling argument for modern data architectures for healthcare organizations. Not sure what those are? They explain that, too, on the AHIMA Data Revolution blog.
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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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What does real-time stream processing mean for big data?

In the pursuit of advanced analytics maturity, some organizations focus a little too much on the "big" aspect of big data. Certainly, big data analytics requires the collection of vast amounts of information, but that's only one part of the equation. 
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