What are the best data modeling trends for handling large amounts of data?

NoSQL Databases: they offer flexibility and scalability, ideal for varied and voluminous data. Ex: MongoDB and Cassandra. Columnar Databases: Instead of row-based storage, store data in columns, optimizing analytics and search speed. Ex: Bigtable Graph Databases: for datasets with complex relationships, like social networks. Ex: Neo4j Data Lakes: store structured and unstructured data at scale, offering raw data access. Ex: AWS's Lake Formation. Automated Machine Learning: simplify model training on large datasets, enhancing predictive accuracy. Ex: Google's Cloud AutoML Aim for flexibility, relationship-centric models, and automation for efficiency in handling colossal data volumes.

Data lakes are the top trend for any modern data platform solutions on premise or cloud. They provide ability to not only store all types of data i.e. structured or unstructured etc but also they provide cheap storage in raw format. Most hyper-scalers like Azure, AWS, GCP, OCI etc. provide cloud based object storage where storage and compute are decoupled. You could store vast amounts of data without the need of buying compute.

Data Lakes are not a Data Modelling trend, but a general Data Management trend. And you still need to be on top of it! Be careful, when providing lake-like systems to your teams. Unmanaged data lakes and will lead to bad data management, resulting in compliance issues, data leaks, and wrong data. A data lake needs to be paired with: - strong pipeline automation - comprehensive metadata - comprehensive consistency check - approachable modelling layers - approachable data test layers - approachable data access management - full auditabiliy of all data access For the modelling, testing, and access management layer, tools like dbt can help a lot, exposing your raw input data as a consistent, secure, and correlated Data Model.

As the data strategies focus more on varied and large volumes of data, data lakes become important. Being Centralized regardless of format, accessibility becomes easy. Scalability at low cost becomes another important aspect.Data can be preserved in a more flexible format and in its native form. Flat architecture where each item has a unique identifier tagged with a set of metadata, makes it better managed.

Schema-on-read architectures within data lakes enable the storage of diverse and raw data without the need to define a schema upfront, providing flexibility for varied data types. Integration of metadata management and governance tools within data lakes ensures better discoverability and understanding of the data, enhancing data quality and compliance. Adoption of cloud-based data lakes allows for scalability and cost-effectiveness, enabling organizations to efficiently manage and analyze vast datasets. Leveraging serverless computing within data lakes facilitates on-demand processing, optimizing resource utilization and reducing operational costs.

I agree, ML based data modelling is an emerging trend but these models need human supervision before an actual model gets productionalized. As the model only knows as much as the data, they might lack practical components of decision making process. It is highly important to implement ethical AI checks and human involvement with AI based data modeling.