Cassandra vs Hazelcast: What are the differences?
Developers describe Cassandra as "A partitioned row store. Rows are organized into tables with a required primary key". Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL. On the other hand, Hazelcast is detailed as "Clustering and highly scalable data distribution platform for Java". With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.
Cassandra belongs to "Databases" category of the tech stack, while Hazelcast can be primarily classified under "In-Memory Databases".
"Distributed" is the primary reason why developers consider Cassandra over the competitors, whereas "High Availibility" was stated as the key factor in picking Hazelcast.
Cassandra and Hazelcast are both open source tools. It seems that Cassandra with 5.23K GitHub stars and 2.33K forks on GitHub has more adoption than Hazelcast with 3.15K GitHub stars and 1.15K GitHub forks.
According to the StackShare community, Cassandra has a broader approval, being mentioned in 337 company stacks & 230 developers stacks; compared to Hazelcast, which is listed in 25 company stacks and 15 developer stacks.
What is Cassandra?
What is Hazelcast?
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Stitch is a wrapper around a Cassandra database. It has a web application that provides read-access to the counts through an HTTP API. The counts are written to Cassandra in two distinct ways, and it's possible to use either or both of them:
Real-time: For real-time updates, Stitch has a processor application that handles a stream of events coming from a broker and increments the appropriate counts in Cassandra.
Batch: The batch part is a MapReduce job running on Hadoop that reads event logs, calculates the overall totals, and bulk loads this into Cassandra.
HazelCast is the foundation for the distributed system that hosts our APIs and intelligent workflows. We wrap the core HazelCast functions in Clojure protocols to implement micro-services on top of a coherent, single-process instance per virtual node.
Cassandra is our data management workhorse. It handles all our key-value services, supports time-series data storage and retrieval, securely stores all our audit trails, and backs our Datomic database.
While we experimented with Cassandra in the past, we are no longer using it. It is, however, open for consideration in future projects.
We are using Cassandra in a few of our apps. One of them is as a count service application to track the number of shares, clicks.. etc