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Google BigQuery

A fully managed data warehouse for large-scale data analytics.

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Social recommendations and mentions

We have tracked the following product recommendations or mentions on various public social media platforms and blogs. They can help you see what people think about Google BigQuery and what they use it for.
  • Swirl: An open-source search engine with LLMs and ChatGPT to provide all the answers you need 🌌
    Using the Galaxy UI, knowledge workers can systematically review the best results from all configured services including Apache Solr, ChatGPT, Elastic, OpenSearch, PostgreSQL, Google BigQuery, plus generic HTTP/GET/POST with configurations for premium services like Google's Programmable Search Engine, Miro and Northern Light Research. - Source: / 9 months ago
  • Modern data stack: scaling people and technology at FINN
    Data Transformations: This phase involves modifying and integrating tables to generate new tables optimized for analytical use. Consider this example: you want to understand the purchasing behavior of customers aged between 20-30 in your online shop. This means you'll need to join product, customer, and transaction data to create a unified table for analytics. These data preparation tasks (e.g., joining... - Source: / 9 months ago
  • Running Transformations on BigQuery using dbt Cloud: step by step
    Introduction In today's data-driven world, transforming raw data into valuable insights is crucial. This process, however, often involves complex tasks that demand efficiency, scalability, and reliability. Enter dbt Cloud—a powerful tool that simplifies data transformations on Google BigQuery. In this article, we'll take you through a step-by-step guide on how to run BigQuery transformations using dbt Cloud.... - Source: / 10 months ago
  • Do I need a cloud computing–based data cloud company
    You'll want to evaluate what BigQuery has to offer and see if it makes sense for you to move over. Source: 11 months ago
  • I used ChatGPT to get an Internship
    Watch the introductory videos on BigQuery on the Google Cloud Platform website ( Source: 11 months ago
  • Wrangling BigQuery at Reddit
    If you've ever wondered what it's like to manage a BigQuery instance at Reddit scale, know that it's exactly like smaller systems just with much, much bigger numbers in the logs. Database management fundamentals are eerily similar regardless of scale or platform; BigQuery handles just about anything we throw at it, and we do indeed throw it the whole book. Our BigQuery platform is more than 100 petabytes of data... Source: about 1 year ago
  • Building a analytics dashboard using OpenSearch
    Now I know I've got some data I could use, I now need to find a platform that I can use to analyse the data coming from the Forem API. I did consider some other pieces of software, such as Google BigQuery (with looker studio) and ElasticSearch (with Kibana), I ultimately went with OpenSearch which is essentially a forked version of ElasticSearch maintained by AWS. The main reasons are that I could host it locally... - Source: / about 1 year ago
  • How to Totally Fubar Your Cloud Infrastructure Costs
    First, in one of our recent projects, we helped our client to run the cloud-based infrastructure of their entirely automated, real-time SEO platform. The solution rested in the safe familiarity of Google’s popular cloud-based data centres (i.e. Google Cloud Platform), whilst also making use of BigQuery — a serverless, multi-cloud data warehouse. Source: over 1 year ago
  • Deploying a Data Warehouse with Pulumi and Amazon Redshift
    A data warehouse is a specialized database that's purpose built for gathering and analyzing data. Unlike general-purpose databases like MySQL or PostgreSQL, which are designed to meet the real-time performance and transactional needs of applications, a data warehouse is designed to collect and process the data produced by those applications, collectively and over time, to help you gain insight from it. Examples of... - Source: / over 1 year ago
  • What is data integration?
    You build a data integration between all the ad service providers (e.g. Google Ads, Facebook Ads, etc.), ingesting data from those APIs and storing it in your BigQuery data warehouse. - Source: / over 1 year ago
  • What are Firebase Extensions? How can they speed up your app development?
    It also includes some extensions that integrate Firebase with Google Cloud Platform services such as BigQuery. - Source: / over 1 year ago
  • Evolutionary Data Infrastructure
    In addition, batch tasks require knowledge of the data schema of each service in order to get the data correctly and save it to the corresponding warehouse table. Assuming our data warehouse is GCP BigQuery, the schema in the warehouse table also needs to be created and modified manually. - Source: / over 1 year ago
  • Moving to Google Cloud managed services, from a FinOps point of view
    BigQuery has a pricing model close to Pub/Sub : you pay for what you insert on the database (in streaming) and the storage of these data. The main difference is on what you can do with these data. BigQuery is not a message queuing service, this is a data warehouse service. It proposes a query service to exploit these data and you pay for these queries. Actually, not on the query itself but on the quantity of data... - Source: / almost 2 years ago
  • The Ultimate Guide to Google Core Web Vitals
    This metric can get processed from Chrome's user/client information of session timings taken from users/ clients' traffic to your site. The information is gathered in a sizeable BigQuery data set by domain and kept up with by the Chromium project. - Source: / over 1 year ago
  • Apache Kafka Use Cases: When To Use It & When Not To
    A Kafka-based data integration platform will be a good fit here. The services can add events to different topics in a broker whenever there is a data update. Kafka consumers corresponding to each of the services can monitor these topics and make updates to the data in real-time. It is also possible to create a unified data store through the same integration platform. Developers can implement a unified store either... - Source: / over 1 year ago
  • I need to run intense graph-related algos and I just don't have the RAM to get the results (which are much less RAM hungry).
    Is it something you can use BigQuery for? They give you free credit to try it out so it might be worth a shot. Source: over 1 year ago
  • DeWitt Clause, or Can You Benchmark %DATABASE% and Get Away With It
    Google Cloud Platform, including BigQuery & Firebase. Benchmarking. Customer may conduct benchmark tests of the Services (each a "Test"). Customer may only publicly disclose the results of such Tests if it (a) obtains Google's prior written consent, (b) provides Google all necessary information to replicate the Tests, and (c) allows Google to conduct benchmark tests of Customer's publicly available products or... - Source: / almost 2 years ago
  • Top PyPI Packages
    Thanks to PyPI and Google BigQuery for the data; pypinfo and jq for the tools; Python Wheels for making their code open source; and DigitalOcean for sponsoring this project's hosting. Visit to see if your project is eligible. - Source: / almost 2 years ago
  • How to upload large excel-sheet file (90MB) to BigQuery
    Check Google Cloud Dataprep – requires no coding, you can normalize & clean up the data as well. I've done this many times, saved me headaches from dirty data in Excel files. Source: about 2 years ago
  • AWS Summit London 2022 day recap
    Amazon Redshift still remains a bit of a mystery to me, even after a whole session on it unpacking loads of its features, possibilities and use cases. Trying to draw some parallels in my head with BigQuery - Google Cloud Platform's own cloud data wharehouse service, which I know well - also didn't help much. So it remains one of those things that now I know a little bit more about than yesterday, but still feels... - Source: / about 2 years ago
  • A modern data stack for startups
    With data warehouse solutions (BigQuery, Snowflake, Redshift) going mainstream, modern data stacks are becoming increasingly boring - great news if you're starting from scratch! - Source: / about 2 years ago

