Cassandra vs Pig: What are the differences?
Cassandra: 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; Pig: Platform for analyzing large data sets. Pig is a dataflow programming environment for processing very large files. Pig's language is called Pig Latin. A Pig Latin program consists of a directed acyclic graph where each node represents an operation that transforms data Operations are of two flavors: (1) relational-algebra style operations such as join, filter, project; (2) functional-programming style operators such as map, reduce. .
Cassandra and Pig are primarily classified as "Databases" and "Big Data" tools respectively.
Cassandra and Pig are both open source tools. Cassandra with 5.23K GitHub stars and 2.33K forks on GitHub appears to be more popular than Pig with 579 GitHub stars and 450 GitHub forks.
Uber Technologies, Facebook, and Spotify are some of the popular companies that use Cassandra, whereas Pig is used by Netflix, Outbrain, and Cobrain. Cassandra has a broader approval, being mentioned in 337 company stacks & 231 developers stacks; compared to Pig, which is listed in 9 company stacks and 4 developer stacks.
What is Cassandra?
What is Pig?
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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.
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