Amazon EMR vs Cloudera Enterprise vs Google BigQuery

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Amazon EMR

492
571
+ 1
54
Cloudera Enterprise

105
147
+ 1
0
Google BigQuery

1.2K
1.1K
+ 1
146
Decisions about Amazon EMR, Cloudera Enterprise, and Google BigQuery
Julien Lafont

Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.

Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.

BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.

BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.

Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.

BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.

We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution

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Pros of Amazon EMR
Pros of Cloudera Enterprise
Pros of Google BigQuery
  • 15
    On demand processing power
  • 12
    Don't need to maintain Hadoop Cluster yourself
  • 7
    Hadoop Tools
  • 6
    Elastic
  • 4
    Backed by Amazon
  • 3
    Flexible
  • 3
    Economic - pay as you go, easy to use CLI and SDKs
  • 2
    Don't need a dedicated Ops group
  • 1
    Great support
  • 1
    Massive data handling
    Be the first to leave a pro
    • 27
      High Performance
    • 24
      Easy to use
    • 21
      Fully managed service
    • 19
      Cheap Pricing
    • 16
      Process hundreds of GB in seconds
    • 11
      Full table scans in seconds, no indexes needed
    • 11
      Big Data
    • 8
      Always on, no per-hour costs
    • 5
      Good combination with fluentd
    • 4
      Machine learning

    Sign up to add or upvote prosMake informed product decisions

    Cons of Amazon EMR
    Cons of Cloudera Enterprise
    Cons of Google BigQuery
      Be the first to leave a con
        Be the first to leave a con
        • 1
          You can't unit test changes in BQ data

        Sign up to add or upvote consMake informed product decisions

        What is Amazon EMR?

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

        What is Cloudera Enterprise?

        Cloudera Enterprise includes CDH, the world’s most popular open source Hadoop-based platform, as well as advanced system management and data management tools plus dedicated support and community advocacy from our world-class team of Hadoop developers and experts.

        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.

        Need advice about which tool to choose?Ask the StackShare community!

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        Blog Posts

        Aug 28 2019 at 3:10AM

        Segment

        PythonJavaAmazon S3+16
        5
        2180
        Jul 2 2019 at 9:34PM

        Segment

        Google AnalyticsAmazon S3New Relic+25
        10
        6008
        GitHubPythonNode.js+47
        52
        69816
        GitHubSlackMySQL+44
        109
        50192
        What are some alternatives to Amazon EMR, Cloudera Enterprise, and Google BigQuery?
        Amazon EC2
        It is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers.
        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.
        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
        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 HDInsight
        It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.
        See all alternatives