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BI Cubed: Data Lineage on OLAP Anyone?

Octopai

How much time has your BI team wasted on finding data and creating metadata management reports? BI groups spend more than 50% of their time and effort manually searching for metadata. This is how the Online Analytical Processing (OLAP) cube was born, which you might call one of the grooviest BI inventions developed in the 70s.

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RDF-Star: Metadata Complexity Simplified

Ontotext

To handle such scenarios you need a transalytical graph database – a database engine that can deal with both frequent updates (OLTP workload) as well as with graph analytics (OLAP). Not Every Graph is a Knowledge Graph: Schemas and Semantic Metadata Matter. Metadata about Relationships Come in Handy. time span).

Metadata 119
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2019 Highlights in Metadata Management

Octopai

As 2019 comes to a close, we think it’s the perfect time to review trends in metadata management as well as look at some of Octopai’s own highlights. Metadata is everywhere and its success depends on deploying the right resources for its management. Expanded our support of Microsoft OLAP cube , an innovative open-source feat.

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Build a real-time analytics solution with Apache Pinot on AWS

AWS Big Data

Online Analytical Processing (OLAP) is crucial in modern data-driven apps, acting as an abstraction layer connecting raw data to users for efficient analysis. OLAP combines data from various data sources and aggregates and groups them as business terms and KPIs.

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CCPA 2020: Getting Your Data Landscape Ready

Octopai

Octopai’s metadata discovery and management suite provides visualization tools that empower you to see and report everything about sensitive customer data. Octopai's Automated Metadata Management Platform can make CCPA compliance a breeze. Keeping the Lights On with Automated Metadata Management. Not Yet CCPA Compliant?

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Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

In other words, using metadata about data science work to generate code. One of the longer-term trends that we’re seeing with Airflow , and so on, is to externalize graph-based metadata and leverage it beyond the lifecycle of a single SQL query, making our workflows smarter and more robust. BTW, videos for Rev2 are up: [link].

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How to Build a Performant Data Warehouse in Redshift

Sisense

OLTP vs OLAP. First, we’ll dive into the two types of databases: OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing). An OLAP database is best for situations where you read from the database more often than you write to it. OLAP databases excel at queries that require large table scans (e.g.