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Google BigQuery

Google BigQuery

Overview

What is Google BigQuery?

Google's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.

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Awards

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Pricing

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Standard edition

$0.04 / slot hour

Cloud

Enterprise edition

$0.06 / slot hour

Cloud

Enterprise Plus edition

$0.10 / slot hour

Cloud

Entry-level set up fee?

  • No setup fee
For the latest information on pricing, visithttps://cloud.google.com/bigquery/prici…

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services

Starting price (does not include set up fee)

  • $6.25 per TiB (after the 1st 1 TiB per month, which is free)
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Product Demos

Lesson#6 - BigQuery for beginners| Analyzing data in google bigquery | Step by step tutorial (2020)

YouTube

How to get started with BigQuery

YouTube

BigQuery, IPython, Pandas and R for data science, starring Pearson

YouTube

Google BigQuery Demo

YouTube

Google BigQuery introduction by Jordan Tigani

YouTube
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Features

Database-as-a-Service

Database as a Service (DBaaS) software, sometimes referred to as cloud database software, is the delivery of database services ocer the Internet as a service

8.4
Avg 8.7
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Product Details

What is Google BigQuery?

Google BigQuery is a serverless, multicloud data warehouse that simplifies the process of working with all types of data. At the core of Google’s data cloud, BigQuery can be used to simplify data integration and securely scale analytics, share rich data experiences with built-in business intelligence, and train and deploy ML models with a simple SQL interface, helping to make an organization’s operations more data-driven.

BigQuery is a fully managed, AI-ready data analytics platform that helps you maximize value from your data and is designed to be multi-engine, multi-format, and multi-cloud.

Store 10 GiB of data and run up to 1 TiB of queries for free per month.


Gemini in BigQuery for an AI-powered assistive experience

BigQuery provides a single, unified workspace that includes a SQL, a notebook and a NL-based canvas interface for data practitioners of various coding skills to simplify analytics workflows from data ingestion and preparation to data exploration and visualization to ML model creation and use. Gemini in BigQuery provides AI-powered assistive and collaboration features including code assist, visual data preparation, and intelligent recommendations that help enhance productivity and optimize costs.


Bring multiple engines to a single copy of data

Serverless Apache Spark is available directly in BigQuery. BigQuery Studio lets users write and execute Spark without exporting data or managing infrastructure. BigQuery metastore provides shared runtime metadata for SQL and open source engines for a unified set of security and governance controls across all engines and storage types. By bringing multiple engines, including SQL, Spark and Python, to a single copy of data and metadata, the solution breaks down data silos.


Built-in machine learning

BigQuery ML provides built-in capabilities to create and run ML models for BigQuery data. It offers a broad range of models for predictions, and access to the latest Gemini models to derive insights from all data types and unlock generative AI tasks such as text summarization, text generation, multimodal embeddings, and vector search. It increases the model development speed by directly applying ML to data and eliminating the need to move data from BigQuery.


Built-in data governance

Data governance is built into BigQuery, including full integration of Dataplex capabilities such as a unified metadata catalog, data quality, lineage, and profiling. Customers can use rich AI-driven metadata search and discovery capabilities for assets including dataset schemas, notebooks and reports, public and commercial dataset listings, and more. BigQuery users can also use governance rules to manage policies on BigQuery object tables.

Google BigQuery Features

Database-as-a-Service Features

  • Supported: Database scalability
  • Supported: Database security provisions
  • Supported: Monitoring and metrics

