Remove Analytics Remove Cost-Benefit Remove Data Warehouse Remove Optimization
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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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Why optimize your warehouse with a data lakehouse strategy

IBM Big Data Hub

We also made the case that query and reporting, provided by big data engines such as Presto, need to work with the Spark infrastructure framework to support advanced analytics and complex enterprise data decision-making. To do so, Presto and Spark need to readily work with existing and modern data warehouse infrastructures.

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Cloudera Data Warehouse Demonstrates Best-in-Class Cloud-Native Price-Performance

Cloudera

Cloud data warehouses allow users to run analytic workloads with greater agility, better isolation and scale, and lower administrative overhead than ever before. With pay-as-you-go pricing, platforms that deliver high-performance benefit users not only through faster results but also through direct cost savings.

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Optimizing the Energy Sector with Data Analytics

Cloudera

Effective use of data can have a direct impact on the cash flow of wind and solar generation companies in areas such as real-time decision making. With the right insights, energy production from renewable assets can be optimized and better predict the future of supply and demand.

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Understanding Apache Iceberg on AWS with the new technical guide

AWS Big Data

Whether you are new to Apache Iceberg on AWS or already running production workloads on AWS, this comprehensive technical guide offers detailed guidance on foundational concepts to advanced optimizations to build your transactional data lake with Apache Iceberg on AWS. I mtiaz (Taz) Sayed is the WW Tech Leader for Analytics at AWS.

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Optimize your Go To Market with AI and ML-driven Analytics platforms

BizAcuity

Optimize your Go To Market: The gaming business consists of various applications like the gaming platforms (Casino, Live Dealer, Poker, Sports, Bingo, etc.), account platform, payment, affiliate, loyalty system, bonus and promotion systems, financial application, CRM system, and many others. Data Enrichment/Data Warehouse Layer.

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Data Modeling 201 for the cloud: designing databases for data warehouses

erwin

Designing databases for data warehouses or data marts is intrinsically much different than designing for traditional OLTP systems. In fact, many commonly accepted best practices for designing OLTP databases could well be considered worst practices for these purely analytical systems. Analytical. Business Focus.