External sources with reviews and comparisons of Google BigQuery

Top 6 Cloud Data Warehouses in 2023
You can also use BigQuery’s columnar and ANSI SQL databases to analyze petabytes of data at a fast speed. Its capabilities extend enough to accommodate spatial analysis using SQL and BigQuery GIS. Also, you can quickly create and run machine learning (ML) models on semi or large-scale structured data using simple SQL and BigQuery ML. Also, enjoy a real-time interactive dashboard using the BigQuery BI engine.
Top 5 Cloud Data Warehouses in 2023
Google BigQuery is an incredible platform for enterprises that want to run complex analytical queries or “heavy” queries that operate using a large set of data. This means it’s not ideal for running queries that are doing simple filtering or aggregation. So if your cloud data warehousing needs lightning-fast performance on a big set of data, Google BigQuery might be a great option for you.
Top 5 BigQuery Alternatives: A Challenge of Complexity
BigQuery's emergence as an attractive analytics and data warehouse platform was a significant win, helping to drive a 45% increase in Google Cloud revenue in the last quarter. The company plans to maintain this momentum by focusing on a multi-cloud future where BigQuery advances the cause of democratized analytics.
16 Top Big Data Analytics Tools You Should Know About
Google BigQuery is a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data. It is a Platform as a Service that supports querying using ANSI SQL. It also has built-in machine learning capabilities.

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