Apache HBase vs. Azure Cosmos DB

HBase is well suited for streaming ingest, fast lookups, massive datasets, data warehouse lookup tables, RDBMS replacement, MongoDB replacement, key-value store, data scans, logs, JSON storage and some binary storage. My preferred use case is for storing data points like time series or data produced by sensors. I often use HBase when I need data available immediately and I am not looking for transactions. This is a great store for really wide tables with tons of columns. It is also great if you are not sure what type of data you are going to have. It really excels at sparse data.

Incentivized

NoSQL platforms are very useful when it comes to security, speed, accuracy, high accessibility with high read and write power. Everything is managed under the cloud and we have the various capabilities of Azure and support for Microsoft products with us. Flexibility in price and variety of features, as well as real-time results, are some of the popular [features] of this platform.

Incentivized

• Scalable and truly non-relational data
• HBase operations run in real-time on its database rather than MapReduce jobs
• Scales linearly to support billions of rows with millions of columns

Incentivized

• Turn-key geo-redundancy with multi-master writes is unprecedented and unparalleled in the industry!
• Guaranteed low latency makes Cosmos DB an excellent fit for most of our performance-intensive situations.
• The tunable consistency model simplifies so many challenges in distributed systems engineering that otherwise require advanced knowledge of computer science topics. I continue to be impressed at how Cosmos DB has abstracted away so much complexity.

Incentivized

• Write performance
• Performance support for parquet file format. supports, but performance wise still not there
• API / library availability for spark, rather than creating a new library for it

Incentivized

• When searching by default, it is case sensitive, which must be changed by default
• In many ways, the price should be more flexible according to the requested facilities, because the price is very expensive for startup companies.
• It is not fully compatible with most common Streaming Analytics tools applications and developers should be worked on it

Incentivized

There's really not anything else out there that I've seen comparable for my use cases. HBase has never proven me wrong. Some companies align their whole business on HBase and are moving all of their infrastructure from other database engines to HBase. It's also open source and has a very collaborative community.

Incentivized

It's efficient, easy to scale, and works. We do have to do a bit of administration, but less now than when we started with this a couple of years ago. Microsoft continues to improve its self-management capability.

Incentivized

Like I said, Cosmos is the way to go. From all of the services that Azure has, Cosmos is very robust in terms of usability. It's ever-evolving and integrates with other applications seamlessly. The interface is pretty easy to understand. I implemented various solutions for my company and Cosmos was one of them.

Incentivized

The support team is very responsive and we are generally satisfied with Microsoft support, in my opinion support team of a product and service is just as valuable as its quality and performance. Telephone answering, 24-hour hotline, email support and ticketing are excellent.

Incentivized

Compared NoSQL databases with traditional databases for faster retrieval and consistency. As MongoDB is a NoSQL supports dynamic fields, however, query performance is bad for aggregations and added maintenance. When compared with MySQL and Teradata, it could not scale up as fast as Hbase and added cost involved to it. HBase can be easily scalable to a huge volume of records, have a faster lookup and provides consistency

Incentivized

Azure Cosmos DB for MongoDB is more affordable than many other solutions and works incredibly well if you're within the Azure ecosystem.

Incentivized

• Positive: Open source, easy to use, good to store big data.
• Negative: SQL functionalities are not available.
• More memory utilization
• More troubleshooting

Incentivized

• Expensive but works if your infra is on Azure data center.
• No latency and nearly no downtime.
• Takes time for end users to adapt.

Incentivized