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Hadoop

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1.9K
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55
Minio

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Hadoop vs Minio: 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, Minio is detailed as "AWS S3 open source alternative written in Go". Minio is an object storage server compatible with Amazon S3 and licensed under Apache 2.0 License.

Hadoop can be classified as a tool in the "Databases" category, while Minio is grouped under "Cloud Storage".

Hadoop and Minio are both open source tools. Minio with 16.9K GitHub stars and 1.59K forks on GitHub appears to be more popular than Hadoop with 9.27K 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 Minio, which is listed in 19 company stacks and 12 developer stacks.

Advice on Hadoop and Minio
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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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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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Decisions about Hadoop and Minio

Minio is a free and open source object storage system. It can be self-hosted and is S3 compatible. During the early stage it would save cost and allow us to move to a different object storage when we scale up. It is also fast and easy to set up. This is very useful during development since it can be run on localhost.

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Pros of Hadoop
Pros of Minio
  • 38
    Great ecosystem
  • 11
    One stack to rule them all
  • 4
    Great load balancer
  • 1
    Amazon aws
  • 1
    Java syntax
  • 7
    Store and Serve Resumes & Job Description PDF, Backups
  • 4
    S3 Compatible
  • 4
    Open Source
  • 4
    Simple
  • 3
    Encryption and Tamper-Proof
  • 2
    Private Cloud Storage
  • 2
    Lambda Compute
  • 2
    Data Protection
  • 2
    Highly Available
  • 2
    Pluggable Storage Backend
  • 2
    Scalable
  • 1
    Performance

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Cons of Hadoop
Cons of Minio
    Be the first to leave a con
    • 1
      Deletion of huge buckets is not possible

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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 Minio?

    Minio is an object storage server compatible with Amazon S3 and licensed under Apache 2.0 License

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

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

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    Blog Posts

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    Segment

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    What are some alternatives to Hadoop and Minio?
    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
    How developers use Hadoop and Minio
    Pinterest uses
    Hadoop

    The MapReduce workflow starts to process experiment data nightly when data of the previous day is copied over from Kafka. At this time, all the raw log requests are transformed into meaningful experiment results and in-depth analysis. To populate experiment data for the dashboard, we have around 50 jobs running to do all the calculations and transforms of data.

    Yelp uses
    Hadoop

    in 2009 we open sourced mrjob, which allows any engineer to write a MapReduce job without contending for resources. We’re only limited by the amount of machines in an Amazon data center (which is an issue we’ve rarely encountered).

    Pinterest uses
    Hadoop

    The massive volume of discovery data that powers Pinterest and enables people to save Pins, create boards and follow other users, is generated through daily Hadoop jobs...

    Robert Brown uses
    Hadoop

    Importing/Exporting data, interpreting results. Possible integration with SAS

    Rohith Nandakumar uses
    Hadoop

    TBD. Good to have I think. Analytics on loads of data, recommendations?