Hadoop vs RethinkDB

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Hadoop

2K
2K
+ 1
55
RethinkDB

288
377
+ 1
307
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Hadoop vs RethinkDB: What are the differences?

Hadoop: 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; RethinkDB: JSON. Scales to multiple machines with very little effort. Open source. RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

Hadoop and RethinkDB can be categorized as "Databases" tools.

"Great ecosystem" is the top reason why over 34 developers like Hadoop, while over 46 developers mention "Powerful query language" as the leading cause for choosing RethinkDB.

Hadoop and RethinkDB are both open source tools. It seems that RethinkDB with 22.4K GitHub stars and 1.74K forks on GitHub has more adoption than Hadoop with 9.26K GitHub stars and 5.78K GitHub forks.

According to the StackShare community, Hadoop has a broader approval, being mentioned in 237 company stacks & 127 developers stacks; compared to RethinkDB, which is listed in 37 company stacks and 25 developer stacks.

Advice on Hadoop and RethinkDB
Needs advice
on
Snowflake
MarkLogic
and
Hadoop

For a property and casualty insurance company, we currently use MarkLogic and Hadoop for our raw data lake. Trying to figure out how snowflake fits in the picture. Does anybody have some good suggestions/best practices for when to use and what data to store in Mark logic versus Snowflake versus a hadoop or all three of these platforms redundant with one another?

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Needs advice
on
Snowflake
MarkLogic
and
Hadoop

for property and casualty insurance company we current Use marklogic and Hadoop for our raw data lake. Trying to figure out how snowflake fits in the picture. Does anybody have some good suggestions/best practices for when to use and what data to store in Mark logic versus snowflake versus a hadoop or all three of these platforms redundant with one another?

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Replies (1)
Ivo Dinis Rodrigues
none of you bussines at Marklogic · | 1 upvotes · 2.6K views
Recommends

As i see it, you can use Snowflake as your data warehouse and marklogic as a data lake. You can add all your raw data to ML and curate it to a company data model to then supply this to Snowflake. You could try to implement the dw functionality on marklogic but it will just cost you alot of time. If you are using Aws version of Snowflake you can use ML spark connector to access the data. As an extra you can use the ML also as an Operational report system if you join it with a Reporting tool lie PowerBi. With extra apis you can also provide data to other systems with ML as source.

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Needs advice
on
Kafka
InfluxDB
and
Hadoop

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
Druid

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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Pros of Hadoop
Pros of RethinkDB
  • 38
    Great ecosystem
  • 11
    One stack to rule them all
  • 4
    Great load balancer
  • 1
    Amazon aws
  • 1
    Java syntax
  • 48
    Powerful query language
  • 46
    Excellent dashboard
  • 42
    JSON
  • 41
    Distributed database
  • 38
    Open source
  • 25
    Reactive
  • 16
    Atomic updates
  • 15
    Joins
  • 9
    MVCC concurrency
  • 9
    Hadoop-style map/reduce
  • 4
    Geospatial support
  • 4
    Real-time, open-source, scalable
  • 2
    Great Admin UI
  • 2
    A NoSQL DB with joins
  • 2
    YC Company
  • 2
    Fast, easily scalable, great customer support
  • 2
    Changefeeds: no polling needed to get updates

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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 RethinkDB?

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

Need advice about which tool to choose?Ask the StackShare community!

What companies use Hadoop?
What companies use RethinkDB?
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What tools integrate with Hadoop?
What tools integrate with RethinkDB?

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What are some alternatives to Hadoop and RethinkDB?
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