Apache Geode vs. Google Cloud Datastore

The biggest advantage of using Apache Geode is DB like consistency. So for applications whose data needs to be in-memory, accessible at low latencies and most importantly writes have to be consistent, should use Apache Geode. For our application quite some amount of data is static which we store in MySQL as it can be easily manipulated. But since this data is large R/w from DB becomes expensive. So we started using Redis. Redis does a brilliant job, but with complex data structures and no query like capability, we have to manage it via code. We are experimenting with Apache Geode and it looks promising as now we can query on complex data-structures and get the required data quickly and also updates consistent.

Incentivized

Using Google Cloud Datastore in conjunction with Google AppEngine was a very seamless integration and much easier than using other datastores since so much of the configuration is abstracted for you. Because of this, creating simple applications is very easy and getting Google Cloud Datastore to power the backend ties everything together. If we were using Google Compute Engine, I'd imagine the same seamless experience would be there as well.

Incentivized

• Super Fast data pull/push
• Provided ACID transactions, so it works like a SQL Database
• Provides replication & partitioning, so our data is never lost and extraction is super fast. NoSql like properties

Incentivized

• It is hosted in GCP, our primary cloud provider.
• It integrates with all the services we use in GCP.

Incentivized

• Needs more supporting languages. Out of box Python, Nodejs adapters would be wonderful
• Currently it supports just KV Store. But if we could cache documents or timeseries data would be great
• Needs more community support, documentation.

Incentivized

• Part of the Google Cloud Platform walled garden.
• Pay for read/writes to the datastore, in addition to storage.
• Not many libraries exist.

Incentivized

I give Google Cloud a full score because it satisfies our needs so well. We host most of our infrastructure on Google Cloud and using Google Cloud Datastore helps us to solve our NoSQL storage problem. and Google Cloud Datastore is so scalable and elastic. It saves us lots of time to maintain and saves us money.

Incentivized

Still Experimenting. Initial results are good. we need to figure out if we can completely replace Redis. Cost wise if it makes sense to keep both or replacement is feasible.

Incentivized

Never contacted support

Incentivized

Still Experimenting. But looks promising as it has query capabilities over complex data structures

Incentivized

We selected Google Cloud Datastore as one of our candidates for our NoSQL data is because it is provided by Google Cloud, which fits our needs. Most of our infrastructure is on Google Cloud, so when we think about the NoSQL database, the first thing we thought about is Google Cloud Datastore. And it proves itself.

Incentivized

• Still experimenting so difficult to quote
• For a small size project/teams might be an overkill as it still has certain learning curve
• For Medium to large projects with complex Data Structures that need to be queried with a fast o/p it definitely works

Incentivized

• Simple billing part of Google Cloud Platform
• No time spent configuring and maintaining Google Cloud Datastore.
• Very good uptime for our applications.

Incentivized