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The magic of DocOps

TD:LR Patterns like DocOps provide massive value by increasing collaboration across team members and automating manual tasks. But it still requires a high level of technical skills to work in a DocOps way.  For the AgileData App and Platform, we want to delvier those...
Understanding Concepts, Details, and Events: The Fundamental Building Blocks of AgileData Design
Understanding Concepts, Details, and Events: The Fundamental Building Blocks of AgileData Design

Reducing the complexity and effort to manage data is at the core of what we do.  We love bringing magical UX to the data domain as we do this.

Every time we add a new capability or feature to the AgileData App or AgileData Platform, we think how could we just remove the need for a Data Magician to do that task at all?

That magic is not always possible in the first, or even the third iteration of those features.

Our AgileData App UX Capability Maturity Model helps us to keep that “magic sorting hat” goal at the top of our mind, every time we add a new thing.

This post outlines what that maturity model is and how we apply it.

Building a vibrant community with Scott Hirleman
Building a vibrant community with Scott Hirleman

In the episode of the AgileData podcast, Shane Gibson chats with Scott Hirleman, the founder of the data mesh community.

They delve into the nuances of cultivating and sustaining thriving communities. 

The duo touch upon the broader patterns that can be applied to both external and internal communities within organisations, and the essence of being agile and responsive to the community’s evolving needs.

AgileData App UX Capability Maturity Model
AgileData App UX Capability Maturity Model

Reducing the complexity and effort to manage data is at the core of what we do.  We love bringing magical UX to the data domain as we do this.

Every time we add a new capability or feature to the AgileData App or AgileData Platform, we think how could we just remove the need for a Data Magician to do that task at all?

That magic is not always possible in the first, or even the third iteration of those features.

Our AgileData App UX Capability Maturity Model helps us to keep that “magic sorting hat” goal at the top of our mind, every time we add a new thing.

This post outlines what that maturity model is and how we apply it.

Unveiling the Magic of Change Data Collection Patterns: Exploring Full Snapshot, Delta, CDC, and Event-Based Approaches
Unveiling the Magic of Change Data Collection Patterns: Exploring Full Snapshot, Delta, CDC, and Event-Based Approaches

Change data collection patterns are like magical lenses that allow you to track data changes. The full snapshot pattern captures complete data at specific intervals for historical analysis. The delta pattern records only changes between snapshots to save storage. CDC captures real-time changes for data integration and synchronization. The event-based pattern tracks data changes triggered by specific events. Each pattern has unique benefits and use cases. Choose the right approach based on your data needs and become a data magician who stays up-to-date with real-time data insights!

Layered Data Architectures with Veronika Durgin
Layered Data Architectures with Veronika Durgin

Shane Gibson and Veronika Durgan discuss layered data architecture, data management, and the challenges of integrating software engineering with data analytics. They advocate for the ELT (Extract, Load, Transform) approach over traditional ETL methods and emphasise the importance of understanding data provenance to increase trust. The hosts also discuss the concept of data lakes and the idea of a “data lakehouse,” merging file storage with cloud compute. The conversation concludes with the importance of defining data layers and their policies, the value of automation in data handling, and the need for clear data governance.

The challenge of parsing files from the wild
The challenge of parsing files from the wild

In this instalment of the AgileData DataOps series, we’re exploring how we handle the challenges of parsing files from the wild. To ensure clean and well-structured data, each file goes through several checks and processes, similar to a water treatment plant. These steps include checking for previously seen files, looking for matching schema files, queuing the file, and parsing it. If a file fails to load, we have procedures in place to retry loading or notify errors for later resolution. This rigorous data processing ensures smooth and efficient data flow.

AgileData App

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

What Is

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

Resources

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

AgileData Podcast

Discussing combining agile, product and data patterns.

No Nonsense Agile Podcast

Discussing agile and product ways of working.

App Videos

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

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 Information Factory
Agile-tecture Information 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.