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How OLAP and AI can enable better business

IBM Big Data Hub

Initially, they were designed for handling large volumes of multidimensional data, enabling businesses to perform complex analytical tasks, such as drill-down , roll-up and slice-and-dice. Early OLAP systems were separate, specialized databases with unique data storage structures and query languages.

OLAP 59
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Simplify Metrics on Apache Druid With Rill Data and Cloudera

Cloudera

Cloudera users can securely connect Rill to a source of event stream data, such as Cloudera DataFlow , model data into Rill’s cloud-based Druid service, and share live operational dashboards within minutes via Rill’s interactive metrics dashboard or any connected BI solution. Cloudera Data Warehouse). Apache Hive.

Metrics 85
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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. Additional capabilities.

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13 power tips for Microsoft Power BI

CIO Business Intelligence

Quick measure suggestions already help users build DAX queries by asking questions about data in natural language, and other upcoming Microsoft Copilot features will take that a step further by suggesting slightly different questions you can ask when customizing reports.

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Improve User Adoption of BI Tools with Embedded BI!

Smarten

Integrate objects (Dashboards, Crosstab, Tabular, KPIs, Graphs, Reports, models, Clickless Analytics and more).’ Embedded BI and Augmented Analytics includes traditional BI components like dashboards, KPIs, reports with interactive drill-down, drill through, slice and dice and self-serve analytics capabilities. Deploy anywhere!

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The Data Journey: From Raw Data to Insights

Sisense

Sisense provides instant access to your cloud data warehouses. Data modeling: Create relationships between data. Once the data is stored, data engineers can pull from the data warehouse or data lake to create tables and objects that are organized in more easily accessible and usable ways.

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Why Choose a Hybrid Data Cloud in Financial Services?

Cloudera

Open and extensible to support new clouds, data types and data services. All in a distributed cloud model that spans multi-public, private & edge clouds. . Here, I’ll provide some guidance on key considerations for a hybrid data cloud. What data do I need to achieve these objectives?