Google BigQuery聽vs聽Google Cloud Bigtable

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Google BigQuery
Google BigQuery

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Google Cloud Bigtable
Google Cloud Bigtable

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Google BigQuery vs Google Cloud Bigtable: What are the differences?

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.; Google Cloud Bigtable: The same database that powers Google Search, Gmail and Analytics. Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years鈥攊t's the database driving major applications such as Google Analytics and Gmail.

Google BigQuery belongs to "Big Data as a Service" category of the tech stack, while Google Cloud Bigtable can be primarily classified under "NoSQL Database as a Service".

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, Google Cloud Bigtable provides the following key features:

  • Unmatched Performance: Single-digit millisecond latency and over 2X the performance per dollar of unmanaged NoSQL alternatives.
  • Open Source Interface: Because Cloud Bigtable is accessed through the HBase API, it is natively integrated with much of the existing big data and Hadoop ecosystem and supports Google鈥檚 big data products. Additionally, data can be imported from or exported to existing HBase clusters through simple bulk ingestion tools using industry-standard formats.
  • Low Cost: By providing a fully managed service and exceptional efficiency, Cloud Bigtable鈥檚 total cost of ownership is less than half the cost of its direct competition.

"High Performance" is the top reason why over 17 developers like Google BigQuery, while over 5 developers mention "High performance" as the leading cause for choosing Google Cloud Bigtable.

Sentry, Vine Labs, and Webedia are some of the popular companies that use Google BigQuery, whereas Google Cloud Bigtable is used by Spotify, Resultados Digitais, and Rainist. Google BigQuery has a broader approval, being mentioned in 156 company stacks & 39 developers stacks; compared to Google Cloud Bigtable, which is listed in 17 company stacks and 3 developer stacks.

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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.

What is Google Cloud Bigtable?

Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years鈥攊t's the database driving major applications such as Google Analytics and Gmail.
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    What are some alternatives to Google BigQuery and Google Cloud Bigtable?
    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.
    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.
    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.
    Google Analytics
    Google Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications.
    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.
    See all alternatives
    Decisions about Google BigQuery and Google Cloud Bigtable
    Snowflake
    Snowflake
    Google BigQuery
    Google BigQuery

    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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    GitHub
    GitHub
    Google Compute Engine
    Google Compute Engine
    Google Cloud Storage
    Google Cloud Storage
    Google BigQuery
    Google BigQuery
    Google Cloud Bigtable
    Google Cloud Bigtable
    Google Cloud Run
    Google Cloud Run
    Google Cloud Build
    Google Cloud Build
    Google Cloud Deployment Manager
    Google Cloud Deployment Manager
    Python
    Python
    Terraform
    Terraform
    Google Cloud IoT Core
    Google Cloud IoT Core

    Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.

    Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!

    Check Out My Architecture: CLICK ME

    Check out the GitHub repo attached

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    Reviews of Google BigQuery and Google Cloud Bigtable
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    How developers use Google BigQuery and Google Cloud Bigtable
    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.

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