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Hadoop vs MariaDB: What are the differences?

What is 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.

What is MariaDB? An enhanced, drop-in replacement for MySQL. Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

Hadoop and MariaDB belong to "Databases" category of the tech stack.

"Great ecosystem" is the top reason why over 34 developers like Hadoop, while over 150 developers mention "Drop-in mysql replacement" as the leading cause for choosing MariaDB.

Hadoop and MariaDB are both open source tools. Hadoop with 9.26K GitHub stars and 5.78K forks on GitHub appears to be more popular than MariaDB with 2.82K GitHub stars and 864 GitHub forks.

Grooveshark, Shutterstock, and Geocodio are some of the popular companies that use MariaDB, whereas Hadoop is used by Airbnb, Uber Technologies, and Spotify. MariaDB has a broader approval, being mentioned in 496 company stacks & 461 developers stacks; compared to Hadoop, which is listed in 237 company stacks and 127 developer stacks.

Advice on Hadoop and MariaDB
Needs advice
on
HadoopHadoopMarkLogicMarkLogic
and
SnowflakeSnowflake

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
HadoopHadoopMarkLogicMarkLogic
and
SnowflakeSnowflake

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 · 20.9K 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
HadoopHadoopInfluxDBInfluxDB
and
KafkaKafka

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
on
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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Maxim Ryakhovskiy
Needs advice
on
MariaDBMariaDBMongooseMongoose
and
PostgreSQLPostgreSQL

Hi all. I am an informatics student, and I need to realise a simple website for my friend. I am planning to realise the website using Node.js and Mongoose, since I have already done a project using these technologies. I also know SQL, and I have used PostgreSQL and MySQL previously.

The website will show a possible travel destination and local transportation. The database is used to store information about traveling, so only admin will manage the content (especially photos). While clients will see the content uploaded by the admin. I am planning to use Mongoose because it is very simple and efficient for this project. Please give me your opinion about this choice.

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Replies (7)

The use case you are describing would benefit from a self-hosted headless CMS like contentful. You can also go for Strapi with a database of your choice but here you would have to host Strapi and the underlying database (if not using SQLite) yourself. If you want to use Strapi, you can ease your work by using something like PlanetSCaleDB as the backing database for Strapi.

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Reza Malek
at Meam Software Engineering Group · | 4 upvotes · 231.5K views
Recommends
on
MongooseMongoosePostgreSQLPostgreSQL

Your requirements seem nothing special. on the other hand, MongoDB is commonly used with Node. you could use Mongo without defining a Schema, does it give you any benefits? Also, note that development speed matters. In most cases RDBMS are the best choice, Learn and use Postgres for life!

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Recommends
on
MongooseMongoose

Any database will be a great choice for your app, which is less of a technical challenge and more about great content. Go for it, the geographical search features maybe be actually handy for you.

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Nutchanon Ninyawee

SQL is not so good at query lat long out of the box. you might need to use additional tools for that like UTM coordinates or Uber's H3.

If you use mongoDB, it support 2d coordinate query out of the box.

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Tarun Batra
Senior Software Developer at Okta · | 2 upvotes · 223.6K views
Recommends
on
MongooseMongoose

MongoDB and Mongoose are commonly used with Node.js and the use case doesn't seem to be requiring any special considerations as of now. However using MongoDB now will allow you to easily expand and modify your use case in future.

If not MongoDB, then my second choice will be PostgreSQL. It's a generic purpose database with jsonb support (if you need it) and lots of resources online. Nobody was fired for choosing PostgreSQL.

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Václav Hodek
CEO, lead developer at Localazy · | 1 upvotes · 224K views
Recommends
on
PostgreSQLPostgreSQL

Any database engine should work well but I vote for Postgres because of PostGIS extension that may be handy for travel related site. There's nothing special about your requirements.

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Ruslan Rayanov
Recommends

Hi, Maxim! Most likely, the site is almost ready. But we would like to share our development with you. https://falcon.web-automation.ru/ This is a constructor for web application. With it, you can create almost any site with different roles which have different levels of access to information and different functionality. The platform is managed via sql. knowing sql, you will be able to change the business logic as necessary and during further project maintenance. We will be glad to hear your feedback about the platform.

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Decisions about Hadoop and MariaDB
Omran Jamal
CTO & Co-founder at Bonton Connect · | 4 upvotes · 553.8K views

We actually use both Mongo and SQL databases in production. Mongo excels in both speed and developer friendliness when it comes to geospatial data and queries on the geospatial data, but we also like ACID compliance hence most of our other data (except on-site logs) are stored in a SQL Database (MariaDB for now)

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Pros of Hadoop
Pros of MariaDB
  • 39
    Great ecosystem
  • 11
    One stack to rule them all
  • 4
    Great load balancer
  • 1
    Amazon aws
  • 1
    Java syntax
  • 149
    Drop-in mysql replacement
  • 100
    Great performance
  • 74
    Open source
  • 55
    Free
  • 44
    Easy setup
  • 15
    Easy and fast
  • 14
    Lead developer is "monty" widenius the founder of mysql
  • 6
    Also an aws rds service
  • 4
    Consistent and robust
  • 4
    Learning curve easy
  • 2
    Native JSON Support / Dynamic Columns
  • 1
    Real Multi Threaded queries on a table/db

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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 MariaDB?

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

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What companies use Hadoop?
What companies use MariaDB?
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What are some alternatives to Hadoop and MariaDB?
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