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Hadoop vs MemSQL: What are the differences?

Introduction

In the realm of big data processing and analysis, Hadoop and MemSQL are two popular technologies. While both serve the purpose of handling large volumes of data, they have distinct characteristics that set them apart from each other.

  1. Architecture: Hadoop utilizes a distributed file system (HDFS) and a MapReduce framework for processing data across a cluster of commodity hardware. On the other hand, MemSQL adopts a distributed, in-memory, SQL database architecture that allows for real-time data processing and analytics.

  2. Data Processing Speed: Hadoop processes data in batch mode, which can result in slower processing times for real-time applications. MemSQL, being an in-memory database, offers much faster data processing speeds by storing data in memory rather than on disk.

  3. Query Language Support: Hadoop primarily uses Java-based MapReduce for processing data, which can be complex for non-developers. In contrast, MemSQL supports standard SQL queries, making it easier for analysts and data scientists to work with the data.

  4. Scalability: Hadoop is highly scalable as it can easily add nodes to the existing cluster to accommodate more data processing requirements. While MemSQL also offers scalability, it is limited by the amount of RAM available in the cluster.

  5. Data Storage: Hadoop is optimized for storing and processing unstructured and semi-structured data, making it ideal for big data analytics. In contrast, MemSQL is suited for structured data storage and processing, making it a better choice for transactional applications and real-time analytics.

Summary

In summary, Hadoop is a distributed file system with a batch processing framework, whereas MemSQL is an in-memory, distributed SQL database, offering faster data processing speeds and support for real-time analytics.

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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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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 MemSQL
  • 39
    Great ecosystem
  • 11
    One stack to rule them all
  • 4
    Great load balancer
  • 1
    Amazon aws
  • 1
    Java syntax
  • 8
    Distributed
  • 4
    Realtime
  • 3
    Sql
  • 3
    Concurrent
  • 3
    JSON
  • 3
    Columnstore
  • 2
    Scalable
  • 2
    Ultra fast
  • 1
    Availability Group
  • 1
    Mixed workload
  • 1
    Pipeline
  • 1
    Unlimited Storage Database

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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 MemSQL?

MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.

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What are some alternatives to Hadoop and MemSQL?
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