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For me, there are only two products in the market which are Databricks and Snowflake Data Cloud [Private Offer Only]. Working on either one of them would give me recognition in the market. Databricks works excellently in certain situations, and Snowflake Data Cloud [Private Offer Only] works excellently in certain situations, whether it is high frequency, low latency, or low frequency and high latency type of data pipelines with different batch processing requirements. Based on this type of requirement, you either recommend a Snowflake or recommend a Databricks solution. Before you actually recommend, you also go with a pilot or an MVP which will justify the need. You go with a set assumption and then determine whether Snowflake or Databricks would work excellently for the customer's situation. It has to be done with an evaluation, a discovery, and a pilot, which will help the customer either pick Databricks or Snowflake Data Cloud [Private Offer Only]. Data sharing is important as far as if it is a multi-node or a multi-nation where there is fiduciary involved. You keep certain data inside the boundary of a country versus certain types of data can be shared across, even if it is the same company working across different geographies. Data sharing and data clean room definitely help a lot, and Databricks is ahead in this direction. Machine learning is not something you do once and then forget about it, forgetting about whether you need to maintain, re-tweak, or retune the model. Machine learning helps in arriving at a decision, but it does not mean that there is no follow-up. There will be a follow-up. Whenever there is a data drift, your outcome keeps changing. When the outcome changes, you need to work on the inventory and retool the algorithm and redeploy. This is what we call model risk assessment or a model risk framework. Machine learning helps, but there will be some sort of framework to put governance on it. Data enrichment does help to improve strategic planning. Data enrichment is all about dealing with missing data, rounding off errors, and coming up with median and average of different types of data. That will give you a wholesome view into the missing data of, for example, customers, suppliers, or vendors. For example, if you have 10 people and for nine or seven of them there is data missing in a feature set, then you will have a way to fill them up and still create a rounded view so that you can take decisions. In this way, it helps strategic decisions. Role-based and row-based security are both important in terms of data security. Both help keep the data privacy. One example is the salary of my peer. I should not be able to see it unless I am their supervisor. These measures help in data protection. Concurrency is a two-pronged strategy. One is you can increase the capability where you house the cloud. The other one is how you manage it within the software. Both are important, and Snowflake has done a decent job in helping concurrency.
