Building Data Analytics Infrastructure with SMACK Stack

Building Big Data Analytics Infrastructure with SMACK Stack

Guide to Building Data Analytics Infrastructure with SMACK Stack using following terms –

  • S – stands for Apache Spark used for Batch Processing or Real-Time Data Streaming.
  • M – stands for Apache Mesos responsible for installation and administration.
  • A – stands for Akka for Data Streaming and Data Ingestion at a faster pace.
  • C – stands for Cassandra database to write and read the stored data.
  • K – stands for Apache Kafka to perform decoupling and reduction of overhead.

Challenge for Building the Analytics Platform

  • To power Scalable Real-Time & Data Driven Application.
  • Build a system of Real-Time insights to create new opportunities and deliver new value.
  • Ingest Data at a scale without loss.
  • Trigger actions based on the analyzed data, and store the data at Cloud-scale.

Solution Offered for Building Infrastructure with SMACK Stack

Propose SMACK Stack to overcome these challenges –

The SMACK stack to build modern enterprise apps because it performs each of these objectives with a loosely coupled toolchain of technologies that are all open source, and production-proven at scale.

  • Spark – A general engine for large-scale Data Processing, enabling analytics from SQL queries to Machine Learning, Graph Analytics, and Stream Processing.
  • Mesos – Distributed systems kernel that provides resourcing and isolation across all the other SMACK stack components. Mesos is the foundation on which other SMACK stack components run.
  • Akka – A toolkit and runtime to easily create concurrent and distributed apps that are responsive to messages.
  • Cassandra – Distributed database management system that can handle a large amount of data across servers with high availability.
  • Kafka – A high throughput, low-latency platform to handle Real-Time data feeds with no data loss.
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