Product forum for data: decisions, backlog, change/deprecation. 100-day plan for data product owners. Learn more with sources.

Breaking changes in data products (semantics, keys, time logic), SemVer as a language, change policy and deprecation practice.

Data quality in data products: exploration/standard/business-critical, SLOs for freshness/availability/quality, and response patterns for deviations.

How data products should look in practice: product page, point of contact, changelog, runbook, catalogue and minimum practice for components.

How to share data products within a domain, across domains and externally. Data contracts when risk and dependency warrant them.

Why data products deliver value: less fragmentation, more reuse and measurable benefit. Examples of value hypotheses and metrics.

Business canvas and Minimum Viable Data Product (MVDP): customers, value, product surface, minimum requirements and Definition of Done.

Ten rules that separate data product from component, plus lifecycle, domains and data ownership in practice.

What is a data product – and what separates it from a table? Definition, characteristics and practical examples.

Five architecture choices that should be made before building starts -- to reduce risk, avoid technical debt and increase delivery speed in data and analytics programs.

Read our honest summary of the second half of 2025 at Glitni. We look back at new clients - and a new office.

How do you go from the wild west of data pipelines to reusable factory patterns — without undermining decentralised product teams? In this article, we look at how a small community of practice can coordinate shared learning and take responsibility for standards, patterns and tools for data engineering.

The integration platform makes the organization react. The data and analytics platform makes it learn. We show why the roles must be separated, and how the interplay between them creates both stable operations and new insights.

Declarative data platforms let you describe the desired state rather than the process. Less code, more structure -- and a faster path from data to value.

Read our honest summary of the first half of 2025 at Glitni. We look back on new clients and more new hires.

Data contracts make it possible to build trust between teams, systems and people who produce and consume data. We explain how and why.

Microsoft Fabric | Glitni has a range of consultants with experience in Microsoft Fabric.

Microsoft Fabric | Glitni has a range of consultants with experience in Microsoft Fabric.

We propose a solution to reduce runtime and cost by replacing Copy activities with Azure Functions in Azure Data Factory, which is particularly relevant when dealing with a large number of simple activities.

Read our honest summary of the second half of 2024 at Glitni. We look back on new clients and more new hires.

Glitni shares a proposed testing framework that enables unit testing of macros in dbt.

Understand the history of data modelling, the significance of the information architect in today's data ecosystem, and how the role has gained new relevance in the face of modern challenges such as data lakehouses and AI.

Glitni and Telum offer expertise in data platforms, data engineering and data science. Read more about the experienced team!

Discover how to get started quickly with generative AI based on advice from experienced data scientists and data engineers.

Read our honest summary of the first half of 2024 at Glitni. We look back on a stable project portfolio, recruitment and increased visibility.

Understand how AI can solve cognitive tasks. We guide you through five steps from strategy to implementation, with a focus on Norwegian examples and practical tips.

Learn about the components of a modern data platform, from data sources to consumption. Explore how they work together to collect, store, process, and present data.

Learn more about the transition from data warehouse monoliths to domain-divided data platforms, and the benefits this provides.

Explore the evolution of data-related terms from Business Intelligence to Data Mesh and Microsoft Fabric in this article. Perfect for those who are new to the world of data and want to learn more.

Many data teams take a reactive approach to projects and are often disconnected from the rest of the company. By actively leveraging metadata, they can work much more proactively and focus more on work that delivers maximum value to the company.

New podcast in Norwegian! "Datautforskerne" explores trends, technology and strategies in data. Subscribe to learn about data-driven organisations and get professional tips.

Discover Glitni's tech stack! From Google Cloud to GitHub, see which tools we use for infrastructure, sales, marketing, admin, HR, and development.

Explore the trends in machine learning, AI and data architecture for 2024. Key players, technology development and predictions from FirstMark Capital.

Glitni has had an eventful second half of 2023, with highs and lows along the way. We run another retro for the past half-year.

Experienced consultants in data and analytics share ten common mistakes. Read to learn, whether you are a consultant or you hire consultants.

