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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Google BigQuery

    Score8.8 out of 10
    N/AGoogle's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.

    $6.25

    per TiB (after the 1st 1 TiB per month, which is free)

    Notepad++

    Score9.2 out of 10
    N/ANotepad++ is a popular free and open source text editor available under the GPL license, featuring syntax highlighting and folding, auto-complete, multi-document management, and ac customizable GUI.N/A
    Pricing
    Google BigQueryNotepad++
    Editions & Modules
    Standard edition
    $0.04 / slot hour
    Enterprise edition
    $0.06 / slot hour
    Enterprise Plus edition
    $0.10 / slot hour
    No answers on this topic
    Offerings
    Pricing Offerings
    Google BigQueryNotepad++
    Free Trial
    YesNo
    Free/Freemium Version
    YesNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    Community Pulse
    Google BigQueryNotepad++
    Considered Both Products
    Google
    Chose Google BigQuery
    In my opinion, Google BigQuery is custom made to be the best data lake system that is easy to use, scalas to fit any business size, has inbuilt security, as well as tools for data integrity. Although a few other tools have some of the same functionality, Google BigQuery is the …
    Incentivized
    Open Source
    No answer on this topic
    Key User Insights
    Would buy again
    98%
    Would buy again
    64 Answers
    100%
    Would buy again
    41 Answers
    Delivers good value for the price
    97%
    Delivers good value for the price
    56 Answers
    100%
    Delivers good value for the price
    41 Answers
    Happy with the feature set
    97%
    Happy with the feature set
    63 Answers
    100%
    Happy with the feature set
    41 Answers
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    42 Answers
    96%
    Lived up to sales and marketing promises
    26 Answers
    Implementation went as expected
    100%
    Implementation went as expected
    59 Answers
    100%
    Implementation went as expected
    30 Answers
    Features
    Google BigQueryNotepad++
    Database-as-a-Service
    Comparison of Database-as-a-Service features of Google BigQuery and Notepad++
    Feature
    Google BigQuery
    8.5
    80 Ratings
    1% above category average
    Notepad++
    -
    Ratings
    Automatic software patching8.017 Ratings00 Ratings
    Database scalability9.079 Ratings00 Ratings
    Automated backups8.524 Ratings00 Ratings
    Database security provisions8.873 Ratings00 Ratings
    Monitoring and metrics8.675 Ratings00 Ratings
    Automatic host deployment8.013 Ratings00 Ratings
    Best Alternatives
    Google BigQueryNotepad++
    Small Businesses
    MongoDB Atlas
    Score7.8 out of 10
    BBEdit
    Score10 out of 10
    Medium-sized Companies
    Azure Database
    Score8.8 out of 10
    Vim
    Score9.8 out of 10
    Enterprises
    Google Cloud SQL
    Score8.7 out of 10
    Vim
    Score9.8 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Google BigQueryNotepad++
    Likelihood to Recommend
    9.0
    (79 ratings)
    8.5
    (62 ratings)
    Likelihood to Renew
    8.1
    (5 ratings)
    10.0
    (1 ratings)
    Usability
    6.6
    (6 ratings)
    9.0
    (4 ratings)
    Availability
    7.3
    (1 ratings)
    -
    (0 ratings)
    Performance
    6.4
    (1 ratings)
    -
    (0 ratings)
    Support Rating
    4.8
    (11 ratings)
    8.1
    (15 ratings)
    Configurability
    6.4
    (1 ratings)
    -
    (0 ratings)
    Contract Terms and Pricing Model
    10.0
    (1 ratings)
    -
    (0 ratings)
    Ease of integration
    7.3
    (1 ratings)
    -
    (0 ratings)
    Product Scalability
    7.3
    (1 ratings)
    -
    (0 ratings)
    Professional Services
    8.2
    (2 ratings)
    -
    (0 ratings)
    User Testimonials
    Google BigQueryNotepad++
    Likelihood to Recommend
    Google
    Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).
    Incentivized
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    Open Source
    well suited for 1) Coding and Development - Writing and editing code, Quick prototyping and testing of code snippets, Debugging and inspecting code using syntax highlighting and line numbering, 2) web development - Creating and editing HTML, CSS, JavaScript, and other web-related files .Managing and organizing web projects with multiple files and directories. Not suited for - 1) processing huge files 2) graphic designing 3) complex gui designs 3) Data Analysis and Manipulation - Editing and cleaning up text-based data files before importing them into analytical tools. Applying regular expressions to extract, transform, and manipulate data. 4) System Administration and IT - change system configuration file
    Incentivized
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    Pros
    Google
    • 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.
    Incentivized
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    Open Source
    • Notepad++ allows us to keep open files in tabs. Like in a web browser, these tabs let us access these files quickly and easily. Furthermore, even if we forget to save the files when closing the program or shutting down the PC, Notepad++ retains them in the open tabs when we reopen it.
    • Notepad++ supports many different file types. We usually save our files created in Notepad as normal text files, but sometimes as JSON, PHP, and HTML files.
    • Notepad++ is lightweight and requires little resources. Using it is snappy and responsive.
    • The developer of Notepad++ frequently updates the software with bug fixes, performance improvements and new features.
    Incentivized
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    Cons
    Google
    • Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
    • If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
    • It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.
    Incentivized
