When and When Not to Use Open Source Apache Cassandra, Kafka, Spark and Elasticsearch

Ben Slater | December 03, 2019

When and When Not to Use Open Source Apache Cassandra, Kafka, Spark and Elasticsearch
Just about all technology decision-making must meet two essential criteria: it must enable you to meet your business goals and it must work well alongside the rest of your technology stack. When it comes to selecting data-layer technologies to build out your application architecture, open source Apache Cassandra, Apache Kafka, Apache Spark, and Elasticsearch continue to rise in popularity.However, they’re not the right choice for every use case.Let’s take a deeper look at each of these technologies, and some of the use cases that are and are not advantageous applications of these open source solutions. Originally created by Facebook in 2007, Cassandra utilizes a Dynamo architecture and a Bigtable-style data model to provide a NoSQL data store that delivers high availability and high scalability. Cassandra is an ideal choice for use cases that require the highest levels of always-on availability. The database is also particularly well suited to serving organizations that anticipate massive workloads, or that wish to ensure that their services can grow flexibly as workloads expand (and thus need the easy scalability that Cassandra provides). Cassandra offers reliable data redundancy and active-active operations across multiple data centers.

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Non-perturbing kinetic measurements of proliferation (top graph) were obtained using the IncuCyte® System followed by downstream analysis of receptor expression by flow cytometry (bottom images) on the same cells. Non-invasive real-time live-cell imaging & analysis enables physiologically re


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