Fundamentals on Data Semantic Layer
cubejs | Cube — Semantic Layer for Building Data Applications
😌I am sure that you have heard of Data Semantic Layer many times in your day-to-day life in this data-driven world.
Let’s see in a very minimalistic yet effective way to understand WHAT WHY and HOW about data semantic layer.
Feel free to look at my 🚀🚀medium blog and 🚀🚀github repo for deployment of cubejs on kubernetes using helm charts
🤔What…?
Data is a new 🤑‘OIL’ in this era, but it has limited value within it.
What matter most is the knowledge that one can extract from this Data. This is where Data Sematic Layer comes to make the job done in easier way.
So basically, The Data Semantic Layer is kind of a “middleware or bridge who sits in between your Data Source and your application and helps in extracting Business insights from the raw data”
Currently there are many platforms through which we can adopt this Semantic layer. one such platform which I like personally is Cube — Semantic Layer for Building Data Applications.
👀WHY…?
Let’s explore some of the potential benefits of using data semantic layer with one example.
🔺Centralized Reporting and Analytics
➖scenarios: A company wants to collects data from its regional offices, manufacturing plants, and sales channels worldwide.
✔use cases: It centralizes all data sources into a unified model, providing a single source of truth for reporting and analytics.
🔺Bussiness Intelligence
➖scenarios: A company wants to visualize their “Bussiness view” of data in user intuitive dashboard.
✔use cases: It is very easy to integrate semantic layer data to BI tools enabling the integration of data from multiple data source and creating the unified model for analysis.
🔺Data Governance and Consistency
➖scenarios: A healthcare organization needs to ensure compliance with regulatory standards (like HIPAA) while maintaining data quality and consistency across patient records..
✔use cases: It establishes governance policies, data definitions, and standards, ensuring data consistency, integrity, and compliance.
🔺Data security
➖scenarios: A financial institution encrypts sensitive customer information such as account numbers and Social Security numbers to prevent unauthorized access.
✔use cases: It implements encryption and masking techniques to protect sensitive data at rest and in transit, ensuring confidentiality and compliance with data privacy regulations
🔺Access Control and Role-Based Permissions
➖scenarios: An e-commerce platform restricts access to customer data based on user roles, allowing only authorized personnel to view or modify sensitive information.
✔use cases: It enforces role-based access control (RBAC) policies, granting permissions based on user roles and responsibilities, and limiting access to data as per defined policies.
🙃HOW…?
Understanding Semantic Layers
👉Data source
🔸Data Ingestion: Collating the data from multiple data sources
🔸Data Preparation(ETL) : Clean , Validate,Extract and Transform the data
👉Implementation
Have a loot at my 🚀🚀medium blog and 🚀🚀github repo for deployment of cubejs on kubernetes using helm charts
🔸Choosing the right platform: cubejs, AtScale, Transform.
🔸Creating the Data Model — Designing the Data models as per the business requirement.
🔸Calculations and aggregations — Adopting the Aggregations models wherever there is a possibility to ensure less latency.
🔸Security and Access Control — Implementing the role-based-access-control so that only the right people have access to the right metrics.
🔸Data Caching — Implementing the caching mechanism will improvide the performance and reduces the compute overhead on servers.
🔸Choosing the API model — GraphQL,SQL and REST APIs.
Watch out for my upcoming blog on Cubejs Data Modeling with Example(coming soon….!)
