Google BigQuery vs Google Cloud Bigtable

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Google BigQuery

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

Introduction

Google BigQuery and Google Cloud Bigtable are two popular data storage and processing services offered by Google Cloud. While both serve as solutions for managing and analyzing large datasets, they differ in their underlying architectures and ideal use cases. In this article, we will explore the key differences between Google BigQuery and Google Cloud Bigtable.

1. Data Structure and Querying

Google BigQuery is a fully-managed, serverless data warehouse that operates on structured and semi-structured data. It is designed for running complex SQL queries across massive datasets and supports a wide range of data formats, including CSV, JSON, Avro, and more. BigQuery allows users to leverage its powerful SQL-like query language to perform analytics and gain insights from their data.

On the other hand, Google Cloud Bigtable is a distributed, scalable NoSQL database that specializes in handling large amounts of unstructured and semi-structured data. It is a key-value store that is built to handle high-volume workloads with low latency. Bigtable does not support SQL queries out of the box but provides a simple API for reading and writing individual rows based on their keys.

2. Scalability and Performance

Google BigQuery is designed to scale horizontally by automatically distributing the data and processing across multiple nodes. It can handle petabytes of data and can execute highly parallelized queries to deliver fast results. BigQuery achieves efficiency through the use of columnar storage and a technique called "Shared Nothing" architecture, where each worker node operates independently on a subset of the data.

Google Cloud Bigtable is also built for scalability, allowing users to store and process massive amounts of data. It utilizes a distributed storage model and can automatically replicate data across multiple clusters for high availability. Bigtable offers low-latency performance by leveraging its distributed architecture and maintaining data locality, ensuring fast access to data stored in nearby nodes.

3. Data Consistency and Transactional Support

In terms of data consistency and transactional support, Google BigQuery operates on an append-only basis, making it ideal for write-once, read-many use cases. While it provides ACID compliance for individual inserts, updates, and deletes within a single table, it does not support multi-row transactions.

On the other hand, Google Cloud Bigtable offers strong consistency for read and write operations within a single row or across multiple rows in a single transaction. It provides atomic mutations and conditional updates to ensure data integrity. However, Bigtable does not support cross-row transactions, which may limit its use cases.

4. Cost and Pricing Model

Google BigQuery pricing is based on a combination of factors, including the amount of data stored, the amount of data processed in queries, and the use of streaming inserts and exports. Users are charged separately for storage and queries, with different pricing tiers based on usage. BigQuery also offers flat-rate pricing options for predictable workloads.

Google Cloud Bigtable pricing is based on the capacity of the cluster, which includes the number and size of nodes used. Users can select different performance levels, and they are billed accordingly. In addition, storage costs are calculated based on the amount of data stored in Bigtable.

5. Secondary Indexes and Data Modeling

Google BigQuery supports the use of powerful SQL features, including the ability to create and use secondary indexes. This enables users to optimize their queries and improve performance by creating indexes on frequently used columns. BigQuery also provides functionality for partitioning and clustering tables, allowing for efficient data organization.

Google Cloud Bigtable does not natively support secondary indexes or SQL-like table operations. It is a schemaless database, where data is stored in a key-value format without enforcing a fixed schema. Instead, users must design their data models and access patterns carefully to ensure optimal performance. Bigtable allows for range scanning of keys and can handle filtering within a single row efficiently.

Summary

In summary, Google BigQuery is a managed data warehouse optimized for running complex SQL queries on structured and semi-structured data, offering horizontal scalability, cost-effective pricing, and support for secondary indexes. On the other hand, Google Cloud Bigtable is a scalable NoSQL database suitable for handling large amounts of unstructured data with low-latency performance, providing strong consistency, and utilizing a key-value storage model.

Advice on Google BigQuery and Google Cloud Bigtable
Rory Gwozdz
CTO at Harvested Financial · | 2 upvotes · 125K 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 · 2.4K 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
  • 28
    High Performance
  • 25
    Easy to use
  • 22
    Fully managed service
  • 19
    Cheap Pricing
  • 16
    Process hundreds of GB in seconds
  • 12
    Big Data
  • 11
    Full table scans in seconds, no indexes needed
  • 8
    Always on, no per-hour costs
  • 6
    Good combination with fluentd
  • 4
    Machine learning
  • 1
    Easy to manage
  • 0
    Easy to learn
  • 11
    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
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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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    See which teams inside your own company are using Google BigQuery or 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