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In-Memory Analytics Solution with Apache Ignite

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Getting Started with In-Memory Analytics

  • Data Generation is happening at exponential rate and Industry (retailers, telco-providers, security agencies, scientists, etc.) needs more insights from the data for business values & customer interests in Real-Time and high-performance solutions.
  • Advance Analytics requires Deep Analytics across all data sources to capture 360-degree view for business insights.
  • Deep and Real-Time Analytics require In-Memory Solutions across databases - MySQL or other RDBMS Databases and Big Data Platform using Apache Spark, Hadoop with Scalable architecture.

Apache Ignite Overview

Apache Ignite, an in-memory computing platform which is strongly consistent, durable and highly available with access to powerful SQL, key-value, and processing APIs. It is an in-memory database that provides a variety of integration with existing technologies such as Cassandra, Hadoop, Spark, etc.

Challenges for Building In Memory Analytics Platform

Need In-Memory solutions for two cases -
  • To improve the performance of Healthcare application with .NET and SQL Server Architecture.
  • Perform fast analytical queries on Apache Spark and Hadoop based community Data Warehouse.
  • Perform both Online Transactional Processing (OLTP) and Online Analytical Processing (OLAP) on workloads distributed among various database stores such as RDBMS, NoSQL, and Hadoop.
  • Real-Time Analytics and Query platform with high performance and consistency.

Solution Offerings for In-Memory Database

Apache Ignite for In-Memory Database and with Apache Spark and Hadoop.

First Use Case

Use Apache Ignite as an In-Memory database to improve the performance of SQL queries with Ignite In-Memory data fabric for .NET. For ACD transactions, SQL Queries and distributed SQL joins.

Second Use Case

Use Apache Ignite as IGFS and shared memory layer Spark RDD using Ignite RDD and build Analytics Dashboard using Play framework to interact with Apache Ignite using its API. The dashboard provisions user to upload semi-structured data in various formats such as CSV, JSON, etc. and run analytical queries.

Guide to In-Memory Computing Platform Architecture

In-Memory computing platforms provide extensive in-memory data management, in-memory data grid, in-memory database, support for streaming analytics, Machine Learning and Deep Learning. All data used by the application is stored by the main memory of the computing environment increasing revenue, risk management, new product innovation.

In-Memory Analytics with Apache Ignite

  • Massive Scale Data Processing of Streaming Data
  • In-Memory Support for Batch and Real-Time Streaming Data
  • Support for Distributed SQL Joins
  • Affinity Collocation
  • State Management

In-Memory Analytics with Apache Spark

  • Data Processing without data storage
  • Low Latency computations
  • Efficient iterative algorithms
  • Interactive Data Analysis
  • Batch Processing at a faster pace
  • Unified Pipelines
  • Faster Decision Making
  • Real-Time Stream Processing

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