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  1. Stackups
  2. Application & Data
  3. In-Memory Databases
  4. In Memory Databases
  5. Apache Ignite vs SAP HANA

Apache Ignite vs SAP HANA

OverviewComparisonAlternatives

Overview

SAP HANA
SAP HANA
Stacks167
Followers148
Votes27
Apache Ignite
Apache Ignite
Stacks110
Followers168
Votes41
GitHub Stars5.0K
Forks1.9K

Apache Ignite vs SAP HANA: What are the differences?

Introduction

Apache Ignite and SAP HANA are both in-memory computing platforms, but they have key differences that set them apart. Here are six specific differences between the two:

  1. Data Storage Approach: Apache Ignite is an in-memory data grid that stores data in a distributed manner across multiple nodes. It can persist data on disk and supports various persistence options, including RDBMS and NoSQL databases. SAP HANA, on the other hand, is an in-memory database that stores and processes data in memory without any need for disk storage. It offers high-performance data processing capabilities optimized for large-scale analytics and transactional workloads.

  2. Scalability: Apache Ignite's architecture is designed for horizontal scalability, allowing it to scale out by adding more nodes to the cluster. It can distribute data and processing across multiple nodes, enabling it to handle large datasets and high loads. SAP HANA also supports scaling out, but it has its limits compared to Ignite. While it can scale horizontally to some extent, it is primarily built for vertical scalability, utilizing the power of a single large server.

  3. Supported Programming Languages: Apache Ignite provides support for multiple programming languages, including Java, .NET, C++, and more. This allows developers to use their preferred programming language to interact with Ignite. SAP HANA, on the other hand, primarily supports SQL-based programming, though it also provides Python and JavaScript APIs for certain use cases.

  4. Data Processing Capabilities: Apache Ignite is known for its distributed computing capabilities, allowing users to perform real-time data processing, querying, and computation on large datasets distributed across multiple nodes. It offers a wide range of built-in distributed processing frameworks and tools, including SQL querying, machine learning, and streaming. While SAP HANA also provides powerful querying and analytics capabilities, it is primarily focused on high-performance transaction processing and analytics rather than distributed computing.

  5. Deployment Flexibility: Apache Ignite can be deployed in various ways, including standalone mode, embedded within applications, or as part of a microservices architecture. It can run on commodity hardware and supports cloud deployments. SAP HANA, on the other hand, is typically deployed as a dedicated database system on specialized hardware, optimized for high-performance and scalability. It is commonly used as an enterprise-grade data platform for mission-critical applications.

  6. Open Source vs. Proprietary: Apache Ignite is an open-source project, providing users with transparency, extensibility, and a vibrant community of contributors. It offers flexibility and customization options for developers. SAP HANA, on the other hand, is a proprietary solution developed and maintained by SAP. While it provides comprehensive support and features tailored for enterprise needs, it may have limitations in terms of extensibility and customization compared to an open-source solution like Ignite.

In summary, Apache Ignite and SAP HANA differ in terms of their data storage approach, scalability options, supported programming languages, data processing capabilities, deployment flexibility, and licensure model. Apache Ignite focuses on distributed computing, scalability, and open-source flexibility, while SAP HANA prioritizes high-performance transaction processing, analytics, and enterprise-grade solutions.

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Detailed Comparison

SAP HANA
SAP HANA
Apache Ignite
Apache Ignite

It is an application that uses in-memory database technology that allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows it to process data stored in RAM as opposed to reading it from a disk.

It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale

processes transactions and analytics at the same time; built-in advanced analytics and multi-model data processing engines
Memory-Centric Storage; Distributed SQL; Distributed Key-Value
Statistics
GitHub Stars
-
GitHub Stars
5.0K
GitHub Forks
-
GitHub Forks
1.9K
Stacks
167
Stacks
110
Followers
148
Followers
168
Votes
27
Votes
41
Pros & Cons
Pros
  • 5
    SQL
  • 5
    In-memory
  • 4
    Performance
  • 4
    Distributed
  • 2
    OLAP
Pros
  • 5
    Free
  • 5
    Written in java. runs on jvm
  • 5
    Multiple client language support
  • 5
    High Avaliability
  • 4
    Rest interface
Integrations
Python
Python
Power BI
Power BI
Tableau
Tableau
MongoDB
MongoDB
MySQL
MySQL
Apache Spark
Apache Spark

What are some alternatives to SAP HANA, Apache Ignite?

Redis

Redis

Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.

Hazelcast

Hazelcast

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.

Aerospike

Aerospike

Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.

MemSQL

MemSQL

MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.

VoltDB

VoltDB

VoltDB is a fundamental redesign of the RDBMS that provides unparalleled performance and scalability on bare-metal, virtualized and cloud infrastructures. VoltDB is a modern in-memory architecture that supports both SQL + Java with data durability and fault tolerance.

Tarantool

Tarantool

It is designed to give you the flexibility, scalability, and performance that you want, as well as the reliability and manageability that you need in mission-critical applications

Azure Redis Cache

Azure Redis Cache

It perfectly complements Azure database services such as Cosmos DB. It provides a cost-effective solution to scale read and write throughput of your data tier. Store and share database query results, session states, static contents, and more using a common cache-aside pattern.

KeyDB

KeyDB

KeyDB is a fully open source database that aims to make use of all hardware resources. KeyDB makes it possible to breach boundaries often dictated by price and complexity.

LokiJS

LokiJS

LokiJS is a document oriented database written in javascript, published under MIT License. Its purpose is to store javascript objects as documents in a nosql fashion and retrieve them with a similar mechanism. Runs in node (including cordova/phonegap and node-webkit), nativescript and the browser.

BuntDB

BuntDB

BuntDB is a low-level, in-memory, key/value store in pure Go. It persists to disk, is ACID compliant, and uses locking for multiple readers and a single writer. It supports custom indexes and geospatial data. It's ideal for projects that need a dependable database and favor speed over data size.

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