Apache Spark vs Neo4j

Overview

Neo4j

Stacks1.2K

Followers1.4K

Votes351

GitHub Stars15.3K

Forks2.5K

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Spark vs Neo4j: What are the differences?

Introduction

In this article, we will compare Apache Spark and Neo4j, focusing on their key differences. Apache Spark is a fast and general-purpose cluster computing system, while Neo4j is a graph database management system. Both have their unique features and use cases, which we will explore in the following sections.

Data Processing Paradigm: Apache Spark is primarily designed for large-scale data processing tasks, providing support for batch processing, real-time streaming, machine learning, and graph processing. It follows the distributed computing model, executing operations on distributed data sets. On the other hand, Neo4j is a graph database, built specifically for managing highly connected data. It excels in querying and traversing relationships within the graph, making it suitable for applications requiring complex data relationships.
Data Model: Apache Spark operates on the Resilient Distributed Dataset (RDD) abstraction, which allows for fault-tolerant, distributed data processing. RDDs are immutable and partitioned across multiple nodes in a cluster. On the contrary, Neo4j uses a property graph model, where entities (nodes) and relationships between them are represented as first-class constructs. This graph-specific model allows for efficient traversal and querying of relationships.
Query Language: Apache Spark offers multiple query languages, including Spark SQL for structured querying, DataFrame API for a programmatic interface, and GraphFrames for graph processing. These languages provide SQL-like syntax for querying data. On the other hand, Neo4j uses the Cypher query language, specifically designed for graph database management. Cypher offers an expressive, pattern-matching syntax that allows querying and traversing the graph using simple and intuitive commands.
Scalability: Apache Spark is highly scalable, as it can distribute computations across a cluster of machines. It leverages a master-worker architecture, where the driver program coordinates tasks across different worker nodes. It can handle big data processing with ease. Neo4j is also scalable, but its scalability is more limited compared to Spark. It can handle large graphs and a decent number of connections, but its performance might degrade significantly with extremely large-scale datasets.
Use Cases: Apache Spark is widely used in various domains, such as data analytics, machine learning, and stream processing. It is suitable for scenarios requiring large-scale data processing, real-time analytics, and iterative algorithms. On the other hand, Neo4j is commonly used for applications involving complex data relationships, such as social networks, fraud detection, recommendation systems, and graph-based analytics. It excels in querying and manipulating highly connected data.
Community Ecosystem: Apache Spark has a flourishing community, with a wide range of third-party libraries, tools, and integrations available. It is one of the most active open-source projects in the big data ecosystem. Neo4j also has a strong community support, but its ecosystem is more focused on graph-related tools and integrations. It has a dedicated marketplace for plugins and extensions.

In summary, Apache Spark and Neo4j serve different purposes in the data processing landscape. Apache Spark is a versatile and scalable framework for large-scale data processing, while Neo4j is a powerful tool for managing highly connected data and querying complex relationships within a graph.

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Advice on Neo4j, Apache Spark

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

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.

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Detailed Comparison

Neo4j	Apache Spark
Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.	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.
intuitive, using a graph model for data representation;reliable, with full ACID transactions;durable and fast, using a custom disk-based, native storage engine;massively scalable, up to several billion nodes/relationships/properties;highly-available, when distributed across multiple machines;expressive, with a powerful, human readable graph query language;fast, with a powerful traversal framework for high-speed graph queries;embeddable, with a few small jars;simple, accessible by a convenient REST interface or an object-oriented Java API	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
Statistics
GitHub Stars 15.3K	GitHub Stars 42.2K
GitHub Forks 2.5K	GitHub Forks 28.9K
Stacks 1.2K	Stacks 3.1K
Followers 1.4K	Followers 3.5K
Votes 351	Votes 140
Pros & Cons
Pros 69 Cypher – graph query language 61 Great graphdb 33 Open source 31 Rest api 27 High-Performance Native API Cons 9 Comparably slow 4 Can't store a vertex as JSON 1 Doesn't have a managed cloud service at low cost	Pros 61 Open-source 48 Fast and Flexible 8 Great for distributed SQL like applications 8 One platform for every big data problem 6 Easy to install and to use Cons 4 Speed

What are some alternatives to Neo4j, Apache Spark?

Presto

Distributed SQL Query Engine for Big Data

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.

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

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 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.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Related Comparisons

Apache Spark vs Neo4j: What are the differences?

Introduction

Data Processing Paradigm: Apache Spark is primarily designed for large-scale data processing tasks, providing support for batch processing, real-time streaming, machine learning, and graph processing. It follows the distributed computing model, executing operations on distributed data sets. On the other hand, Neo4j is a graph database, built specifically for managing highly connected data. It excels in querying and traversing relationships within the graph, making it suitable for applications requiring complex data relationships.
Data Model: Apache Spark operates on the Resilient Distributed Dataset (RDD) abstraction, which allows for fault-tolerant, distributed data processing. RDDs are immutable and partitioned across multiple nodes in a cluster. On the contrary, Neo4j uses a property graph model, where entities (nodes) and relationships between them are represented as first-class constructs. This graph-specific model allows for efficient traversal and querying of relationships.
Query Language: Apache Spark offers multiple query languages, including Spark SQL for structured querying, DataFrame API for a programmatic interface, and GraphFrames for graph processing. These languages provide SQL-like syntax for querying data. On the other hand, Neo4j uses the Cypher query language, specifically designed for graph database management. Cypher offers an expressive, pattern-matching syntax that allows querying and traversing the graph using simple and intuitive commands.
Scalability: Apache Spark is highly scalable, as it can distribute computations across a cluster of machines. It leverages a master-worker architecture, where the driver program coordinates tasks across different worker nodes. It can handle big data processing with ease. Neo4j is also scalable, but its scalability is more limited compared to Spark. It can handle large graphs and a decent number of connections, but its performance might degrade significantly with extremely large-scale datasets.
Use Cases: Apache Spark is widely used in various domains, such as data analytics, machine learning, and stream processing. It is suitable for scenarios requiring large-scale data processing, real-time analytics, and iterative algorithms. On the other hand, Neo4j is commonly used for applications involving complex data relationships, such as social networks, fraud detection, recommendation systems, and graph-based analytics. It excels in querying and manipulating highly connected data.
Community Ecosystem: Apache Spark has a flourishing community, with a wide range of third-party libraries, tools, and integrations available. It is one of the most active open-source projects in the big data ecosystem. Neo4j also has a strong community support, but its ecosystem is more focused on graph-related tools and integrations. It has a dedicated marketplace for plugins and extensions.

Apache Spark vs Neo4j

Overview

Apache Spark vs Neo4j: What are the differences?