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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data As A Service
  5. Cloudera Enterprise vs Neo4j

Cloudera Enterprise vs Neo4j

OverviewDecisionsComparisonAlternatives

Overview

Cloudera Enterprise
Cloudera Enterprise
Stacks126
Followers172
Votes5
Neo4j
Neo4j
Stacks1.2K
Followers1.4K
Votes351
GitHub Stars15.3K
Forks2.5K

Cloudera Enterprise vs Neo4j: What are the differences?

Developers describe Cloudera Enterprise as "Enterprise Platform for Big Data". Cloudera Enterprise includes CDH, the world’s most popular open source Hadoop-based platform, as well as advanced system management and data management tools plus dedicated support and community advocacy from our world-class team of Hadoop developers and experts. 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.

Cloudera Enterprise 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 Cloudera Enterprise are:

  • Unified – one integrated system, bringing diverse users and application workloads to one pool of data on common infrastructure
  • no data movement required
  • Secure – perimeter security, authentication, granular authorization, and data protection

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 6.6K GitHub stars and 1.63K GitHub forks. Here's a link to Neo4j's open source repository on GitHub.

Medium, Movielala, and Hinge are some of the popular companies that use Neo4j, whereas Cloudera Enterprise is used by Hammer Lab, JPush, and Jobrapido. Neo4j has a broader approval, being mentioned in 114 company stacks & 47 developers stacks; compared to Cloudera Enterprise, which is listed in 4 company stacks and 7 developer stacks.

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Advice on Cloudera Enterprise, Neo4j

Jaime
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

Cloudera Enterprise
Cloudera Enterprise
Neo4j
Neo4j

Cloudera Enterprise includes CDH, the world’s most popular open source Hadoop-based platform, as well as advanced system management and data management tools plus dedicated support and community advocacy from our world-class team of Hadoop developers and experts.

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.

Unified – one integrated system, bringing diverse users and application workloads to one pool of data on common infrastructure; no data movement required;Secure – perimeter security, authentication, granular authorization, and data protection;Governed – enterprise-grade data auditing, data lineage, and data discovery;Managed – native high-availability, fault-tolerance and self-healing storage, automated backup and disaster recovery, and advanced system and data management;Open – Apache-licensed open source to ensure your data and applications remain yours, and an open platform to connect with all of your existing investments in technology and skills
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
126
Stacks
1.2K
Followers
172
Followers
1.4K
Votes
5
Votes
351
Pros & Cons
Pros
  • 1
    Hybrid cloud
  • 1
    Scalability
  • 1
    Cheeper
  • 1
    Multicloud
  • 1
    Easily management
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 Cloudera Enterprise, Neo4j?

Google BigQuery

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

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

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

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Altiscale

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

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

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

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

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

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.

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