Myntra's use case of Aerospike for optimizing online feature stores for home page personalization. At the MLOps conference held between 23 and 27 July, Myntra’s data scientist - Sajan Kedia - explained how Myntra uses Aerospike to optimize online feature s… more
How do you select datastores and be aware of their limitations when applied to the problem at hand? Are there misconceptions you wish someone had cleared for you as you started on your journey of scaling with datastores?
Choosing data stores for your use cases conference will help you understand:
- Running datastores at scale - and tuning, debugging and operations.
- Solving specific use cases with a certain datastore.
- Data modelling and developer experience with datastore.
Senior infrastructure and software engineers from Farfetch, Aerospike, Zeotap, eightfold.ai, LinkedIn and Tesco engineering will share war stories and their learnings with practitioners in the audience.
View schedule at https://hasgeek.com/rootconf/choosing-datastores/schedule
Powering EMI Financing in ZestMoney with CQRS
Creating, editing, deleting and querying data related to merchant partners of ZestMoney and what EMI Financing options to allow for them was a major problem we solved at Zest. While doing a deeper analysis of the problem, we realised that business data entry and querying systems usually have different functional and non-functional requirements. The data creation and related updates are done by business users, usually during business hours. It is highly structured and interconnected data with inter-relationship among entities. While the data modelling of such systems is a challenge, it does not have to be highly available and is not expected to handle high loads. The querying system on the other hand, is required to have very low response times, scale to high loads and be highly available. What is intuitively CRUD, is practically two different sets of requirements, responsibilities and performance. This talk is about how we solved this problem using the CQRS - Command query responsibility segregation - pattern and the interesting data modelling that followed