Hot, Warm & Cold: Data Tiering Strategies in MongoDB Atlas for Reducing Operational Costs

As data typically grows over time, keeping all data in Atlas can become cost prohibitive, forcing you to either purge data or ETL it out of your Atlas cluster to manage costs. This presentation will explore the available Data Tiering strategies in Atlas: using Atlas Data Lake or Atlas Online Archive for cold storage, which leverages cheap cloud object storage; or using Federated Queries to query two separate & heterogenous Atlas clusters as one, which is a newer and effective strategy for warm storage. You will learn the advantages and disadvantages of each strategy and when best to use them, as well as how to get started with implementing the approaches. You can keep all your data in Atlas — whether it's hot, warm or cold — and no longer have to fear data growth\!