Google BigQuery vs Google Cloud Bigtable

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Google BigQuery

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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—it'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’s 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’s 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.

Advice on Google BigQuery and Google Cloud Bigtable
Rory Gwozdz
CTO at Harvested Financial · | 2 upvotes · 19.7K views

I'm trying to build a way to read financial data really, really fast, for low cost. We are write/update-light (in this arena) and read-heavy. Google BigQuery being serverless can keep costs beyond low, but query speeds are always a few seconds because, I think, of the lack of indexing and potential to take advantage of the structure of the common queries. I have tried various partitions on BigQuery to speed things up too with some success but nothing extraordinary. I have never used Google Cloud Bigtable but get how it works conceptually. I believe it would make date-range based queries markedly faster. Question is, are there ways to take advantage of date-ranges in BigQuery, or does it makes sense to just shift to BigTable for mega-fast reads? I'd love to get sub-50ms.

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Sanjeev Singh
Lead Data Engineer at BharatPe · | 3 upvotes · 1.7K views

As a DataWarehouse Solution Google Bigquery is meant more for Large Data Analysis then real time Write/Update. You can go with BigTable instead of BigQuery but be prepare for the hight cost. Also, in most of the Data solution if you are looking for heavy real time Wrire/Update you have to put some cost on the solution. For more detail you can check this link https://cloud.google.com/blog/products/gcp/in-memory-query-execution-in-google-bigquery

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Decisions about Google BigQuery and Google Cloud Bigtable
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 Google BigQuery
Pros of Google Cloud Bigtable
  • 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
  • 10
    High performance
  • 9
    Fully managed
  • 5
    High scalability

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Cons of Google BigQuery
Cons of Google Cloud Bigtable
  • 1
    You can't unit test changes in BQ data
    Be the first to leave a con

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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—it's the database driving major applications such as Google Analytics and Gmail.

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    What companies use Google BigQuery?
    What companies use Google Cloud Bigtable?
    See which teams inside your own company are using Google BigQuery or Google Cloud Bigtable.
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    What tools integrate with Google BigQuery?
    What tools integrate with Google Cloud Bigtable?

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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)—no 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.
    Elasticsearch
    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
    See all alternatives