What is the best way to scale data engineering and warehousing for OLAP?

OLAP is a semantic layer and you have to think that how will the users consume this data? How will you add annotations to these models?

Business First, Tech Second Scaling OLAP requires understanding the business goals. Start by clearly defining the questions and data sources needed. Then, map out the data warehouse based on performance demands and choose tools that optimize OLAP queries.

One of the best ways to scale data engineering and warehousing for OLAP is by utilizing cloud-based solutions. Cloud platforms like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform provide scalable and efficient infrastructure for data storage, processing, and analytics. Leveraging cloud-based services such as distributed storage (e.g., Amazon S3, Azure Blob Storage), scalable compute resources (e.g., Amazon EC2, Azure Virtual Machines), and managed data warehousing services (e.g., Amazon Redshift, Azure Synapse Analytics) allows for seamless scalability, on-demand provisioning, and cost optimization.

Some general Data Model considerations: -Kimball: Ideal for agile, cost-effective solutions in smaller businesses. Prioritize if quick deployment & user-focused queries are essential. -Inmon: Best for large enterprises with complex, highly normalized data needs. Choose for long-term data integration & accuracy. -Data Mesh: Suitable for decentralized data environments with strong cross-functional teams. Ideal for flexible, domain-driven data management. -Data Vault: Optimal for frequently changing data & historical tracking. Fits well in environments needing adaptability & parallel development. -Snowflake: Perfect for cloud scalability & diverse data workloads. Choose for easy data sharing & integration with various sources.

?? Phrase indicate about data modeling techniques to data warehousing with dimensional approach; but there are others approach like: ? Normalized approach ( or named "inmon" or "corporate information factory"), ? Ensemble data modeling: Data Vault, Anchor, Focal, Hyper-agility, Head & Version. In my opinion and knowledge, ensemble data modeling is a great option to mantain and build it in an agile way, since you can create different consumption models based on business requirements.

You also need to consider what type of toolset is right for you. Do want to go for a data pipeline tool that has a fixed yearly subscription or should I go with a usage based tool. If the pipelines are not optimized using a usage based tool the cost can skyrocket.

?? In general, 2 points can help to control in case of processing in data pipeline: 1?? Request flow control: use task queue & gateway to handle workload, since each transformation is to be a separate and independent workload inside data pipeline. 2?? Processing servers: use a server cluster to handle required working. If server cluster use auto-scaling feature better.

???? In general, maintain tracking of activities in your network architecture inside OLAP system! ? From security view, know what requests is come in and out in each component in data arquitecture is critical to avoid bad users and act in worst cases: so setup valid required ports and CIDR both out and in. ? Always get document what IPs will be used from components in data architecture.

??? Remember that any system; including data architecture; have several view must need to covered within the system itself. Check several topics inside organization teams as well community and public resources, somethings are: ?? Security ?? Networking ?? Privacy There are several experts in these topics want to share their expertise ??.