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  1. Stackups
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  4. Big Data As A Service
  5. Amazon Redshift vs Couchbase

Amazon Redshift vs Couchbase

OverviewDecisionsComparisonAlternatives

Overview

Amazon Redshift
Amazon Redshift
Stacks1.5K
Followers1.4K
Votes108
Couchbase
Couchbase
Stacks505
Followers606
Votes110

Amazon Redshift vs Couchbase: What are the differences?

# Key Differences between Amazon Redshift and Couchbase

Amazon Redshift and Couchbase are two popular databases with distinct features that cater to different use cases. Understanding the key differences between them can help in choosing the right database for specific needs.

1. **Data Model**:
Amazon Redshift is a fully-managed, columnar data warehouse that is optimized for OLAP workloads. It is designed to handle large volumes of data and complex queries efficiently. On the other hand, Couchbase is a NoSQL database that stores data in JSON format and is ideal for use cases requiring flexible schemas and fast read and write operations.

2. **Scaling**:
Amazon Redshift offers automatic scaling capabilities through cluster resizing and distribution keys to optimize query performance. In contrast, Couchbase allows for seamless scaling through its built-in sharding mechanism, which distributes data across nodes to ensure high availability and performance.

3. **ACID Compliance**:
Amazon Redshift provides strong ACID compliance with support for transactions and data integrity. In comparison, Couchbase offers eventual consistency by default but can achieve strong consistency through specific configurations, making it suitable for real-time applications that require strict data consistency.

4. **Query Language**:
Amazon Redshift uses SQL as the query language, making it familiar and easy to use for SQL developers. On the other hand, Couchbase supports N1QL (SQL for JSON) in addition to traditional key-value operations, allowing developers to query and manipulate JSON data using SQL-like syntax.

5. **Data Replication**:
Amazon Redshift supports cross-region snapshots and automated backups for data replication and disaster recovery. In contrast, Couchbase provides multi-dimensional scaling with separate nodes for query, index, and data services, enabling efficient data replication and distribution across different geographical regions.

6. **Tuning and Optimization**:
Amazon Redshift offers various optimization techniques like sort keys, distribution keys, and advanced data compression to enhance query performance. Couchbase provides built-in caching mechanisms, indexing options, and data partitioning strategies to optimize read and write operations for high throughput and low latency.

In Summary, understanding the key differences between Amazon Redshift and Couchbase in terms of their data model, scaling, ACID compliance, query language, data replication, and tuning and optimization can help in making informed decisions when choosing the right database for specific use cases.

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Advice on Amazon Redshift, Couchbase

Gabriel
Gabriel

CEO at Naologic

Jan 2, 2020

DecidedonCouchDBCouchDBCouchbaseCouchbaseMemcachedMemcached

We implemented our first large scale EPR application from naologic.com 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.

592k views592k
Comments
Mike
Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k
Comments
datocrats-org
datocrats-org

Jul 29, 2020

Needs adviceonAmazon EC2Amazon EC2TableauTableauPowerBIPowerBI

We need to perform ETL from several databases into a data warehouse or data lake. We want to

  • keep raw and transformed data available to users to draft their own queries efficiently
  • give users the ability to give custom permissions and SSO
  • move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

319k views319k
Comments

Detailed Comparison

Amazon Redshift
Amazon Redshift
Couchbase
Couchbase

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

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.

Optimized for Data Warehousing- It uses columnar storage, data compression, and zone maps to reduce the amount of IO needed to perform queries. Redshift has a massively parallel processing (MPP) architecture, parallelizing and distributing SQL operations to take advantage of all available resources.;Scalable- With a few clicks of the AWS Management Console or a simple API call, you can easily scale the number of nodes in your data warehouse up or down as your performance or capacity needs change.;No Up-Front Costs- You pay only for the resources you provision. You can choose On-Demand pricing with no up-front costs or long-term commitments, or obtain significantly discounted rates with Reserved Instance pricing.;Fault Tolerant- Amazon Redshift has multiple features that enhance the reliability of your data warehouse cluster. All data written to a node in your cluster is automatically replicated to other nodes within the cluster and all data is continuously backed up to Amazon S3.;SQL - Amazon Redshift is a SQL data warehouse and uses industry standard ODBC and JDBC connections and Postgres drivers.;Isolation - Amazon Redshift enables you to configure firewall rules to control network access to your data warehouse cluster.;Encryption – With just a couple of parameter settings, you can set up Amazon Redshift to use SSL to secure data in transit and hardware-acccelerated AES-256 encryption for data at rest.<br>
JSON document database; N1QL (SQL-like query language); Secondary Indexing; Full-Text Indexing; Eventing/Triggers; Real-Time Analytics; Mobile Synchronization for offline support; Autonomous Operator for Kubernetes and OpenShift
Statistics
Stacks
1.5K
Stacks
505
Followers
1.4K
Followers
606
Votes
108
Votes
110
Pros & Cons
Pros
  • 41
    Data Warehousing
  • 27
    Scalable
  • 17
    SQL
  • 14
    Backed by Amazon
  • 5
    Encryption
Pros
  • 18
    High performance
  • 18
    Flexible data model, easy scalability, extremely fast
  • 9
    Mobile app support
  • 7
    You can query it with Ansi-92 SQL
  • 6
    All nodes can be read/write
Cons
  • 4
    Terrible query language
Integrations
SQLite
SQLite
MySQL
MySQL
Oracle PL/SQL
Oracle PL/SQL
Hadoop
Hadoop
Kafka
Kafka
Elasticsearch
Elasticsearch
Kubernetes
Kubernetes
Apache Spark
Apache Spark

What are some alternatives to Amazon Redshift, Couchbase?

MongoDB

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.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

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.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

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

RethinkDB

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.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

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