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    Open Source
    • UI looks a bit dated.
    • Sometimes the number of options are overwhelming and require a quick search to figure out where to locate a particular function.
    • Some way to do a diff between files would be great. Still need to resort to another paid app for that - unless it is a buried function I don't know about or there's a plugin for it.
    Incentivized
    Read full review
    Likelihood to Renew
    Google
    We have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.
    Incentivized
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    Open Source
    I use it every day for 13 years already and it never disappointed me
    Incentivized
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    Usability
    Google
    I think overall it is easy to use. I haven't done anything from the development side but an more of an end user of reporting tables built in Google BigQuery. I connect data visualization tools like Tableau or Power BI to the BigQuery reporting tables to analyze trends and create complex dashboards.
    Incentivized
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    Open Source
    There are lot of features to talk about. Especially the usability is good. Everyone can easily to use and user-friendly. Can also update easily. Can also write and execute the programming languages like C, C++ etc. Encoding is also the major feature that helps me a lot and converter as well.
    Read full review
    Reliability and Availability
    Google
    I have never had any significant issues with Google Big Query. It always seems to be up and running properly when I need it. I cannot recall any times where I received any kind of application errors or unplanned outages. If there were any they were resolved quickly by my IT team so I didn't notice them.
    Incentivized
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    Open Source
    No answers on this topic
    Performance
    Google
    I think Google Big Query's performance is in the acceptable range. Sometimes larger datasets are somewhat sluggish to load but for most of our applications it performs at a reasonable speed. We do have some reports that include a lot of complex calculations and others that run on granular store level data that so sometimes take a bit longer to load which can be frustrating.
    Incentivized
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    Open Source
    No answers on this topic
    Support Rating
    Google
    BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.
    Incentivized
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    Open Source
    I haven't needed to utilize any support related to Notepad++. I guess this is a good thing because I found it to be quite intuitive. There are almost infinite features you can tweak and plugins you can download but I haven't had to do that because Notepad++ is really good right out of the box.
    Incentivized
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    Alternatives Considered
    Google
    PowerBI can connect to GA4 for example but the data processing is more complicated and it takes longer to create dashboards. Azure is great once the data import has been configured but it's not an easy task for small businesses as it is with BigQuery.
    Incentivized
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    Open Source
    Notepad for Windows, Microsoft Word...LibreOffice Writer....I have used all of these for code writing and editing. Once again I like the universal feel of Notepad++. Basic Notepad, is just that, basic...and kind of clunky for what it is. This is a cool that I have installed on all my computers and also keep it on a thumb drive if I need it elsewhere.
    Incentivized
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    Contract Terms and Pricing Model
    Google
    None so far. Very satisfied with the transparency on contract terms and pricing model.
    Read full review
    Open Source
    No answers on this topic
    Scalability
    Google
    We have continued to expand out use of Google Big Query over the years. I'd say its flexibility and scalability is actually quite good. It also integrates well with other tools like Tableau and Power BI. It has served the needs of multiple data sources across multiple departments within my company.
    Incentivized
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    Open Source
    No answers on this topic
    Professional Services
    Google
    Google Support has kindly provide individual support and consultants to assist with the integration work. In the circumstance where the consultants are not present to support with the work, Google Support Helpline will always be available to answer to the queries without having to wait for more than 3 days.
    Read full review
    Open Source
    No answers on this topic
    Return on Investment
    Google
    • Previously, running complex queries on our on-premise data warehouse could take hours. Google BigQuery processes the same queries in minutes. We estimate it saves our team at least 25% of their time.
    • We can target our marketing campaigns very easily and understand our customer behaviour. It lets us personalize marketing campaigns and product recommendations and experience at least a 20% improvement in overall campaign performance.
    • Now, we only pay for the resources we use. Saved $1 million annually on data infrastructure and data storage costs compared to our previous solution.
    Incentivized
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    Open Source
    • Productivity has increased for developers.
    • It's free so instead of buying a piece of software, you can use this to replace many of them that may only specialize in one thing.
    • It gives our developers confidence knowing they have such a reliable, free tool at their disposal.
    Incentivized
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    ScreenShots

    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.