What is an Architecture Blueprint?
The first step for designing your data platform
Are you a data architect or a senior data engineer who has been given the responsibility of designing your organization's new data platform? If yes, the first step in your design process is creating the architecture diagram, also known as the blueprint.
Introduction
An architecture blueprint represents the core components of the data platform and their interdependencies, showing how the data will flow between them. It helps you understand the target state you want to achieve and the final technology stack you plan to use to implement your data platform.
Once the architecture blueprint is finalized, you can use it as input to create the high-level design document. It will help you establish the design principles and various data management processes accordingly.
Key design considerations
Some of the key considerations for designing the architecture blueprint are listed below:
Functional and non-functional requirements
Data type to be supported - structured, semi-structured or unstructured data
Workloads to be supported - BI, ETL, Real-time, AI/ML
Any specific tech stack to be preferred (or not to be considered as per the organization’s guidelines)
Any particular requirement around data governance, security, and operations.
These are just a few key considerations, and the list can vary based on your use case.
Design decisions
There are different architectures and several tools and technologies for implementing your data platform, so you must make critical design decisions while creating the architecture. Some of these decisions are:
Which architecture to use - a cloud data warehouse or a lakehouse approach
Which cloud platform to use - Do you want to use AWS, Azure GCP, or any third-party platforms like Databricks or Snowflake
Which services should you consider - you can use AWS Glue or Amazon EMR for processing in AWS. In Azure, you can use Synapse Analytics or the Microsoft Fabric. Every platform has multiple services that you can explore.
Buy vs. Build: Do you want to buy commercial products or build specific functionalities internally?
Cost vs. Performance: What are the tradeoffs, and how do you want to approach these?
Evaluate each of these carefully, study their pros and cons and other aspects like cost and performance benefits.
Creating the blueprint
Based on the above design considerations, you should create the final blueprint. You will need multiple iterations to develop the draft, numerous reviews to improve it, and architecture walkthroughs with the relevant stakeholders before finalizing it.
To understand this process in more detail, watch this YouTube video, which explains all these points.
Common mistakes and best practices
Some of the common mistakes to avoid while creating the blueprint are summarized below:
Avoid making decisions without proper research and analysis. Decisions like architecture and tech stack selection require time, effort, and self-study.
Selecting a tech stack without doing a PoC or tech feasibility study can lead to severe issues later, delaying delivery timelines.
Another common mistake is following generic design templates. Every use case is different; every project is unique. So design per your requirements and use cases.
Don't just follow what has been done previously. Be bold and explore new things. Many new tools & products are available in the market that might suit your requirements. Explore them while evaluating the tech stack.
Conclusion
The architecture blueprint can help you visualize the target state and finalize the architecture and tech stack. Spend enough time studying and exploring before making critical decisions. If you need further guidance, you can book a free mentoring session with me on topmate.
Thanks for reading Data & Cloud. I'm a solopreneur working as a data architecture consultant. I'm certified in AWS, Databricks, and Snowflake and the author of Practical Lakehouse Architecture (O'Reilly).