SMACK Stack for building Data Intensive Enterprise Applications
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.
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