AgileData Cost Comparison
AgileData reduces the cost of your data team and your data platform.
In this article we provide examples of those costs savings.
Scrum Anti Patterns with Stefan Wolpers
Scrum Anti Patterns with Stefan Wolpers Join Murray Robinson and Shane Gibson as they chat with Stefan Wolpers about scrum anti-patterns. Explore common anti-patterns, such as scrum masters assigning tasks to disempowered teams, and discover the solutions for...
Cloud Analytics Databases: The Magical Realm for Data
Cloud Analytics Databases provide flexible, high-performance, cost-effective, and secure solution for storing and analysing large amounts of data. These databases promote collaboration and offer various choices, such as Snowflake, Google BigQuery, Amazon Redshift, and Azure Synapse Analytics, each with its unique features and ecosystem integrations.
Data Warehouse Technology Essentials: The Magical Components Every Data Magician Needs
The key components of a successful data warehouse technology capability include data sources, data integration, data storage, metadata, data marts, data query and reporting tools, data warehouse management, and data security.
Unveiling the Definition of Data Warehouses: Looking into Bill Inmon’s Magicians Top Hat
In a nutshell, a data warehouse, as defined by Bill Inmon, is a subject-oriented, integrated, time-variant, and non-volatile collection of data that supports decision-making processes. It helps data magicians, like business and data analysts, make better-informed decisions, save time, enhance collaboration, and improve business intelligence. To choose the right data warehouse technology, consider your data needs, budget, compatibility with existing tools, scalability, and real-world user experiences.
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