Blogs

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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.

DataOps: The Magic Wand for Data Magicians
DataOps: The Magic Wand for Data Magicians

DataOps is a magical approach to data management, combining Agile, DevOps, and Lean Manufacturing principles. It fosters collaboration, agility, automation, continuous integration and delivery, and quality control. This empowers data magicians like you to work more efficiently, adapt to changing business requirements, and deliver high-quality, data-driven insights with confidence.

Information Product Canvas

Join Shane and Tammy Leahy as they discuss the Information Product Canvas, what each area of the canvas holds and why you would want to collect this information.

This is the second in a series of podcast episodes that deep dives into the Information Product pattern.

ELT without persisted watermarks ? not a problem
ELT without persisted watermarks ? not a problem

We no longer need to manually track the state of a table, when it was created, when it was updated, which data pipeline last touched it …. all these data points are available by doing a simple call to the logging and bigquery api. Under the covers the google cloud platform is already tracking everything we need … every insert, update, delete, create, load, drop, alter is being captured