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Hadoop vs Minio: What are the differences?
Introduction
In this post, we will discuss the key differences between Hadoop and Minio. Hadoop is a widely used open-source framework for distributed storage and processing of big data, while Minio is an open-source object storage server compatible with Amazon S3. Both systems have their unique characteristics and use cases.
Scalability: One key difference between Hadoop and Minio is their approach to scalability. Hadoop is designed to scale horizontally by adding more nodes to the cluster, allowing for parallel processing of data. On the other hand, Minio is primarily focused on scalable storage, with support for distributed setups but with limited built-in parallel processing capabilities.
Distributed File System: Hadoop utilizes the Hadoop Distributed File System (HDFS), a distributed file system that provides high-throughput access to data across clusters of computers. HDFS is fault-tolerant and designed to handle large amounts of data stored on commodity hardware. Minio, on the other hand, does not have its own distributed file system but can be deployed on top of existing file systems like Linux filesystems or network-attached storage (NAS).
Data Processing Paradigm: Hadoop follows the MapReduce paradigm, where data is divided into chunks and processed in parallel across multiple nodes in the cluster. Hadoop provides a programming model and runtime environment to execute large-scale data processing jobs. Minio, however, does not include a built-in data processing framework and primarily focuses on providing scalable object storage.
Compatibility: Hadoop is compatible with a wide range of data processing tools and systems, including Apache Spark, Apache Hive, and Apache Pig, making it a versatile platform for big data analytics. Minio, on the other hand, is primarily compatible with Amazon S3 and provides S3-compatible APIs, allowing seamless integration with existing S3-compatible applications and services.
Data Consistency: Hadoop guarantees strong data consistency through the use of replication and synchronization mechanisms in HDFS. This ensures that data is always available and consistent across the cluster, even in the event of failures. Minio, being an object storage server, provides eventual consistency by default, which means that there might be a temporary inconsistency between replicas, but it eventually converges to a consistent state.
Ease of Deployment and Management: Hadoop requires a more involved setup and configuration process, with multiple components like HDFS, YARN, and MapReduce to be installed and configured. It also requires dedicated infrastructure for running the Hadoop cluster. Minio, on the other hand, is easier to deploy and manage, as it can be installed on a single server or deployed in a distributed setup without requiring additional cluster management frameworks.
In summary, Hadoop and Minio differ in terms of their scalability approach, distributed file system, data processing paradigm, compatibility, data consistency guarantees, and ease of deployment and management. While Hadoop is designed for scalable data processing using the MapReduce paradigm, Minio focuses on scalable object storage compatible with Amazon S3.
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.
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.
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.
Pros of Hadoop
- Great ecosystem39
- One stack to rule them all11
- Great load balancer4
- Amazon aws1
- Java syntax1
Pros of Minio
- Store and Serve Resumes & Job Description PDF, Backups10
- S3 Compatible8
- Simple4
- Open Source4
- Encryption and Tamper-Proof3
- Lambda Compute3
- Private Cloud Storage2
- Pluggable Storage Backend2
- Scalable2
- Data Protection2
- Highly Available2
- Performance1
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Cons of Hadoop
Cons of Minio
- Deletion of huge buckets is not possible3
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