Amazon Redshift vs etleap vs Google BigQuery

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Amazon Redshift

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Google BigQuery

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Pros of Amazon Redshift
Pros of etleap
Pros of Google BigQuery
  • 41
    Data Warehousing
  • 27
  • 17
  • 14
    Backed by Amazon
  • 5
  • 1
    Cheap and reliable
  • 1
  • 1
    Best Cloud DW Performance
  • 1
    Fast columnar storage
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    • 28
      High Performance
    • 25
      Easy to use
    • 22
      Fully managed service
    • 19
      Cheap Pricing
    • 16
      Process hundreds of GB in seconds
    • 12
      Big Data
    • 11
      Full table scans in seconds, no indexes needed
    • 8
      Always on, no per-hour costs
    • 6
      Good combination with fluentd
    • 4
      Machine learning
    • 1
      Easy to manage
    • 0
      Easy to learn

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    Cons of Amazon Redshift
    Cons of etleap
    Cons of Google BigQuery
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        • 1
          You can't unit test changes in BQ data

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        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 etleap?

        Etleap simplifies and automates ETL on AWS. Etleap's data wrangler and modeling tools let users control how data is transformed for analysis, without writing any code, and monitors pipelines to ensure availability and completeness of data.

        What is 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.

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        What companies use Amazon Redshift?
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        What tools integrate with Amazon Redshift?
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        What are some alternatives to Amazon Redshift, etleap, and Google BigQuery?
        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.
        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.
        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.
        See all alternatives