Software Alternatives, Accelerators & Startups

Google BigQuery VS DevSpace (for Kubernetes and Docker)

Compare Google BigQuery VS DevSpace (for Kubernetes and Docker) and see what are their differences

Google BigQuery logo Google BigQuery

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

DevSpace (for Kubernetes and Docker) logo DevSpace (for Kubernetes and Docker)

Cloud-Native Software Development with Kubernetes and Docker
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • DevSpace (for Kubernetes and Docker) Landing page
    Landing page //
    2023-08-26

Google BigQuery videos

Cloud Dataprep Tutorial - Getting Started 101

More videos:

  • Review - Advanced Data Cleanup Techniques using Cloud Dataprep (Cloud Next '19)
  • Demo - Google Cloud Dataprep Premium product demo

DevSpace (for Kubernetes and Docker) videos

No DevSpace (for Kubernetes and Docker) videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to Google BigQuery and DevSpace (for Kubernetes and Docker))
Data Dashboard
100 100%
0% 0
DevOps Tools
0 0%
100% 100
Big Data
100 100%
0% 0
Developer Tools
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Google BigQuery and DevSpace (for Kubernetes and Docker)

Google BigQuery Reviews

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...
Source: geekflare.com
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...
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.
Source: blog.panoply.io
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.

DevSpace (for Kubernetes and Docker) Reviews

We have no reviews of DevSpace (for Kubernetes and Docker) yet.
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Social recommendations and mentions

Based on our record, Google BigQuery seems to be a lot more popular than DevSpace (for Kubernetes and Docker). While we know about 35 links to Google BigQuery, we've tracked only 3 mentions of DevSpace (for Kubernetes and Docker). We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Google BigQuery mentions (35)

  • 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: dev.to / 10 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: dev.to / 10 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: dev.to / 11 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: 12 months ago
  • I used ChatGPT to get an Internship
    Watch the introductory videos on BigQuery on the Google Cloud Platform website (https://cloud.google.com/bigquery). Source: 12 months ago
View more

DevSpace (for Kubernetes and Docker) mentions (3)

  • 5 Key Elements for a Great Developer Experience with Kubernetes
    DevSpace is very similar to Skaffold in terms of features, with the added benefits of a dedicated UI and a two-way file sync. The UI gives your team an overview of the stack and easy access to logs. At the same time, the file synchronization feature makes their development process faster by letting them directly change code from a running container. - Source: dev.to / about 2 years ago
  • How to Speed Up Your Local Kubernetes Development With DevSpace
    DevSpace is an open-source developer tool for Kubernetes that lets you develop and deploy cloud-native software faster. It is a client-only CLI tool that runs on your machine and works with any Kubernetes cluster. You can use it to automate image building and deployments, to develop software directly inside Kubernetes and to streamline workflows across your team as well as across dev, staging and production. - Source: dev.to / about 2 years ago
  • Meet Rich Burroughs
    And speaking of cycle times, the Loft team has also built DevSpace, a developer workflow tool for engineers working with Kubernetes clusters. Have you ever waited around for a new container to build so you can see if your changes work? Or even worse, for a CI pipeline to run integration tests? With DevSpace you can hot reload your app in the running container as you make changes. It's super cool and it's open... - Source: dev.to / about 3 years ago

What are some alternatives?

When comparing Google BigQuery and DevSpace (for Kubernetes and Docker), you can also consider the following products

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

Okteto - Development platform for Kubernetes applications.

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

Telepresence - Telepresence is an open source tool that lets you develop and debug your Kubernetes services...

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

Garden.io - Cloud native & Kubernetes testing done right