Google BigQuery Screenshots

Screenshot of Migrating data warehouses to BigQuery - Features a streamlined migration path from Netezza, Oracle, Redshift, Teradata, or Snowflake to BigQuery using the fully managed BigQuery Migration Service.Screenshot of bringing any data into BigQuery - Data files can be uploaded from local sources, Google Drive, or Cloud Storage buckets, using BigQuery Data Transfer Service (DTS), Cloud Data Fusion plugins, by replicating data from relational databases with Datastream for BigQuery, or by leveraging Google's data integration partnerships.Screenshot of generative AI use cases with BigQuery and Gemini models - Data pipelines that blend structured data, unstructured data and generative AI models together can be built to create a new class of analytical applications. BigQuery integrates with Gemini 1.0 Pro using Vertex AI. The Gemini 1.0 Pro model is designed for higher input/output scale and better result quality across a wide range of tasks like text summarization and sentiment analysis. It can be accessed using simple SQL statements or BigQuery’s embedded DataFrame API from right inside the BigQuery console.Screenshot of insights derived from images, documents, and audio files, combined with structured data - Unstructured data represents a large portion of untapped enterprise data. However, it can be challenging to interpret, making it difficult to extract meaningful insights from it. Leveraging the power of BigLake, users can derive insights from images, documents, and audio files using a broad range of AI models including Vertex AI’s vision, document processing, and speech-to-text APIs, open-source TensorFlow Hub models, or custom models.Screenshot of event-driven analysis - Built-in streaming capabilities automatically ingest streaming data and make it immediately available to query. This allows users to make business decisions based on the freshest data. Or Dataflow can be used to enable simplified streaming data pipelines.Screenshot of predicting business outcomes AI/ML - Predictive analytics can be used to streamline operations, boost revenue, and mitigate risk. BigQuery ML democratizes the use of ML by empowering data analysts to build and run models using existing business intelligence tools and spreadsheets.Screenshot of tracking marketing ROI and performance with data and AI - Unifying marketing and business data sources in BigQuery provides a holistic view of the business, and first-party data can be used to deliver personalized and targeting marketing at scale with ML/AI built-in. Looker Studio or Connected Sheets can share these insights.Screenshot of BigQuery data clean rooms for privacy-centric data sharing - Creates a low-trust environment to collaborate in without copying or moving the underlying data right within BigQuery. This is used to perform privacy-enhancing transformations in BigQuery SQL interfaces and monitor usage to detect privacy threats on shared data.

Google BigQuery Video

Demo: Solving business challenges with an end-to-end analysis in BigQuery

Google BigQuery Technical Details

Deployment TypesSoftware as a Service (SaaS), Cloud, or Web-Based
Operating SystemsUnspecified
Mobile ApplicationNo

Frequently Asked Questions

Google's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.

Google BigQuery starts at $6.25.

Snowflake, Amazon Redshift, and Databricks Data Intelligence Platform are common alternatives for Google BigQuery.

Reviewers rate Database scalability highest, with a score of 9.1.

The most common users of Google BigQuery are from Mid-sized Companies (51-1,000 employees).
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Comparisons

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Reviews From Top Reviewers

(1-5 of 45)

Seamless and near real-time integration for GCP users.

Rating: 8 out of 10
September 21, 2024
Vetted Review
Verified User
Google BigQuery
7 years of experience
BigQuery fits naturally into the GCP estate, seamlessly integrating with GSheets and allowing users to ingest data into BigQuery with little to no technical knowledge. This is beneficial for GForm data, which is naturally presented via GSheet. With the real-time integration GSheets has with BigQuery, this form of data can then be in your data stack in mere seconds. This is really helpful when we want to collect feedback data or review a process using GForms as the method of data collection.
  • Realtime integration with Google Sheets.
  • GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
  • Seamless integration with other GCP products.
  • A simple pipeline might look like this:-
  • GForms -> GSheets -> BigQuery -> Looker
  • It all links up really well and with ease.
  • One instance holds many projects.
  • Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.
Cons
  • Can't customise compute methods.
  • In some cases, it's nice to limit a specific query, user, database, or process to a specific compute engine. This helps standardize costs, run time, and the fallout on shared resources. BigQuery's ease of use makes it simple for most users but difficult to customize for power uses.
  • Potential for vendor lock-in.
  • If your data stack is heavily reliant on integrations with other GCP products, you'll find it hard to move. Even if you just move from BigQuery to another supplier, you'll need to set up integrations from your current GCP products to the external vendor. Or find a vendor that integrates with GCP well.
  • Autoscaling might cause unexpected costs.
  • Since BigQuery charges on storage, compute, and streaming inserts, if any of these become unexpectedly in-demand (especially compute), your costs may skyrocket without much notice.
Google BigQuery is great for being the central datastore and entry point of data if you're on GCP. It seamlessly integrates with other Google products, meaning you can ingest data from other Google products with ease and little technical knowledge, and all of it is near real-time. Being serverless, BigQuery will scale with you, which means you don't have to worry about contention or spikes in demand/storage. This can, however, mean your costs can run away quickly or mount up at short notice.

