Cloudera Enterprise vs Google BigQuery

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Cloudera Enterprise

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Google BigQuery

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Cloudera Enterprise vs Google BigQuery: What are the differences?

What is Cloudera Enterprise? Enterprise Platform for Big Data. 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? Analyze terabytes of data in seconds. 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..

Cloudera Enterprise and Google BigQuery belong to "Big Data as a Service" category of the tech stack.

Some of the features offered by Cloudera Enterprise are:

  • Unified – one integrated system, bringing diverse users and application workloads to one pool of data on common infrastructure
  • no data movement required
  • Secure – perimeter security, authentication, granular authorization, and data protection

On the other hand, Google BigQuery provides the following key features:

  • All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.
  • Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.
  • Affordable big data- The first Terabyte of data processed each month is free.

According to the StackShare community, Google BigQuery has a broader approval, being mentioned in 156 company stacks & 39 developers stacks; compared to Cloudera Enterprise, which is listed in 4 company stacks and 7 developer stacks.

Decisions about 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 Cloudera Enterprise
Pros of Google BigQuery
    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 Cloudera Enterprise
    Cons of Google BigQuery
      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 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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      What companies use Google BigQuery?
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      What tools integrate with Cloudera Enterprise?
      What tools integrate with Google BigQuery?

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      Blog Posts

      Aug 28 2019 at 3:10AM

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      Jul 2 2019 at 9:34PM

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      What are some alternatives to Cloudera Enterprise and Google BigQuery?
      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.
      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.
      Amazon EMR
      It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.
      Stitch
      Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.
      Dremio
      Dremio—the data lake engine, operationalizes your data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts.
      See all alternatives