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Imparting knowledge on Google Cloud's capabilities and its role in data-driven workflows
Describing data and analytics concepts, terms, and technologies to enable better understanding
Data as a First-Class Citizen: Empowering Data Magicians
Data as a first-class citizen recognizes the value and importance of data in decision-making. It empowers data magicians by integrating data into the decision-making process, ensuring accessibility and availability, prioritising data quality and governance, and fostering a data-centric mindset.
The patterns of Data Vault with Hans Hultgren
In this episode of the AgileData Podcast, we are joined by Hans Hultgren to discuss the patterns of data vault. Data vault is a data modeling technique that helps organisations to manage their data more effectively....
To whitelabel or not to whitelabel
Are you wrestling with the concept of whitelabelling your product? We at AgileData have been there. We discuss our journey through the decision-making process, where we grappled with the thought of our painstakingly crafted product being rebranded by another company.
Metadata-Driven Data Pipelines: The Secret Behind Data Magicians’ Greatest Tricks
Metadata-driven data pipelines are the secret behind seamless data flows, empowering data magicians to create adaptable, scalable, and evolving data management systems. Leveraging metadata, these pipelines are dynamic, flexible, and automated, allowing for easy handling of changing data sources, formats, and requirements without manual intervention.
Data Consulting Patterns with Joe Reis
Join Shane Gibson as he chats with Joe Reis on his experience in building and running a successful data and analytics consulting company.
The Enchanting World of Data Modeling: Conceptual, Logical, and Physical Spells Unraveled
Data modeling is a crucial process that involves creating shared understanding of data and its relationships. The three primary data model patterns are conceptual, logical, and physical. The conceptual data model provides a high-level overview of the data landscape, the logical data model delves deeper into data structures and relationships, and the physical data model translates the logical model into a database-specific schema. Understanding and effectively using these data models is essential for business analysts and data analysts, create efficient, well-organised data ecosystems.
Shane Gibson – Making Data Modeling Accessible
TD:LR Early in 2023 I was lucky enough to talk to Joe Reis on the Joe Reis Show to discuss how to make data modeling more accessible, why the world's moved past traditional data modeling and more. Listen to the episode...
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.
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.
Martech – The Technologies Behind the Marketing Analytics Stack: A Guide for Data Magicians
Explore the MarTech stack based on two different patterns: marketing application and data platform. The marketing application pattern focuses on tools for content management, email marketing, CRM, social media, and more, while the data platform pattern emphasises data collection, integration, storage, analytics, and advanced technologies. By understanding both perspectives, you can build a comprehensive martech stack that efficiently integrates marketing efforts and harnesses the power of data to drive better results.
Anatomy of a Data Product
A graphical overview of the components required for a Data Product
Unveiling the Magic of Data Clean Rooms: Your Data Privacy Magicians
Data clean rooms are secure environments that enable organisations to process, analyse, and share sensitive data while maintaining privacy and security. They use data anonymization, access control, data usage policies, security measures, and auditing to ensure compliance with privacy regulations, making them indispensable for industries like healthcare, finance, and marketing.
Data Lineage Patterns – Tomas Kratky
Join Shane Gibson as he chats with Tomas Kratky on his experience in defining data lineage and DataOps patterns.Guests Tomas Kratky Shane GibsonResourcesSubscribe | Apple Podcast | Spotify | Google Podcast | Amazon...
Free Google Analytics 4 (GA4) online courses
TD:LR There is some great free course content to help you upskill in Google Analytics 4 (GA4) Here are the ones we recomend.Discover the Next Generation of Google Analytics Find out how the latest generation of Google...
Observability – Raj Joseph
Join Shane Gibson as he chats with Raj Joseph on his experience in defining data observability patterns.Guests Raj JosephShane GibsonResourcesSubscribe | Apple Podcast | Spotify | Google Podcast | Amazon Audible |...
As Data Consultants your customers are buying and outcome based on one of these patterns – effort, expertise, experience or efficiency.
We outline what each of these are, how they are different to each other and how to charge for delivering them.
Conceptually Modeling Concepts, Details and Events in AgileData
Join Shane and Nigel as they discuss how and why we define a conceptual model of Concepts, Details and Events in AgileData and how we map these to a physical Data Vault model.Guests Nigel ViningShane...
Agile-tecture Data Factory
Defining a Data Architecture is a key pattern when working in the data domain.
Its always tempting to boil the ocean when defining yours, don’t!
And once you have defined your data architecture, find a way to articulate and share it with simplicity.
Here is how we articulate the AgileData Data Agile-tecture.
Data Architecture as a Service (DAaaS)
TD:LR Data Architecture as a Service (DAaaS), is it Buzzwashing or not? As is often the case, it depends on your point of view. Our point of view? Nope its a real thing.