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How to Choose Your Data for Optimal Reporting: Live vs. Warehouse vs. Cubes – Webinar June 13th, 2019

Jet Global

Jet Analytics provides users with several data sources and data structures to choose from when building reports or dashboards. But how do you decide when to choose your live database, your data warehouse or your cubes? Webinar Date: Thurs June 13th, 2019 | 9:00am – 9:30am PDT. Register Now!

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Open Data Lakehouse powered by Iceberg for all your Data Warehouse needs

Cloudera

In this blog, we will share with you in detail how Cloudera integrates core compute engines including Apache Hive and Apache Impala in Cloudera Data Warehouse with Iceberg. We will publish follow up blogs for other data services. Read why the future of data lakehouses is open.

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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. Towards a better customer experience.

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Sisense’s Q2 Release: A Modern Data Experience Across the Analytics Continuum

Sisense

In-Warehouse Data Prep provides builders with the advanced functionality they need to rapidly transform and optimize raw data creating materialized views on cloud data warehouses. In-Warehouse Data Prep supports both AWS Redshift and Snowflake data warehouses.

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Become More Data-Driven by Evolving Analytics Workloads

CIO Business Intelligence

Data-driven organizations understand that data, when analyzed, is a strategic asset. It forms the basis for making informed decisions around product innovation, dynamic pricing, market expansion, and supply chain optimization. It’s the “new oil.” The challenge: There. Intel® Technologies Move Analytics Forward.

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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. Accordingly, data modelers must embrace some new tricks when designing data warehouses and data marts. Figure 1: Pricing for a 4 TB data warehouse in AWS.

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Birst automates the creation of data warehouses in Snowflake

Birst BI

Managing large-scale data warehouse systems has been known to be very administrative, costly, and lead to analytic silos. The good news is that Snowflake, the cloud data platform, lowers costs and administrative overhead. agentless) Birst to Snowflake real-time connector. What gaps does the joint solution address in the market?