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Census
Census

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Google BigQuery
Google BigQuery

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Google BigQuery vs Census: What are the differences?

Developers describe Google BigQuery as "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.. On the other hand, Census is detailed as "Sync your warehouse data to any app". It syncs your data warehouse with CRM & go-to-market tools. Get your customer success, sales & marketing teams on the same page by sharing the same customer data.

Google BigQuery and Census can be categorized as "Big Data as a Service" tools.

Some of the features offered by Google BigQuery are:

  • 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.

On the other hand, Census provides the following key features:

  • Turn your warehouse into a Customer Data Platform
  • Sync with customer facing tools
  • No more data outages
- No public GitHub repository available -
- No public GitHub repository available -

What is Census?

It syncs your data warehouse with CRM & go-to-market tools. Get your customer success, sales & marketing teams on the same page by sharing the same customer 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 are some alternatives to Census 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.
        Amazon EMR
        It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.
        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)鈥攏o infrastructure to manage and no knobs to turn.
        Cloudera Enterprise
        Cloudera Enterprise includes CDH, the world鈥檚 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.
        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.
        See all alternatives
        Decisions about Census and Google BigQuery
        Google BigQuery
        Google BigQuery
        Snowflake
        Snowflake

        I use Google BigQuery because it makes is super easy to query and store data for analytics workloads. If you're using GCP, you're likely using BigQuery. However, running data viz tools directly connected to BigQuery will run pretty slow. They recently announced BI Engine which will hopefully compete well against big players like Snowflake when it comes to concurrency.

        What's nice too is that it has SQL-based ML tools, and it has great GIS support!

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        Google BigQuery
        Google BigQuery
        Amazon Redshift
        Amazon Redshift
        Amazon Athena
        Amazon Athena
        Amazon S3
        Amazon S3

        Hi all,

        Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?

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        Reviews of Census and Google BigQuery
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        How developers use Census and Google BigQuery
        Avatar of ShareThis
        ShareThis uses Google BigQueryGoogle BigQuery

        BigQuery allows our team to pull reports quickly using a SQL-like queries against our large store of data about social sharing. We use the information throughout the company, to do everything from making internal product decisions based on usage patterns to sharing certain kinds of custom reports with our publishers.

        Avatar of Lyndon Wong
        Lyndon Wong uses Google BigQueryGoogle BigQuery

        Aggregation of user events and traits across a marketing website, SaaS web application, user account provisioning backend and Salesforce CRM. Enables full-funnel analysis of campaign ROI, customer acquisition, engagement and retention at both the user and target account level.

        Avatar of Blue Shell Games
        Blue Shell Games uses Google BigQueryGoogle BigQuery

        Google's insanely fast, feature-rich, zero-maintenance column store. Used for real-time customer data queries.

        How much does Census cost?
        How much does Google BigQuery cost?
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