Thanks for the reply, but neither the article nor the comments are particularly helpful to me.
For instance this from the comment you highlighted:
“The emerging architecture that’s replacing old Oracle stacks is MongoDB + … Unless another technology disrupts it, MongoDB will have a long, long runway of growth ahead of it.”
That Mongo is replacing other databases shows that Mongo is itself replaceable. And my question is precisely whether Snowflake is that disruptive technology.
Yes, I know today the emphasis is on integrations with Mongo at the front-end for data input and then periodic transfer of data to Snowflake (Snowflake had a blog post on this over 4 years ago), but I can’t help but feel that Snowflake doesn’t actually need something as heavy-weight as Mongo for a front-end.
And while Mongo is a great NoSQL database, the data prep processing times in order to perform analytics (Map Reduce, anyone?) are often not tolerable in today’s faster paced world. Snowflake has a real advantage there. And Snowflake supports traditional relational databases as well as it supports unstructured, NoSQL-style data. So things like ACID don’t need special handling like they do in Mongo. And while MongoDb is NoSQL (only), it wasn’t really built for the very large datasets that Hadoop was, but for which Snowflake is, in my limited understanding, clearly better.
Hadoop vs Snowflake: https://community.snowflake.com/s/article/Hadoop-Vs-Snowflak…
Snowflake’s ability to handle document-oriented data (Mongo’s specialty) is described in gory detail here: https://www.snowflake.com/wp-content/uploads/2015/06/Snowfla… It uses what they call a “schema-on-read” technology which differs in that the schema is defined in the SQL statement itself. So you get the benefits of storing JSON (simply tags) data with the benefits of using SQL statements for access.
Here’s Snowflake’s summary: Snowflake’s architecture makes it possible to query semi-structured data and structured data together using SQL. You can join, window, compare and calculate structured and semi-structured data in a single query. This makes it possible to eliminate extra systems and steps while realizing superior performance, simplifying data pipelines and reducing the time from when data is generated to when it can be accessed and analyzed.
In layman’s terms, Snowflake gives a single place (Data Lake) to store all your data for quick access.
Here’s 2.5 minute video to watch: https://www.snowflake.com/workloads/data-lake/?wvideo=eudbyp…
So, my question is: given that MongoDB’s future is its Atlas cloud product, why would developers not choose the cloud-first, more data structure agnostic, and better speed performing Snowflake database instead, and what does that mean for MongoDB’s future?