Cassandra vs Apache Spark: What are the differences?
What is 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.
What is Apache Spark? Fast and general engine for large-scale data processing. Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Cassandra belongs to "Databases" category of the tech stack, while Apache Spark can be primarily classified under "Big Data Tools".
"Distributed" is the top reason why over 96 developers like Cassandra, while over 45 developers mention "Open-source" as the leading cause for choosing Apache Spark.
Cassandra and Apache Spark are both open source tools. It seems that Apache Spark with 22.5K GitHub stars and 19.4K forks on GitHub has more adoption than Cassandra with 5.27K GitHub stars and 2.35K GitHub forks.
Uber Technologies, Facebook, and Spotify are some of the popular companies that use Cassandra, whereas Apache Spark is used by Uber Technologies, Slack, and Shopify. Cassandra has a broader approval, being mentioned in 342 company stacks & 240 developers stacks; compared to Apache Spark, which is listed in 266 company stacks and 112 developer stacks.
What is Cassandra?
What is Apache Spark?
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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
Spark is good at parallel data processing management. We wrote a neat program to handle the TBs data we get everyday.