Cloud BigTable Reviews & Insights

We use Google Cloud BigTable to help us do the following:
1. Store aggregates
2. Build and keep lookup tables
3. Have a storage that can be looked up really fast to find duplicates
4. Given the large no. of txns it can handle, we use it for enriching our data from pipelines thus having rich information in the flows below.

It works great for any use case that requires high throughput for reads and writes, for key-value kinda data. This enables you to use it for usecases where you need to retain data for a long term or even short term but then use it in pipelines to enrich data, sync states between backend and frontend or to quickly lookup say a user.

Analytics for big data is our biggest use case for Google Cloud's BigTable platform. It is efficient enough to read, handle and analyse TBs of data under unbelievable times.

Majorly, we have a lot of data captured from customer interactions with our applications which is required to be processed and analysed for gaining benefits from those data sets. Now, this data is so huge as it is accumulated every minute from each applications. Our traditional systems cannot process them all that once, and even if we can do slotting and then anaylse them, it will take longer durations and with that new data is ready to be consumed. So as to solve this problem, we started working on BigTable which has huge capabilities and Google's analytical skills. Its efficient and most importantly - fast!

Google Bigtable is ONLY suited for massive data sets which scale PetaBytes and TerraBytes. Anything under this can easily be done via dedicated VMs and open source tools.
Google Bigtable is expensive and shall be used wisely. It should be utilised only where it is well suited else you would simply be wasting dollars and not utilizing its full benefits.