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Azure Cosmos DB vs Hadoop: What are the differences?
What is Azure Cosmos DB? A fully-managed, globally distributed NoSQL database service. Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.
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.
Azure Cosmos DB can be classified as a tool in the "NoSQL Database as a Service" category, while Hadoop is grouped under "Databases".
"Best-of-breed NoSQL features" is the top reason why over 13 developers like Azure Cosmos DB, while over 34 developers mention "Great ecosystem" as the leading cause for choosing Hadoop.
Hadoop is an open source tool with 9.18K GitHub stars and 5.74K GitHub forks. Here's a link to Hadoop's open source repository on GitHub.
According to the StackShare community, Hadoop has a broader approval, being mentioned in 237 company stacks & 116 developers stacks; compared to Azure Cosmos DB, which is listed in 24 company stacks and 23 developer stacks.
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.
Pros of Azure Cosmos DB
- Best-of-breed NoSQL features28
- High scalability22
- Globally distributed15
- Automatic indexing over flexible json data model14
- Tunable consistency10
- Always on with 99.99% availability sla10
- Javascript language integrated transactions and queries7
- Predictable performance6
- High performance5
- Analytics Store5
- Rapid Development2
- No Sql2
- Auto Indexing2
- Ease of use2
Pros of Hadoop
- Great ecosystem39
- One stack to rule them all11
- Great load balancer4
- Amazon aws1
- Java syntax1
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Cons of Azure Cosmos DB
- Pricing17
- Poor No SQL query support4
Cons of Hadoop
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What is Azure Cosmos DB?
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