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  1. Stackups
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  5. Apache Ignite vs Apache Spark

Apache Ignite vs Apache Spark

OverviewDecisionsComparisonAlternatives

Overview

Apache Spark
Apache Spark
Stacks3.1K
Followers3.5K
Votes140
GitHub Stars42.2K
Forks28.9K
Apache Ignite
Apache Ignite
Stacks110
Followers168
Votes41
GitHub Stars5.0K
Forks1.9K

Apache Ignite vs Apache Spark: What are the differences?

Introduction

In this article, we will discuss the key differences between Apache Ignite and Apache Spark. Both Apache Ignite and Apache Spark are widely used open-source frameworks for distributed computing and processing big data. While they share the common goal of processing large volumes of data, they have distinct features and use cases that set them apart. Let's delve into the key differences between the two:

  1. Storage and Processing Paradigm: Apache Ignite is an in-memory data fabric designed for high-performance processing and storage. It offers a distributed in-memory key-value store that allows fast data access and processing. On the other hand, Apache Spark is a general-purpose distributed processing framework that primarily focuses on data processing. It supports various storage systems and offers fault-tolerant distributed processing capabilities.

  2. Data Processing Model: Apache Ignite supports both batch and stream processing capabilities. It provides an SQL engine for querying data and offers processing APIs for stream processing scenarios. With Apache Ignite, you can build real-time processing pipelines and perform complex data transformations. In contrast, Apache Spark is primarily designed for batch processing, although it also has streaming capabilities. Spark provides a high-level API for defining batch processing workflows and offers powerful libraries for data analytics and machine learning.

  3. Fault Tolerance: Apache Ignite provides built-in fault tolerance mechanisms by replicating data across the cluster nodes. In case of a node failure, the data is automatically recovered and processed seamlessly without any interruption. Apache Spark, on the other hand, achieves fault tolerance through its resilient distributed dataset (RDD) abstraction. RDDs are immutable distributed collections that automatically recover lost data through lineage information.

  4. Data Ingestion and Integration: Apache Ignite offers robust integration capabilities with various data sources, such as databases, messaging systems, and file systems. It enables seamless data ingestion and synchronization between different data platforms. Apache Spark also provides integration with different data sources, but its primary focus is on big data processing from distributed storage systems like Hadoop Distributed File System (HDFS) and Apache HBase.

  5. In-Memory Computing: One of the core strengths of Apache Ignite is its in-memory computing capabilities. It allows you to store and process large datasets entirely in memory, resulting in extremely fast data access and processing speeds. Apache Spark, on the other hand, leverages memory for caching intermediate data but is not exclusively an in-memory computing engine. It provides a disk-based storage option for storing and processing large volumes of data.

  6. Ecosystem and Community: Both Apache Ignite and Apache Spark have vibrant and active communities. However, Apache Spark has a more extensive ecosystem with a rich set of libraries and tools for various use cases like streaming, machine learning, and graph processing. Apache Ignite, being a newer framework, has a smaller ecosystem but is rapidly growing and evolving.

Summary

In summary, while both Apache Ignite and Apache Spark are powerful frameworks for distributed processing and big data analytics, they have distinct differences in terms of storage and processing paradigm, data processing model, fault tolerance mechanisms, data ingestion and integration capabilities, in-memory computing capabilities, and ecosystem and community support.

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Advice on Apache Spark, Apache Ignite

Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k
Comments

Detailed Comparison

Apache Spark
Apache Spark
Apache Ignite
Apache Ignite

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

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

Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
Memory-Centric Storage; Distributed SQL; Distributed Key-Value
Statistics
GitHub Stars
42.2K
GitHub Stars
5.0K
GitHub Forks
28.9K
GitHub Forks
1.9K
Stacks
3.1K
Stacks
110
Followers
3.5K
Followers
168
Votes
140
Votes
41
Pros & Cons
Pros
  • 61
    Open-source
  • 48
    Fast and Flexible
  • 8
    One platform for every big data problem
  • 8
    Great for distributed SQL like applications
  • 6
    Easy to install and to use
Cons
  • 4
    Speed
Pros
  • 5
    Free
  • 5
    High Avaliability
  • 5
    Multiple client language support
  • 5
    Written in java. runs on jvm
  • 4
    Rest interface
Integrations
No integrations available
MongoDB
MongoDB
MySQL
MySQL

What are some alternatives to Apache Spark, 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.

Presto

Presto

Distributed SQL Query Engine for Big Data

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.

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

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.

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

SAP HANA

SAP HANA

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.

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