With the advent of Big Data, the enterprise applications nowadays are following a Data Intensive microservices based enterprise application architecture deviating more monolithic architectures, which we have been used to decades.

These data intensive applications should meet a set of requirements.

1. Ingest Data at Scale without a loss

2. Analyze data in real-time

3. Trigger action based on the analyzed data

4. Store the data at cloud-scale.

5. Need to run in a distributed and highly resilient cloud platform

The SMACK is such a stack, which can be used for building modern enterprise applications because it can performs each of the above objectives with a loosely coupled tool chain of technologies that are are all open source, and production-proven at scale.

(S – Spark, M – Mesos, A – Akka, C – Cassendra, K – Kafka)

  • 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 large amounts of data across servers with high availability.
  • Kafka – A high throughput, low-latency platform for handling real-time data feeds with no data loss.
SMACK Stack

SMACK Stack

Source: www.mesosphere.com

SMACK Data Pipeline

Source: www.mesosphere.com

The following commercial options available for some of the components of SMACK.
1. Spark – Lightbend and Databricks
2, Cassendra – DataStax
3. Kafka – Confluent
4. Mesos – Mesosphere DC/OS
References:
1. Building Data-Rich apps with “SMAL” stack – https://www.youtube.com/watch?v=jV1WsUuQNck
2. The SMACK Stack is the new LAMP Stack – https://mesosphere.com/blog/smack-stack-new-lamp-stack/
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