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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. The rise of cloud has allowed data warehouses to provide new capabilities such as cost-effective data storage at petabyte scale, highly scalable compute and storage, pay-as-you-go pricing and fully managed service delivery.

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The Top Three Entangled Trends in Data Architectures: Data Mesh, Data Fabric, and Hybrid Architectures

Cloudera

Data teams have the impossible task of delivering everything (data and workloads) everywhere (on premise and in all clouds) all at once (with little to no latency). Each of these trends claim to be complete models for their data architectures to solve the “everything everywhere all at once” problem. Data mesh defined.

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Power analytics as a service capabilities using Amazon Redshift

AWS Big Data

The AaaS model accelerates data-driven decision-making through advanced analytics, enabling organizations to swiftly adapt to changing market trends and make informed strategic choices. times better price-performance than other cloud data warehouses. times better price-performance than other cloud data warehouses.

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Bringing Financial Services Business Use Cases to Life: Leveraging Data Analytics, ML/AI, and Gen AI

Cloudera

The financial services industry is undergoing a significant transformation, driven by the need for data-driven insights, digital transformation, and compliance with evolving regulations. What are some of the reasons that TAI Solutions’ customers choose Cloudera?

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Peloton embraces Amazon Redshift to unlock the power of data during changing times

AWS Big Data

Credit: Phil Goldstein Jerry Wang, Peloton’s Director of Data Engineering (left), and Evy Kho, Peloton’s Manager of Subscription Analytics, discuss how the company has benefited from using Amazon Redshift. From 2019 to now, Wang reports the amount of data the company holds has grown by a factor of 20.

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Announcing zero-ETL integrations with AWS Databases and Amazon Redshift

AWS Big Data

As customers become more data driven and use data as a source of competitive advantage, they want to easily run analytics on their data to better understand their core business drivers to grow sales, reduce costs, and optimize their businesses. ETL is the process data engineers use to combine data from different sources.

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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

Businesses are constantly evolving, and data leaders are challenged every day to meet new requirements. Customers are using AWS and Snowflake to develop purpose-built data architectures that provide the performance required for modern analytics and artificial intelligence (AI) use cases.

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