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5 misconceptions about cloud data warehouses

IBM Journey to AI blog

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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Self-Service BI vs Traditional BI: What’s Next?

Alation

Business intelligence has a long history. Today, the term describes that same activity, but on a much larger scale, as organizations race to collect, analyze, and act on data first. With remote and hybrid work on the rise, the ability to locate and leverage data and expertise — wherever it resides — is more critical than ever.

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Data Lakes Vs. Data Warehouse: Its significance and relevance in the data world

Pickl AI

Discover the nuanced dissimilarities between Data Lakes and Data Warehouses. Data management in the digital age has become a crucial aspect of businesses, and two prominent concepts in this realm are Data Lakes and Data Warehouses. It acts as a repository for storing all the data.

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Data architecture strategy for data quality

IBM Journey to AI blog

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases.

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Connecting Amazon Redshift and RStudio on Amazon SageMaker

AWS Machine Learning Blog

Many of the RStudio on SageMaker users are also users of Amazon Redshift , a fully managed, petabyte-scale, massively parallel data warehouse for data storage and analytical workloads. It makes it fast, simple, and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools.

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Understanding AdTech Data and How Snowflake Can Help

phData

You can store and access your structured, semi-structured, and unstructured data in one location and gain seamless access to external data with similar scale and speed. Snowflake’s cloud-based data warehouse can be used to store and query large amounts of data from multiple sources, such as ad networks, DSPs, and SSPs.

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IBM to help businesses scale AI workloads, for all data, anywhere

IBM Journey to AI blog

Watsonx.data will allow users to access their data through a single point of entry and run multiple fit-for-purpose query engines across IT environments. Through workload optimization an organization can reduce data warehouse costs by up to 50 percent by augmenting with this solution. [1]