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  1. Stackups
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  4. Big Data As A Service
  5. Amazon Redshift vs Azure Cosmos DB

Amazon Redshift vs Azure Cosmos DB

OverviewDecisionsComparisonAlternatives

Overview

Amazon Redshift
Amazon Redshift
Stacks1.5K
Followers1.4K
Votes108
Azure Cosmos DB
Azure Cosmos DB
Stacks594
Followers1.1K
Votes130

Amazon Redshift vs Azure Cosmos DB: What are the differences?

Introduction

In this article, we will compare Amazon Redshift and Azure Cosmos DB, two popular data storage and analytics solutions. While both platforms offer powerful features for managing data, there are several key differences that set them apart. Let's dive into the details.

  1. Scalability and Performance: Amazon Redshift is built specifically for data warehousing and offers excellent scalability and performance for analytical workloads. It uses columnar storage and parallel processing to deliver fast query performance, especially for complex queries involving large datasets. On the other hand, Azure Cosmos DB is a globally distributed, multi-model database that provides low-latency, scalable storage and querying capabilities. It is designed to handle vast amounts of data with guaranteed low latency and high throughput.

  2. Data Model and Query Language: Amazon Redshift follows a traditional relational database model with support for SQL as the primary query language. It offers a SQL-based interface for querying and managing data, making it easy for SQL-savvy users to work with. In contrast, Azure Cosmos DB is a NoSQL database that supports multiple data models, including key-value, document, graph, and column-family. It provides a JSON-like query language, SQL API, MongoDB API, and more, allowing developers to choose the most suitable model for their application's needs.

  3. Data Consistency and Availability: Amazon Redshift guarantees high availability and durability of data through automated backups, replication, and fault tolerance mechanisms. It provides different levels of data consistency, such as eventual consistency and read-after-write consistency, depending on the configuration. Azure Cosmos DB, being a globally distributed database, ensures high availability and consistency across multiple regions. It offers strong consistency, bounded staleness, session consistency, and more, allowing developers to configure the desired level of consistency for their applications.

  4. Geographical Presence: Amazon Redshift is available in multiple regions across the globe, allowing users to deploy their clusters in the nearest region to optimize performance. However, the geographical presence may be limited compared to Azure Cosmos DB, which is designed with global distribution in mind. Azure Cosmos DB provides global scale-out and automatic multi-region replication, ensuring data availability and low-latency access in any region where Azure is available.

  5. Data Integration and Ecosystem: Amazon Redshift has strong integration with other AWS services like AWS Glue for data ingestion and transformation, Amazon S3 for data storage, and Amazon EMR for big data processing. It also integrates well with popular business intelligence (BI) tools like Tableau and Looker. Azure Cosmos DB integrates seamlessly with other Azure services, such as Azure Functions, Azure Logic Apps, and Azure Stream Analytics, allowing developers to build end-to-end solutions using a unified Azure ecosystem.

In summary, Amazon Redshift is a powerful data warehousing solution with excellent scalability and performance, suitable for complex analytical workloads. On the other hand, Azure Cosmos DB is a globally distributed, multi-model database that provides low-latency access, high scalability, and flexibility to handle diverse data models. The choice between the two depends on specific requirements, workload types, and the need for global data distribution.

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Advice on Amazon Redshift, Azure Cosmos DB

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
Azure Cosmos DB
Azure Cosmos DB

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.

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.

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>
Fully managed with 99.99% Availability SLA;Elastically and highly scalable (both throughput and storage);Predictable low latency: <10ms @ P99 reads and <15ms @ P99 fully-indexed writes;Globally distributed with multi-region replication;Rich SQL queries over schema-agnostic automatic indexing;JavaScript language integrated multi-record ACID transactions with snapshot isolation;Well-defined tunable consistency models: Strong, Bounded Staleness, Session, and Eventual
Statistics
Stacks
1.5K
Stacks
594
Followers
1.4K
Followers
1.1K
Votes
108
Votes
130
Pros & Cons
Pros
  • 41
    Data Warehousing
  • 27
    Scalable
  • 17
    SQL
  • 14
    Backed by Amazon
  • 5
    Encryption
Pros
  • 28
    Best-of-breed NoSQL features
  • 22
    High scalability
  • 15
    Globally distributed
  • 14
    Automatic indexing over flexible json data model
  • 10
    Always on with 99.99% availability sla
Cons
  • 18
    Pricing
  • 4
    Poor No SQL query support
Integrations
SQLite
SQLite
MySQL
MySQL
Oracle PL/SQL
Oracle PL/SQL
Azure Machine Learning
Azure Machine Learning
MongoDB
MongoDB
Hadoop
Hadoop
Java
Java
Azure Functions
Azure Functions
Azure Container Service
Azure Container Service
Azure Storage
Azure Storage
Azure Websites
Azure Websites
Apache Spark
Apache Spark
Python
Python

What are some alternatives to Amazon Redshift, Azure Cosmos DB?

Amazon DynamoDB

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

Google BigQuery

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Cloud Firestore

Cloud Firestore

Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale.

Qubole

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Cloudant

Cloudant

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.

Altiscale

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

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.

Google Cloud Bigtable

Google Cloud Bigtable

Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.

Google Cloud Datastore

Google Cloud Datastore

Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.

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