![]() In many cases, however, the data collected and produced by an organization can itself be packaged and offered as a product to other organizations. Many analytical use cases focus on extracting value from data collected and produced by an organization to serve the organization’s internal business and operational goals. Now let’s explore some of the most popular use cases that have traditionally required high resiliency or have come to require high resiliency in the modern data-driven organization. Part two of this series (coming soon) provides a deeper look into the individual high resiliency and availability features of Amazon Redshift. In particular, we discuss the Lake House Architecture in the high resiliency context. In the final section of this post, we expand the scope to discuss high resiliency in a data ecosystem that uses Amazon Redshift. In the following section, we include a brief mention of some of the complimentary high resiliency features in Amazon Redshift as they apply for each use case. For each use case, we provide a brief description and explore the reasons for its critical business profile, and provide a reference architecture for implementing the use case following best practices. The goal of this post is to show the art of the possible with high resiliency use cases. In this post, we discuss a diverse set of popular analytical use cases that have traditionally or perhaps more recently assumed a critical business profile. This post is part one of a series discussing high resiliency and availability with Amazon Redshift. Machine learning (ML) use cases that relied on overnight batch jobs to extract customer churn predictions from extremely large datasets are now expected to perform those same customer churn predictions on demand using both historical and intraday datasets. For example, analytical use cases that once relied solely on historical data and produced static forecasts are now expected to continuously weave real-time streaming and operational data into their ever-updating analytical forecasts. Those use cases are now required to be highly resilient with little to no downtime. In the modern data-driven organization, many data analytics use cases using Amazon Redshift have increasingly evolved to assume a critical business profile. ![]() This post explores different architectures and use cases that focus on maximizing data availability, using Amazon Redshift as the core data warehouse platform. ![]() Amazon Redshift is the most popular and fastest cloud data warehouse, offering seamless integration with your data lake and other data sources, up to three times faster performance than any other cloud data warehouse, automated maintenance, separation of storage and compute, and up to 75% lower cost than any other cloud data warehouse. ![]()
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