Apache Cassandra vs. Google Cloud Bigtable

Cassandra excels in a broad range of applications -- especially if you understand its data model and write your applications accordingly. It's an excellent choice for time-series data, and a poor choice for application queues. It performs the best if you can simply record history and compute from it, rather than going back and editing or deleting things a lot.

Incentivized

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.

Incentivized

• High Availability - we utilize the data replication features of Cassandra. This enables us to access our data even when several nodes have gone down
• Data Locality - our architecture combines Cassandra storage nodes and computation nodes in the same machine. This enables us to utilize data locality and limit expensive network IO to read data.
• Elasticity - Cassandra is a shared nothing architecture. Nodes can be added very easily and they discover the network topology. As soon as a node has joined the Cassandra ring, the data is redistributed among the existing nodes and streamed to it automatically.

Incentivized

• Analytics: is at Google's heart. No on can beat Google in this space and BigTable is one of its implementation of this. The insights you gain from BigTable are simply usable in your day to day activities and can help you make real difference.
• Speed: Processing TBs and PBs of data under minutes needs real efficient platform which is capable of doing much more than just processing data. All this data cannot be processed by a single machine, but rather huge pairs of machines working in conjuction with each other. BigTable's implementation is one of the finest and allows you achieve great speeds!
• Interface: is great. Google has segregated required task under logically placed buttons which takes no time by users to understand and get habituated.

Incentivized

• No Ad-Hoc Queries: Cassandra data storage layer is basically a key-value storage system. This means that you must "model" your data around the queries you want to surface, rather than around the structure of the data itself.
• There are no aggregations queries available in Cassandra.
• Not fit for transactional data.

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• Global replication
• Complete SQL support
• Size limit of 1MB

Incentivized

I would recommend Cassandra DB to those who know their use case very well, as well as know how they are going to store and retrieve data. If you need a guarantee in data storage and retrieval, and a DB that can be linearly grown by adding nodes across availability zones and regions, then this is the database you should choose.

Incentivized

It’s great tool but it can be complicated when it comes administration and maintenance.

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For big IT firms like us, data is very important and it only holds its value if it can make sense to us. Therefore, Bigtable's usability is priceless when it comes to decision making based on data.

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Sometimes instead giving straight answer, we ‘re getting transfered to talk professional service.

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Google provides premium support services for BigTable which is absolutely blazing fast similar to Bigtable's performance.

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Apache Cassandra has the best of both worlds, it is a Java based NoSQL, linearly scalable, best in class
tunable performance across different workloads, fault tolerant, distributed, masterless, time series database. We have used both Apache HBase and MongoDB for some use cases which were within hadoop setup and JSON (JavaScript Object Notation) document store respectively, but given the overall factors favoring Apache Cassandra, it is a technology choice for multiple platforms!

Incentivized

As a hosted solution, that can be managed easily from the Google cloud platform, it enables the teams to think about business use cases vs thinking about the management of the database itself. It is easily to scale it up when you need more throughput or storage. This keeps the developers and the product folks happy.

Incentivized

• The open source version of Cassandra is only suggested for learning the basic concepts and play with its core features. Unless you really want to invest a lot in your developers and architects knowing every detail of Cassandra, I prefer the DataStax enterprise version. Although the license cost is relatively high, I think they it is worth it. I'm thinking about the support, the monitoring tool OpsCenter, and the integration of Solr and Spark (for data analysis).
• Cassandra didn't fully replace our old and traditional relation database Oracle. In addition, it opens another door for us to deal with some special business use cases that NoSQL database can do better in a more feasible and efficient way.

Incentivized

• Building new features that can leverage storage on the edge
• Realtime decision making
• Data enrichment

Incentivized