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The biggest cons of the Tableau Public is that any data used in the program is ‘public’ and therefore not secure. And, with Tableau Public, published workbooks are “disconnected” from the underlying data sources and require periodic updates when the data changes. From Google.
The underlying data is in charge of data management, covering data collection, ETL, building a data warehouse, etc. The data analysis part is responsible for extracting data from the data warehouse, using the query, OLAP, data mining to analyze data, and forming the data conclusion with data visualization.
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). Further, “ML-Augmented dataintegration is making active metadata analysis and semantic knowledge graphs pivotal parts of the data fabric””.
Then the reporting engine publishes these reports to the reporting portal to allow non-technical end-users access. In this way, users can gain insights from the data and make data-driven decisions. . The underlying data is responsible for data management, including data collection, ETL, building a data warehouse, etc.
Other benefits include: Providing accurate, governed data through a single source of truth. Providing pre-built OLAP cubes, a data warehouse, and visualized dashboards. Rapid time to value through turnkey installation within hours. Low total cost of ownership. A drag-and-drop customization platform.
Quickly link all your data from Amazon Redshift, MongoDB, Hadoop, Snowflake, Apache Solr, Elasticsearch, Impala, and more. OLAP cubes Used for multi-dimensional analysis Strategic Objective When a vendor-specific connector is not available, generic connectors provide flexibility with data.
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