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CouchDB vs Hadoop: What are the differences?

  1. Storage Model: One key difference between CouchDB and Hadoop is their storage model. CouchDB uses a document-oriented storage model where data is stored in JSON documents, making it easy to retrieve and query data. On the other hand, Hadoop uses a distributed file system (HDFS) to store data in a more structured manner, making it highly scalable for big data processing.

  2. Query Language: Another difference is in the query language each platform uses. CouchDB utilizes a MapReduce query language for data retrieval and manipulation, allowing for flexible querying and indexing of documents. In contrast, Hadoop relies on distributed computing frameworks like Apache Spark or Hive to query and analyze large datasets, offering more advanced analytics capabilities.

  3. Consistency Model: CouchDB and Hadoop also differ in their consistency models. CouchDB offers eventual consistency by default, allowing for faster writes and updates to documents but potentially leading to some inconsistencies. Hadoop, on the other hand, ensures strong consistency by default, guaranteeing data accuracy and reliability at the cost of slower performance in some cases.

  4. Scalability: When it comes to scalability, Hadoop is designed to scale horizontally by adding more nodes to its cluster to handle increasing data volumes and processing demands efficiently. In contrast, while CouchDB can also be scaled horizontally to some extent, it is more limited compared to the scalability capabilities of Hadoop.

  5. Use Cases: CouchDB is typically used for real-time applications that require quick access to data and flexible querying capabilities, making it ideal for web and mobile applications. On the other hand, Hadoop is more suited for batch processing and analyzing large datasets, making it a popular choice for big data analytics and offline processing tasks.

  6. Fault Tolerance: In terms of fault tolerance, Hadoop provides robust fault tolerance mechanisms through data replication and job recovery mechanisms, ensuring data reliability and integrity even in the event of node failures. While CouchDB also offers some level of fault tolerance, it may not be as comprehensive as Hadoop's fault tolerance features.

In Summary, CouchDB and Hadoop differ in their storage models, query languages, consistency models, scalability, use cases, and fault tolerance mechanisms, catering to different needs in data storage and processing.

Advice on CouchDB and Hadoop
Needs advice

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

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 · 18.9K views

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

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)

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 CouchDB and Hadoop

I’m newbie I was developing a pouchdb and couchdb app cause if the sync. Lots of learning very little code available. I dropped the project cause it consumed my life. Yeats later I’m back into it. I researched other db and came across rethinkdb and mongo for the subscription features. With socketio I should be able to create and similar sync feature. Attempted to use mongo. I attempted to use rethink. Rethink for the win. Super clear l. I had it running in minutes on my local machine and I believe it’s supposed to scale easy. Mongo wasn’t as easy and there free online db is so slow what’s the point. Very easy to find mongo code examples and use rethink code in its place. I wish I went this route years ago. All that corporate google Amazon crap get bent. The reason they have so much power in the world is cause you guys are giving it to them.

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Karan Kaushik
Senior Software Developer at Shyplite · | 5 upvotes · 37.3K views

So, we started using foundationDB for an OLAP system although the inbuilt tools for some core things like aggregation and filtering were negligible, with the high through put of the DB, we were able to handle it on the application. The system has been running pretty well for the past 6 months, although the data load isn’t very high yet, the performance is fairly promising

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James Bender
Lead Application Architect at TekPartners · | 4 upvotes · 7.3K views

Our application data all goes in SQL. We will use something like Cosmos or Couch DB if one or both of these conditions are true: * We need to ingest a large amount of bulk data from a third party, and integrating it straight into an RDBMS with referential integrity checks would create a performance hit * We need to ingest a large amount of data that does not have a clearly defined, or consistent schema. In either case, we will have a process that migrates the data from Cosmos/Couch to SQL in a way that doesn't create a noticeable performance hit and ensures that we are not introducing bad data to the system. Because of this, there is a third condition that must be met: the data that is coming in must be something that the users will not need immediately, i.e. stock ticker information, real-time telemetry from other systems for performance/safety monitoring, etc.

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Gabriel Pa

We implemented our first large scale EPR application from using CouchDB .

Very fast, replication works great, doesn't consume much RAM, queries are blazing fast but we found a problem: the queries were very hard to write, it took a long time to figure out the API, we had to go and write our own @nodejs library to make it work properly.

It lost most of its support. Since then, we migrated to Couchbase and the learning curve was steep but all worth it. Memcached indexing out of the box, full text search works great.

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Pros of CouchDB
Pros of Hadoop
  • 43
  • 30
    Open source
  • 18
    Highly available
  • 12
    Partition tolerant
  • 11
    Eventual consistency
  • 7
  • 5
  • 4
    Attachments mechanism to docs
  • 4
    Multi master replication
  • 3
    Changes feed
  • 1
    REST interface
  • 1
    js- and erlang-views
  • 39
    Great ecosystem
  • 11
    One stack to rule them all
  • 4
    Great load balancer
  • 1
    Amazon aws
  • 1
    Java syntax

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What is CouchDB?

Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript.

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.

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What are some alternatives to CouchDB and Hadoop?
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.
Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands.
Cloudant’s distributed database as a service (DBaaS) allows developers of fast-growing web and mobile apps to focus on building and improving their products, instead of worrying about scaling and managing databases on their own.
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.
RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.
See all alternatives