Remove Analytics Remove Data Architecture Remove Data Warehouse Remove Data-driven
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Migrate a petabyte-scale data warehouse from Actian Vectorwise to Amazon Redshift

AWS Big Data

Amazon Redshift is a fast, scalable, and fully managed cloud data warehouse that allows you to process and run your complex SQL analytics workloads on structured and semi-structured data.

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Empowering data-driven excellence: How the Bluestone Data Platform embraced data mesh for success

AWS Big Data

In the ever-evolving world of finance and lending, the need for real-time, reliable, and centralized data has become paramount. Bluestone , a leading financial institution, embarked on a transformative journey to modernize its data infrastructure and transition to a data-driven organization.

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5 misconceptions about cloud data warehouses

IBM Big Data Hub

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics, that enable faster decision making and insights.

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Power analytics as a service capabilities using Amazon Redshift

AWS Big Data

Analytics as a service (AaaS) is a business model that uses the cloud to deliver analytic capabilities on a subscription basis. This model provides organizations with a cost-effective, scalable, and flexible solution for building analytics. times better price-performance than other cloud data warehouses.

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Data Warehouse Teams Adapt to Be Data Driven

TDAN

When companies embark on a journey of becoming data-driven, usually, this goes hand in and with using new technologies and concepts such as AI and data lakes or Hadoop and IoT. Suddenly, the data warehouse team and their software are not the only ones anymore that turn data […].

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Modernizing the Data Warehouse: Challenges and Benefits

BI-Survey

Data warehousing is getting on in years. Concepts and architectures have been applied more or less unchanged since the 1990s. However, data warehousing and BI applications are only considered moderately successful. But what are the right measures to make the data warehouse and BI fit for the future?

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Data architecture strategy for data quality

IBM Big Data Hub

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.