Overview

Google BigQuery

Stacks1.7K

Followers1.5K

Votes152

Google Cloud Bigtable

Stacks139

Followers363

Votes25

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, Google Cloud Bigtable

Julien

CTO at Hawk

Sep 19, 2020

Decided

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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Comments

Sanjeev

Lead Data Engineer at BharatPe

Sep 14, 2020

Review

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

2.55k views2.55k

Comments

Rory

CTO at Harvested Financial

May 5, 2020

Needs adviceon

Google BigQuery

Google Cloud Bigtable

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.

128k views128k

Comments

Detailed Comparison

Google BigQuery	Google Cloud Bigtable
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 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.
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.;The right interface- Separate interfaces for administration and developers will make sure that you have access to the tools you need.	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.;Security: Cloud Bigtable is built with a replicated storage strategy, and all data is encrypted both in-flight and at rest.;Simplicity: Creating or reconfiguring a Cloud Bigtable cluster is done through a simple user interface and can be completed in less than 10 seconds. As data is put into Cloud Bigtable the backing storage scales automatically, so there’s no need to do complicated estimates of capacity requirements.;Maturity: Over the past 10+ years, Bigtable has driven Google’s most critical applications. In addition, the HBase API is a industry-standard interface for combined operational and analytical workloads.
Statistics
Stacks 1.7K	Stacks 139
Followers 1.5K	Followers 363
Votes 152	Votes 25
Pros & Cons
Pros 28 High Performance 25 Easy to use 22 Fully managed service 19 Cheap Pricing 16 Process hundreds of GB in seconds Cons 1 You can't unit test changes in BQ data 0 Sdas	Pros 11 High performance 9 Fully managed 5 High scalability
Integrations
Xplenty Fluentd Looker Chartio Treasure Data	Heroic Hadoop Apache Spark

What are some alternatives to Google BigQuery, Google Cloud Bigtable?

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

Azure Cosmos DB

Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.

Cloud Firestore

Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale.

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.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Cloudant

Cloudant’s distributed database as a service (DBaaS) allows developers of fast-growing web and mobile apps to focus on building and improving their products, instead of worrying about scaling and managing databases on their own.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

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 Cloud Datastore

Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.