Analyze data at lightening speed @BigQuery

Rating: 9 out of 10
November 29, 2018
GG
Vetted Review
Verified User
Google BigQuery
4 years of experience
BigQuery is a user platform designed to ingest, process and output large volumes of data. We use it to ingest data from Google Analytics and collate that data with other internal data systems by pushing them to BigQuery. BigQuery makes it possible to process a huge volume of data sessions and user levels in almost real-time and achieve personalization and optimized UX.
  • Processing of huge volumes of data enabled us to provide strategic insights by understanding the facts and realities.
  • Detailed Audience analysis enabled us to achieve better targeting for digital media and marketing campaigns
  • Personalization: We are able to achieve personalization by marrying, stitching, and processing huge volume of data.
Cons
  • SQL syntax is not exactly same as ANSI SQL so there is a learning curve. Traditional SQL queries cannot execute in BigQuery which limits portabiltiy of the code.
  • Limitation on visualization: We can improve visualization in data studio by bringing in the ability to support complex functions/formulas such as Tableau can do.
BigQuery's main strength is its ability to process huge volumes of data with lightning speed, and also perform personal detailed analysis on web analytics data or continuous streams of piles of data and then link it directly to data studio.

BigQuery is a game changer for OLAP

Rating: 10 out of 10
June 26, 2017
AG
Vetted Review
Verified User
Google BigQuery
2 years of experience
We use BigQuery as our data warehouse. Meaning, we use BigQuery (BQ) for storing our data, aggregating it and creating pipelines to push data into BQ, and take aggregates out of BQ in order to push them into ElasticSearch. We use it across our whole organization, and most of our data pipelines are now natively using BQ. For us, BQ helped us scale beyond Postgres for very large data sets in a convenient and most importantly, inexpensive way.
  • BigQuery integrates exceptionally well with Google Storage. All you have to do is push a CSV to Google Storage, and add it to BQ. BQ will try to detect the schema and import the CSV as a table. The process is very quick.
  • There are lots of ways to interact with BQ. Besides the web interface, there are also SDKs you can use to interface with bigquery from your tools. Meaning, it's not just data stuck in the cloud.
  • BigQuery lets you search extremely large datasets, quickly. We have many 100m+ datasets loaded, and searching any number of fields through them is not only easy (SQL!) but fast as well (most queries finish < 30 seconds). It's not a real-time system, but for OLAP, it's unbeatable.
Cons
  • It would be awesome to have BQ be real-time. Right now it serves the OLAP use case very well, but interactive would be great too.
  • The user interface is not the best we've used.
  • We'd love to have the Standard SQL mode be on by default.
BigQuery is best of OLAP. It's not a real-time system, so you shouldn't expect it to search through your billion records in 2 seconds. We use it to store raw, unaggregated data. For this use case, it's perfect, since the storage costs are low and the performance is more than good enough. BigQuery is also great for building data pipelines. It has convenient SDK to get data in and out of it, and SQL to marshall the data any way you want.

Best value, fast time to market.

Rating: 9 out of 10
August 20, 2024
Vetted Review
Verified User
Google BigQuery
4 years of experience
In our company, we handle large customer bases for our partners, usually 2-3 million+ users. When a partner asks us how to analyze the customer data in their possession, the standard approach we suggest is using a combination of Looker and BigQuery, which, in our opinion, is the cheapest and fastest way to generate a CDP capable of handling millions of records quickly and reliably.
  • Data Ingestion.
  • Query Speed in large dataset.
  • Cost-Effectiveness.
Cons
  • Cost Unpredictability: like every cloud option, you must watch your costs.
  • The SQL model utilized is specific to analytics,
  • Transactional queries can be slow (don't use BigQuery as a regular database!)
BigQuery is perfect if you have to query/analyze big data sets, no matter their size. It's also perfect for generating POC or on-demand analytics due to its fast ingestion and reduced cost. However, don't use BigQuery as a standard database: updating small sets or, in general, running transactional queries is not efficient.

Data analytics experience on Google BigQuery

Rating: 9 out of 10
March 25, 2024
We are analyzing large volumes of data generated by IoT devices to derive actionable insights and improve decision-making and for monitoring purpose while sitting from different places around the globe. Google BigQuery is helping us in setting up automation of gear manufacturing process in factories so as to reduce human effort.

  • Provide real time data for analysis and monitoring purpose.
  • SQL based queries makes it user friendly.
  • It can handle large amount of data.
Cons
  • sometime faced performance issues in query execution
  • training material is not easily available
  • Continuous maintenance required
Google BigQuery we have used while processing large amount of data when connected with Iot devices in automation factory which continuously give real time data and Google BigQuery can handle it very easily.

Sometime Small volume of data require same effort of writing query which is little bit hectic for users.
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