The consulting company Glitni is growing, with some friction, but mostly proud moments. We run a retro for the past half-year.

Establishing a data platform does not have to take months and years. We outline some simple steps to deliver value quickly -- but remember that development must not stop there.

Establishing data ownership is, in our view, the first step on the path to data governance and can be the key to increasing the value of data in your organisation.

Learn more about the books you should read on data and analytics

We summarize DataOps and answer several frequently asked questions.

We look at 10 steps that can help you get started with DataOps.

How does DataOps work in practice? We cover the four main areas: automation, continuous delivery, quality management, and collaboration.

What role does agile play in DataOps? We answer that, and a bit more!

In this article, we look at the differences between DataOps and DevOps.

We have written an article about a key limitation in Azure Data Factory that is not yet resolved by Microsoft Fabric

Learn more about Data Mesh's new approach to data architecture and data organization, based on ideas from software and team organization.

Azure | Glitni has a number of consultants with certifications and experience with Microsoft Azure.

Azure Synapse Analytics | Glitni has a number of consultants with certifications and experience with Azure Analytics.

Snowflake | Glitni has experienced consultants who help you with architecture and implementation of Snowflake.

Glitni has experienced consultants who can help you with architecture and implementation on Google Cloud.

Databricks | Glitni has experienced consultants who help you with architecture and implementation of Databricks.

Snowflake | Glitni has experienced consultants who help you with architecture and implementation of Snowflake

Glitni has experienced consultants who can help you with architecture and implementation of Google BigQuery

Databricks | Glitni has experienced consultants who help you with architecture and implementation of Databricks

Read on to learn about the pros and cons of both alternatives and what the best approach would be for your specific needs.

Glitni summarises the MAD Landscape 2023, which maps the technology development in machine learning, AI and data.

Do you have a full overview of everything needed to become more data-driven? Here are 7 trends you should know about.

OK. It took some effort to secure projects for everyone. Decision-making processes take time, after all. But it ended well.

How do we prepare ourselves and those around us for a new data platform? Here are 5 things worth considering before the implementation is complete.

Glitni has now entered partnerships with several of the biggest names in modern data platforms: dbt Labs, Snowflake, Google Cloud and Databricks.

Is a separate strategy for data necessary? Read about what a data strategy helps you with and why you might want to reconsider the data strategy you already have.

Cloud-based architecture changes how we work and organize. Here is why you need a centralized platform team.

We take a quick look at the launches from dbt Labs during Coalesce 2022, which included a new semantic layer and support for Python-developed models.

We started with reporting on top of databases, moved on to 30 years of data warehousing, before data lake and now data lakehouse have taken over.

The data lakehouse is the lovechild of the data lake and the data warehouse, and is suited for storing and processing all forms of data for reporting and analytics.

A data warehouse is well suited for supporting reporting with data from many sources, and is used to collect relevant data needed for various management purposes.

A data lake is suited for storing all forms of data for analytical user stories, including data we are not entirely sure we will use.

A database is designed to store data and is a common component in a great many IT solutions, because how else would we be able to keep track of everything that happens?

It has been fun running a consulting company this half-year! Because it is quite fun when everyone is running in the same direction and rolling up their sleeves when it counts.

Data processing costs can easily spiral out of control in the cloud. We have some simple tips - you wouldn't want to give away money when it's easy to do something about it, would you?

We have published a Medium article comparing schema-on-read support for JSON across a selection of popular data processing engines

We have just been through some exercises that we believe most people thinking about starting a consulting company will need to go through, so we are sharing!

Do you have a full overview of everything needed to become data-driven? Here are 7 trends, including data lakehouse, dataops, ELT and Pub/Sub, business analytics, data mesh.

Get tips on how to start a dbt project (data build tool) from a data platform engineer. dbt should be considered a central part of any data stack in 2022.

Many of today's most successful organisations are building the ability to learn from data -- and are adopting increasingly advanced data analytics.