Amazon EMR vs Neo4j

Overview

Amazon EMR

Stacks543

Followers682

Votes54

Neo4j

Stacks1.2K

Followers1.4K

Votes351

GitHub Stars15.3K

Forks2.5K

Amazon EMR vs Neo4j: What are the differences?

Developers describe Amazon EMR as "Distribute your data and processing across a Amazon EC2 instances using Hadoop". Amazon EMR is used in a variety of applications, including log analysis, web indexing, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics. Customers launch millions of Amazon EMR clusters every year. On the other hand, Neo4j is detailed as "The world’s leading Graph Database". 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.

Amazon EMR can be classified as a tool in the "Big Data as a Service" category, while Neo4j is grouped under "Graph Databases".

Some of the features offered by Amazon EMR are:

Elastic- Amazon EMR enables you to quickly and easily provision as much capacity as you need and add or remove capacity at any time. Deploy multiple clusters or resize a running cluster
Low Cost- Amazon EMR is designed to reduce the cost of processing large amounts of data. Some of the features that make it low cost include low hourly pricing, Amazon EC2 Spot integration, Amazon EC2 Reserved Instance integration, elasticity, and Amazon S3 integration.
Flexible Data Stores- With Amazon EMR, you can leverage multiple data stores, including Amazon S3, the Hadoop Distributed File System (HDFS), and Amazon DynamoDB.

On the other hand, Neo4j provides the following key features:

intuitive, using a graph model for data representation
reliable, with full ACID transactions
durable and fast, using a custom disk-based, native storage engine

"On demand processing power" is the primary reason why developers consider Amazon EMR over the competitors, whereas "Cypher – graph query language" was stated as the key factor in picking Neo4j.

Neo4j is an open source tool with 6.61K GitHub stars and 1.63K GitHub forks. Here's a link to Neo4j's open source repository on GitHub.

According to the StackShare community, Neo4j has a broader approval, being mentioned in 114 company stacks & 47 developers stacks; compared to Amazon EMR, which is listed in 95 company stacks and 18 developer stacks.

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Advice on Amazon EMR, Neo4j

Jaime

none at none

Aug 31, 2020

Needs advice

Hi, I want to create a social network for students, and I was wondering which of these three Oriented Graph DB's would you recommend. I plan to implement machine learning algorithms such as k-means and others to give recommendations and some basic data analyses; also, everything is going to be hosted in the cloud, so I expect the DB to be hosted there. I want the queries to be as fast as possible, and I like good tools to monitor my data. I would appreciate any recommendations or thoughts.

Context:

I released the MVP 6 months ago and got almost 600 users just from my university in Colombia, But now I want to expand it all over my country. I am expecting more or less 20000 users.

56.4k views56.4k

Comments

Detailed Comparison

Amazon EMR	Neo4j
It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.	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.
Elastic- Amazon EMR enables you to quickly and easily provision as much capacity as you need and add or remove capacity at any time. Deploy multiple clusters or resize a running cluster;Low Cost- Amazon EMR is designed to reduce the cost of processing large amounts of data. Some of the features that make it low cost include low hourly pricing, Amazon EC2 Spot integration, Amazon EC2 Reserved Instance integration, elasticity, and Amazon S3 integration.;Flexible Data Stores- With Amazon EMR, you can leverage multiple data stores, including Amazon S3, the Hadoop Distributed File System (HDFS), and Amazon DynamoDB.;Hadoop Tools- EMR supports powerful and proven Hadoop tools such as Hive, Pig, and HBase.	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
Statistics
GitHub Stars -	GitHub Stars 15.3K
GitHub Forks -	GitHub Forks 2.5K
Stacks 543	Stacks 1.2K
Followers 682	Followers 1.4K
Votes 54	Votes 351
Pros & Cons
Pros 15 On demand processing power 12 Don't need to maintain Hadoop Cluster yourself 7 Hadoop Tools 6 Elastic 4 Backed by Amazon	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

What are some alternatives to Amazon EMR, Neo4j?

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Stitch

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

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.

Dgraph

Dgraph's goal is to provide Google production level scale and throughput, with low enough latency to be serving real time user queries, over terabytes of structured data. Dgraph supports GraphQL-like query syntax, and responds in JSON and Protocol Buffers over GRPC and HTTP.

Dremio

Dremio—the data lake engine, operationalizes your data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts.

RedisGraph

RedisGraph is a graph database developed from scratch on top of Redis, using the new Redis Modules API to extend Redis with new commands and capabilities. Its main features include: - Simple, fast indexing and querying - Data stored in RAM, using memory-efficient custom data structures - On disk persistence - Tabular result sets - Simple and popular graph query language (Cypher) - Data Filtering, Aggregation and ordering

Related Comparisons

Some of the features offered by Amazon EMR are:

Elastic- Amazon EMR enables you to quickly and easily provision as much capacity as you need and add or remove capacity at any time. Deploy multiple clusters or resize a running cluster
Low Cost- Amazon EMR is designed to reduce the cost of processing large amounts of data. Some of the features that make it low cost include low hourly pricing, Amazon EC2 Spot integration, Amazon EC2 Reserved Instance integration, elasticity, and Amazon S3 integration.
Flexible Data Stores- With Amazon EMR, you can leverage multiple data stores, including Amazon S3, the Hadoop Distributed File System (HDFS), and Amazon DynamoDB.

On the other hand, Neo4j provides the following key features:

intuitive, using a graph model for data representation
reliable, with full ACID transactions
durable and fast, using a custom disk-based, native storage engine

Neo4j is an open source tool with 6.61K GitHub stars and 1.63K GitHub forks. Here's a link to Neo4j's open source repository on GitHub.

Amazon EMR vs Neo4j