Blogs
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Data Asset, Data Product, Data Service?
TD:LR Should we treat data as an Asset, a Product, a Service or a hybrid combination of all three? Data Asset, Data Product, Data Service? There has been a lot of discussions on LinkedIn, lots of podcasts, lots of webinars lately on the question of whether data should...
Demystifying the Semantic Layer
The semantic layer is your mystical bridge between complex data and meaningful business insights. It acts as a translator, converting technical data into a language you understand. It works through metadata, simplifying queries, promoting consistency, and enabling self-service analytics. This layer fosters collaboration, empowers customization, and adapts to changes seamlessly. With the semantic layer’s power, you can decipher data mysteries, conjure insights, and make decisions with wizard-like precision. Embrace this enchanting tool and let it elevate your data sorcery to new heights.
Attribution Model Patterns with Yorgos Moschovis
Shane Gibson and Yorgos Moschovis discuss the multifaceted world of marketing attribution. They explore the complexity of tracking customer behaviour across online and offline channels, the impact of privacy regulations, and various attribution models.
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
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.
Upgrading Python: A Plumbing Adventure in the Google Stack
In the ever-evolving world of AgileData DataOps, it was time to upgrade the Python version that powers the AgileData Platform.
We utilise micro-services patterns throughout the AgileData Platform and a bunch of Google Cloud Services. The upgrade could have gone well, or caused no end of problems.
Read more on our exciting plumbing journey.
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
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
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.
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AgileData Podcast
Discussing combining agile, product and data patterns.
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Discussing agile and product ways of working.
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How can data teams use Generative AI with Shaun McGirr
In this episode of the Agile Data Podcast, hosts Shane Gibson and Shaun McGirr delve deep into the transformative capabilities of large language models (LLMs), such as ChatGPT-3, and their potential to revolutionise...
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.
The Magic of Customer Segmentation: Unlocking Personalised Experiences for Customers
Customer segmentation is the magical process of dividing your customers into distinct groups based on their characteristics, preferences, and needs. By understanding these segments, you can tailor your marketing strategies, optimize resource allocation, and maximize customer lifetime value. To unleash your customer segmentation magic, define your objectives, gather and analyze relevant data, identify key criteria, create distinct segments, profile each segment, tailor your strategies, and continuously evaluate and refine. Embrace the power of customer segmentation and create personalised experiences that enchant your customers and drive business success.
Fast Answers at Your Fingertips: Unveiling AgileData’s ‘Ask a Quick Question’ Feature
Immerse yourself in the magical world of data with AgileData’s ‘Ask a Quick Question’ capability. Perfectly designed for data analysts and business analysts who need to swiftly extract insights from data, this capability facilitates quick data queries and rapid exploratory data analysis.
The Hitchhikers guide to the Information Product Canvas
TD:LR In mid 2023 I was lucky enough to present at The Knowledge Gap on the Information Product Canvas. Watch The Information Product Canvas, is an innovative pattern designed to capture data requirements visually and...
Magical plumbing for effective change dates
We discuss how to handle change data in a hands-off filedrop process. We use the ingestion timestamp as a simple proxy for the effective date of each record, allowing us to version each day’s data. For files with multiple change records, we scan all columns to identify and rank potential effective date columns. We then pass this information to an automated rule, ensuring it gets applied as we load the data. This process enables us to efficiently handle change data, track data flow, and manage multiple changes in an automated way.
The patterns of Activity Schema with Ahmed Elsamadisi
In this episode of the AgileData Podcast, we are joined by Ahmed Elsamadisi to discuss the patterns of activity schema. The activity schema is a data modeling pattern for data modeling and transformation. Some...
Unveiling the Secrets of Data Quality Metrics for Data Magicians: Ensuring Data Warehouse Excellence
Data quality metrics are crucial indicators in a data warehouse that measure the accuracy, completeness, consistency, timeliness, and uniqueness of data. These metrics help organisations ensure their data is reliable and fit for use, thus driving effective decision-making and analytics
Amplifying Your Data’s Value with Business Context
The AgileData Context feature enhances data understanding, facilitates effective decision-making, and preserves corporate knowledge by adding essential business context to data. This feature streamlines communication, improves data governance, and ultimately, maximises the value of your data, making it a powerful asset for your business.
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.
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.