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A data platform modernisation is a daunting, once-per-decade endeavour. Fortunately, we do dozens per year! Whether you're building a lakehouse architecture or deciding between Microsoft Fabric vs. Databricks vs. Snowflake, this free briefing is the best hour you can spend to refine your thinking.

  • What's the difference between a Data Warehouse, a Data Lake, a Data Lakehouse and a Data Mesh?
  • Our BI architecture is struggling to keep up with the demands of the business, what are our options?
  • Should I invest in Microsoft Fabric, Snowflake, Databricks or Azure Synapse Analytics?
  • Should I use Azure SQL Database or Cosmos DB?
  • How can I build a Knowledge Graph on my data lake?
  • Could I migrate from Tableau or Qlik to Power BI, simplify my data estate and save money?
  • What is Azure Purview, and how can I use it to implement a data governance framework?
  • How much would it cost to build, grow and maintain a data platform?
  • What skills do my data team need now?
  • What is the Microsoft Fabric Roadmap?
  • Is Azure Synapse Analytics dead?
  • How can we implement the Medallion Architecture for our data platform?
  • How can OneLake simplify my data integration architecture?
  • What are Azure Data Lake best practices?
  • How do we create a Cloud Operating Model for our new data platform?
  • Do I need a Data Strategy before I can have an AI Strategy?
  • What is the difference between Databricks Spark, Azure Synapse Spark and Microsoft Fabric Spark?
  • What can we replace our existing SQL Server Integration Services (SSIS) implementation with?
  • Should we use ADF, Azure Synapse Pipelines or Microsoft Fabric Data Factory for our data / ETL pipelines?
  • Can I migrate from Azure Synapse Analytics to Microsoft Fabric?
  • How can we apply DevOps to our data platform?
  • Should we invest in R or Python programming skills?
  • How can I get started with analytics and insights from my IIoT data?
  • Is a Full Stack Serverless data warehouse possible?
  • How do I architect for scale, cost efficiency and governance on Azure?


Wishing to accelerate the journey to becoming a data driven organisation.


Interested in using data to optimise business outcomes and costs.


Creating a roadmap for your data platform modernisation.

IT Director

Reducing the operational cost of your data platform while increasing security.

Head of Analytics

Needing to increase agility and speed of delivery of new insights.

Analytics Manager

Responsible for creating new analytical capabilities with your existing team.

The briefing is a FREE one-to-one session via Microsoft Teams. We'll cut through the marketing hype and provide pragmatic advice about adopting Cloud Native Analytics, and how you can transform into a data driven and AI infused organisation.

Our briefing with endjin helped us quickly understand the available options for modernizing and scaling up our BI processes with Microsoft Azure.
Chris Umphlett Manager of Data Analysis

Wardley Maps are a strategic thinking tool used to explore a problem space; describe value streams, relationships and influences.

A map allows you to run scenarios to understand how a stratagem may play out, and which tactics are most effective to achieve your desired outcome.

We use Wardley Maps to explore how different data products and architectures compare and contrast, and how best to utilise them to achieve your goals.

Diagram showing a wardley map of azure products
The data strategy briefing is unlike other sessions I have gone through with other vendors, endjin truly are the best in their field and their approach in designing and deploying Microsoft analytics solutions is advanced, yet practical.
7Eleven's Data and Analytics Manager Renee Chin Sheau Wei Data & Analytics Manager
The evolution from traditional Business Intelligence to Bimodal Analytics to Cloud Native Analytics.
Creating Data APIs and Data Products to deliver economies of scale across your insight pipelines.
Use an Insight Discovery Process to ensure you deliver analytics that drive impactful outcomes.
The commercial model of Cloud Native Analytics Platforms and how to optimise your costs.
How new data engineering & DataOps practices can help you deliver more value, faster, at higher quality.
New technical and new analytical capabilities your existing team will need to develop.

At the end of the briefing you will have the knowledge to make informed decisions about your future Azure data strategy & roadmap. We'll also provide you with a recording of the meeting, a transcript and our Cloud Native Analytics Toolkit.

How long does the Data Strategy Briefing last?
We aim for 60 minutes, which usually breaks down into: introductions (5 mins), context (5 mins), briefing (35 mins) end-to-end demo (15 mins), but we try to allow a buffer so that the session can overrun, as we find that once the conversation starts flowing, it can easily last 90 minutes!
Can I share my current challenges before the briefing?
Absolutely! You can either add some context to the booking form, or you can reply to the meeting invite and add some background, attach architecture diagrams. The more information we have in advance, the more we can tailor the briefing to your specific needs.
Is the briefing confidential?
By default we operate under Chatham House Rule, but if you prefer, we can issue a mutual NDA to sign. If you require your own organisations NDA to be signed, we can do that too, but it may take a little longer as we'll have to review the contract.
Can I invite my colleagues to the briefing?
Feel free to invite as many people as you feel comfortable with. The majority of people who book want a 1-2-1 briefing as they want a safe space where they can speak about their challenges candidly and without judgement. We have run the session for different groups within a single organisation, so that it could be tailored to the specific needs of each group. We'll also provide you with a recording of the briefing, which you are free to share with your colleages.
Is this a thinly veiled sales meeting?
Absolutely not! There's no obligation to engage with us in any way, and we're not going to try and sell you anything. We don't even have a sales team! The two quotes on this page are from people who had briefings, but aren't our customers. We love to talk and share knowledge, which you may have noticed if you read our blog, watch any of our talks, consume any of our open source projects, or subscribe to our Azure Weekly or Power BI Weekly newsletters. We find it really useful to talk to external organisations about their data challenges - it helps us better understand the marketplace. That being said, often the participants get a huge amount of value from the session, and the last question we get asked is "how can we work together?"
Are you going to push Microsoft Fabric?
No. We have experience with Databricks, Snowflake, Azure Synapse Analytics, Power BI, SQL Server, Cosmos DB and many other data platforms and tools. We often work in heterogenous data estates, where teams have selected various "best of breed" solutions to solve specific problems. But our "We help small teams achieve big things" mantra comes into play here, as we find that data teams are often under-resourced for the demands placed upon them, and we advocate for any solution which alleviates low value infrastructure work, and allows the team to focus on delivering high business value activities. This is why we love Wardley Maps, as they help to visualise between "Genesis" or "Custom Built", which tend to require large amount of effort, and "Product" and "Commodity" which tends to require less effort, and deliver more value.