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Data Modeling 301 for the cloud: data lake and NoSQL data modeling and design

erwin

For NoSQL, data lakes, and data lake houses—data modeling of both structured and unstructured data is somewhat novel and thorny. This blog is an introduction to some advanced NoSQL and data lake database design techniques (while avoiding common pitfalls) is noteworthy. Data modeling basics.

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The Future of the Data Lakehouse – Open

Cloudera

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes. Iterations of the lakehouse.

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The Future of the Data Lakehouse – Open

CIO Business Intelligence

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes. Iterations of the lakehouse.

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Keys to Ensure that Data isn’t Slowing Down your Innovation Efforts

Cloudera

Data Lifecycle Management: The Key to AI-Driven Innovation. In digital transformation projects, it’s easy to imagine the benefits of cloud, hybrid, artificial intelligence (AI), and machine learning (ML) models. The hard part is to turn aspiration into reality by creating an organization that is truly data-driven.

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Optimize data layout by bucketing with Amazon Athena and AWS Glue to accelerate downstream queries

AWS Big Data

In the era of data, organizations are increasingly using data lakes to store and analyze vast amounts of structured and unstructured data. Data lakes provide a centralized repository for data from various sources, enabling organizations to unlock valuable insights and drive data-driven decision-making.

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Why optimize your warehouse with a data lakehouse strategy

IBM Big Data Hub

In a prior blog , we pointed out that warehouses, known for high-performance data processing for business intelligence, can quickly become expensive for new data and evolving workloads. To do so, Presto and Spark need to readily work with existing and modern data warehouse infrastructures.

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How the cloud helps banking and finance companies tackle core modernization challenges

CIO Business Intelligence

Two decades of technology-driven transformation has left many financial services firms with significant complexity and technical debt. With the added challenge for IT leaders in the highly regulated financial services industry is that data security and business resiliency are unassailable business objectives.

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