Building Big Data Analytics Infrastructure with SMACK Stack
Guide to Building Data Analytics Infrastructure with SMACK Stack using the 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
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
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.
A general engine for large-scale Data Processing, enabling analytics from SQL queries to Machine Learning, Graph Analytics, and Stream Processing.
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.