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Hadoop

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Hadoop vs Neo4j: What are the differences?

Developers describe Hadoop as "Open-source software for reliable, scalable, distributed computing". The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. 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.

Hadoop and Neo4j are primarily classified as "Databases" and "Graph Databases" tools respectively.

"Great ecosystem" is the primary reason why developers consider Hadoop over the competitors, whereas "Cypher – graph query language" was stated as the key factor in picking Neo4j.

Hadoop and Neo4j are both open source tools. Hadoop with 9.18K GitHub stars and 5.74K forks on GitHub appears to be more popular than Neo4j with 6.56K GitHub stars and 1.62K GitHub forks.

Slack, Shopify, and SendGrid are some of the popular companies that use Hadoop, whereas Neo4j is used by Movielala, Hinge, and Sportsy. Hadoop has a broader approval, being mentioned in 237 company stacks & 116 developers stacks; compared to Neo4j, which is listed in 114 company stacks and 47 developer stacks.

Advice on Hadoop and Neo4j
Needs advice
on
KafkaKafkaInfluxDBInfluxDB
and
HadoopHadoop

I have a lot of data that's currently sitting in a MariaDB database, a lot of tables that weigh 200gb with indexes. Most of the large tables have a date column which is always filtered, but there are usually 4-6 additional columns that are filtered and used for statistics. I'm trying to figure out the best tool for storing and analyzing large amounts of data. Preferably self-hosted or a cheap solution. The current problem I'm running into is speed. Even with pretty good indexes, if I'm trying to load a large dataset, it's pretty slow.

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Replies (1)
Recommends
DruidDruid

Druid Could be an amazing solution for your use case, My understanding, and the assumption is you are looking to export your data from MariaDB for Analytical workload. It can be used for time series database as well as a data warehouse and can be scaled horizontally once your data increases. It's pretty easy to set up on any environment (Cloud, Kubernetes, or Self-hosted nix system). Some important features which make it a perfect solution for your use case. 1. It can do streaming ingestion (Kafka, Kinesis) as well as batch ingestion (Files from Local & Cloud Storage or Databases like MySQL, Postgres). In your case MariaDB (which has the same drivers to MySQL) 2. Columnar Database, So you can query just the fields which are required, and that runs your query faster automatically. 3. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. 4. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures 5. Gives ana amazing centralized UI to manage data sources, query, tasks.

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Jaime Ramos
Needs advice
on
Neo4jNeo4jDgraphDgraph
and
ArangoDBArangoDB

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.

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Replies (2)
Recommends
ArangoDBArangoDB

I have not used the others but I agree, ArangoDB should meet your needs. If you have worked with RDBMS and SQL before Arango will be a easy transition. AQL is simple yet powerful and deployment can be as small or large as you need. I love the fact that for my local development I can run it as docker container as part of my project and for production I can have multiple machines in a cluster. The project is also under active development and with the latest round of funding I feel comfortable that it will be around a while.

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David López Felguera
Full Stack Developer at NPAW · | 5 upvotes · 21.1K views
Recommends
ArangoDBArangoDB

Hi Jaime. I've worked with Neo4j and ArangoDB for a few years and for me, I prefer to use ArangoDB because its query sintax (AQL) is easier. I've built a network topology with both databases and now ArangoDB is the databases for that network topology. Also, ArangoDB has ArangoML that maybe can help you with your recommendation algorithims.

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Pros of Hadoop
Pros of Neo4j
  • 38
    Great ecosystem
  • 11
    One stack to rule them all
  • 4
    Great load balancer
  • 1
    Amazon aws
  • 1
    Java syntax
  • 68
    Cypher – graph query language
  • 58
    Great graphdb
  • 31
    Open source
  • 29
    Rest api
  • 27
    High-Performance Native API
  • 24
    ACID
  • 20
    Easy setup
  • 15
    Great support
  • 10
    Clustering
  • 9
    Hot Backups
  • 8
    Great Web Admin UI
  • 7
    Powerful, flexible data model
  • 7
    Mature
  • 6
    Embeddable
  • 5
    Easy to Use and Model
  • 4
    Best Graphdb
  • 4
    Highly-available
  • 2
    It's awesome, I wanted to try it
  • 2
    Great onboarding process
  • 2
    Great query language and built in data browser
  • 2
    Used by Crunchbase

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Cons of Hadoop
Cons of Neo4j
    Be the first to leave a con
    • 4
      Comparably slow
    • 4
      Can't store a vertex as JSON
    • 1
      Doesn't have a managed cloud service at low cost

    Sign up to add or upvote consMake informed product decisions

    What is Hadoop?

    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

    What is Neo4j?

    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.

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    What companies use Hadoop?
    What companies use Neo4j?
    See which teams inside your own company are using Hadoop or Neo4j.
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    What tools integrate with Hadoop?
    What tools integrate with Neo4j?

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    What are some alternatives to Hadoop and Neo4j?
    Cassandra
    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    Elasticsearch
    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
    Splunk
    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
    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.
    See all alternatives