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Amazon Redshift

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Amazon Redshift vs Microsoft Azure: What are the differences?

  1. Scalability: Amazon Redshift and Microsoft Azure both offer scalability in terms of storage and compute resources. However, Amazon Redshift provides automatic scaling of both storage and compute, allowing users to easily add or remove nodes as needed. In contrast, Microsoft Azure provides manual scaling, requiring users to manually add or remove nodes to adjust the storage and compute capacity.

  2. Pricing: When it comes to pricing, there are differences between Amazon Redshift and Microsoft Azure. Amazon Redshift offers a pay-as-you-go pricing model based on usage, with options for on-demand or reserved instances. On the other hand, Microsoft Azure offers a similar pay-as-you-go pricing model, but also provides options for purchasing reserved capacity to save costs in the long run.

  3. Security and Compliance: Both Amazon Redshift and Microsoft Azure offer strong security features and compliance certifications. However, Amazon Redshift has more strict access control capabilities, allowing granular control over user permissions and access to data. Azure, on the other hand, offers strong security features and complies with various industry standards, but may require additional configuration for more granular access control.

  4. Integration with Ecosystem: While Amazon Redshift is tightly integrated with the overall AWS ecosystem, including data ingestion, data lake storage, and various analytics services, Microsoft Azure provides a more comprehensive ecosystem with its own suite of data services, including Azure Data Lake Storage, Azure Data Factory, and Azure Analysis Services. This makes it easier for users who are already invested in the Azure ecosystem to work seamlessly with these services.

  5. Data Warehousing Features: Amazon Redshift is specifically designed for data warehousing, offering a wide range of features optimized for performance, scalability, and analytical queries. On the other hand, Microsoft Azure offers a more diverse range of data services, including both data warehousing and database solutions. This allows users to choose the most appropriate solution based on their specific needs and requirements.

  6. Geographical Availability: Amazon Redshift is available in multiple regions worldwide, allowing users to choose the location that best meets their needs for data residency and latency. Microsoft Azure also has a global presence, offering data warehousing services in several regions. However, the availability may vary across regions, and users should consider the available regions when selecting a provider.

In summary, Amazon Redshift and Microsoft Azure differ in terms of scalability, pricing models, security and compliance features, integration with ecosystem, data warehousing features, and geographical availability. Both platforms have their own strengths and users should evaluate these differences to choose the most suitable solution for their specific requirements.

Advice on Amazon Redshift and Microsoft Azure

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.

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Replies (3)
John Nguyen
Recommends
on
AirflowAirflowAWS LambdaAWS Lambda

You could also use AWS Lambda and use Cloudwatch event schedule if you know when the function should be triggered. The benefit is that you could use any language and use the respective database client.

But if you orchestrate ETLs then it makes sense to use Apache Airflow. This requires Python knowledge.

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Recommends
on
AirflowAirflow

Though we have always built something custom, Apache airflow (https://airflow.apache.org/) stood out as a key contender/alternative when it comes to open sources. On the commercial offering, Amazon Redshift combined with Amazon Kinesis (for complex manipulations) is great for BI, though Redshift as such is expensive.

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Recommends

You may want to look into a Data Virtualization product called Conduit. It connects to disparate data sources in AWS, on prem, Azure, GCP, and exposes them as a single unified Spark SQL view to PowerBI (direct query) or Tableau. Allows auto query and caching policies to enhance query speeds and experience. Has a GPU query engine and optimized Spark for fallback. Can be deployed on your AWS VM or on prem, scales up and out. Sounds like the ideal solution to your needs.

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Pros of Amazon Redshift
Pros of Microsoft Azure
  • 41
    Data Warehousing
  • 27
    Scalable
  • 17
    SQL
  • 14
    Backed by Amazon
  • 5
    Encryption
  • 1
    Cheap and reliable
  • 1
    Isolation
  • 1
    Best Cloud DW Performance
  • 1
    Fast columnar storage
  • 114
    Scales well and quite easy
  • 96
    Can use .Net or open source tools
  • 81
    Startup friendly
  • 73
    Startup plans via BizSpark
  • 62
    High performance
  • 38
    Wide choice of services
  • 32
    Low cost
  • 32
    Lots of integrations
  • 31
    Reliability
  • 19
    Twillio & Github are directly accessible
  • 13
    RESTful API
  • 10
    PaaS
  • 10
    Enterprise Grade
  • 10
    Startup support
  • 8
    DocumentDB
  • 7
    In person support
  • 6
    Free for students
  • 6
    Service Bus
  • 6
    Virtual Machines
  • 5
    Redis Cache
  • 5
    It rocks
  • 4
    Storage, Backup, and Recovery
  • 4
    Infrastructure Services
  • 4
    SQL Databases
  • 4
    CDN
  • 3
    Integration
  • 3
    Scheduler
  • 3
    Preview Portal
  • 3
    HDInsight
  • 3
    Built on Node.js
  • 3
    Big Data
  • 3
    BizSpark 60k Azure Benefit
  • 3
    IaaS
  • 2
    Backup
  • 2
    Open cloud
  • 2
    Web
  • 2
    SaaS
  • 2
    Big Compute
  • 2
    Mobile
  • 2
    Media
  • 2
    Dev-Test
  • 2
    Storage
  • 2
    StorSimple
  • 2
    Machine Learning
  • 2
    Stream Analytics
  • 2
    Data Factory
  • 2
    Event Hubs
  • 2
    Virtual Network
  • 2
    ExpressRoute
  • 2
    Traffic Manager
  • 2
    Media Services
  • 2
    BizTalk Services
  • 2
    Site Recovery
  • 2
    Active Directory
  • 2
    Multi-Factor Authentication
  • 2
    Visual Studio Online
  • 2
    Application Insights
  • 2
    Automation
  • 2
    Operational Insights
  • 2
    Key Vault
  • 2
    Infrastructure near your customers
  • 2
    Easy Deployment
  • 1
    Enterprise customer preferences
  • 1
    Documentation
  • 1
    Security
  • 1
    Best cloud platfrom
  • 1
    Easy and fast to start with
  • 1
    Remote Debugging

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Cons of Amazon Redshift
Cons of Microsoft Azure
    Be the first to leave a con
    • 7
      Confusing UI
    • 2
      Expensive plesk on Azure

    Sign up to add or upvote consMake informed product decisions

    What is Amazon Redshift?

    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.

    What is Microsoft Azure?

    Azure is an open and flexible cloud platform that enables you to quickly build, deploy and manage applications across a global network of Microsoft-managed datacenters. You can build applications using any language, tool or framework. And you can integrate your public cloud applications with your existing IT environment.

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    What tools integrate with Amazon Redshift?
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    What are some alternatives to Amazon Redshift and Microsoft Azure?
    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.
    Amazon Athena
    Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
    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.
    Amazon Redshift Spectrum
    With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” -- without having to load or transform any data.
    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.
    See all alternatives