Blogs

Because sharing is caring

New Google Cloud feature to Optimise BigQuery Costs

This blog explores AgileData’s use of Google Cloud, specifically its BigQuery service, for cost-effective data handling. As a bootstrapped startup, AgileData incorporates data storage and compute costs into its SaaS subscription, protecting customers from unexpected bills. We constantly seek ways to minimise costs, utilising new Google tools for cost-saving recommendations. We argue that the efficiency and value of Google Cloud make it a preferable choice over other cloud analytic database options.

AgileData Product

Explore AgileData features, updates, and tips

Consulting

Learn about consulting practises and good patterns for data focused consultancies

DataOps

Learn from our DataOps expertise, covering essential concepts, patterns, and tools

Data and Analytics

Unlock the power of data and analytics with expert guidance

Google Cloud

Imparting knowledge on Google Cloud's capabilities and its role in data-driven workflows

Journey

Explore real-life stories of our challenges, and lessons learned

Product Management

Enrich your product management skills with practical patterns

Resources

Valuable resources to support your growth in the agile, and data and analytics domains

What Is

Describing data and analytics concepts, terms, and technologies to enable better understanding

AgileData Podcast

Discussing combining agile, product and data patterns.

No Nonsense Agile Podcast

Discussing agile and product ways of working.

Product Videos

Explore product videos to better understand AgileData's features and capabilities.

Data as a First-Class Citizen: Empowering Data Magicians
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.

To whitelabel or not to whitelabel
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.

The Enchanting World of Data Modeling: Conceptual, Logical, and Physical Spells Unraveled
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.

Cloud Analytics Databases: The Magical Realm for Data
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.

Unveiling the Definition of Data Warehouses: Looking into Bill Inmon’s Magicians Top Hat
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
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.

Unveiling the Magic of Data Clean Rooms: Your Data Privacy Magicians
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.

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 |...

5E’s
5E’s

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.

Agile-tecture Data Factory
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.