Google BigQuery vs. PostgreSQL vs. Microsoft SQL Server

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).

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PostgreSQL is best used for structured data, and best when following relational database design principles. I would not use PostgreSQL for large unstructured data such as video, images, sound files, xml documents, web-pages, especially if these files have their own highly variable, internal structure.

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Microsoft SQL is ubiquitous, while MySQL runs under the hood all over the place. Microsoft SQL is the platform taught in colleges and certification courses and is the one most likely to be used by businesses because it is backed by Microsoft. Its interface is friendly (well, as pleasant as SQL can be) and has been used by so many for so long that resources are freely available if you encounter any issues.

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• 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.

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• It works well with external data sources and runs on platforms with stable performance.
• Clients can rest assured that their personal information will be safe and secure.
• Many forums discuss setup and usage, and most are free.
• Adding tooling applications to a computer is unlimited.
• PostgreSQL runs on many OS platforms and supports ANSI SQL, stored procedures, and triggers.

• Easy to configure and use with Visual Studio and Dot Net
• Easy integration with MSBI to perform data analysis
• Data Security
• Easy to understand and use
• Very easy to export database and tables in the form of SQL query or a script

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• 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.

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• Clearer indications on what is the query plan, to optimize the query
• More out of the box, Postgres specific, SQL functions
• It would be nice to have a more visual aid of the relationship between all tables, but possibly this depend more on the UI used

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• Microsoft SQL Server Enterprise edition has a high cost but is the only edition which supports SQL Always On Availability Groups. It would be nice to include this feature in the Standard version.
• Licensing of Microsoft SQL Server is a quite complex matter, it would be good to simplify licensing in the future. For example, per core vs per user CAL licensing, as well as complex licensing scenarios in the Cloud and on Edge locations.
• It would be good to include native tools for converting Oracle, DB2, Postgresql and MySQL/MariaDB databases (schema and data) for import into Microsoft SQL Server.

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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.

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As a needed software for day to day development activities

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We understand that the Microsoft SQL Server will continue to advance, offering the same robust and reliable platform while adding new features that enable us, as a software center, to create a superior product. That provides excellent performance while reducing the hardware requirements and the total cost of ownership of our solution.

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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.

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Postgresql is the best tool out there for relational data so I have to give it a high rating when it comes to analytics, data availability and consistency, so on and so forth. SQL is also a relatively consistent language so when it comes to building new tables and loading data in from the OLTP database, there are enough tools where we can perform ETL on a scalable basis.

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SQL Server mostly 'just works' or generates error messages to help you sort out the trouble. You can usually count on the product to get the job done and keep an eye on your potential mistakes. Interaction with other Microsoft products makes operating as a Windows user pretty straight forward. Digging through the multitude of dialogs and wizards can be a pain, but the answer is usually there somewhere.

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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.

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PostgreSQL's availability is top notch. Apart from connection time-out for an idle user, the database is super reliable.

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Its does not have outages.

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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.

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The data queries are relatively quick for a small to medium sized table. With complex joins, and a wide and deep table however, the performance of the query has room for improvement.

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SSAS data cubes may some time slow down your Excel reports.

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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.

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There are several companies that you can contract for technical support, like EnterpriseDB or Percona, both first level in expertise and commitment to the software.

But we do not have contracts with them, we have done all the way from googling to forums, and never have a problem that we cannot resolve or pass around. And for dozens of projects and more than 15 years now.

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We managed to handle most of our problems by looking into Microsoft's official documentation that has everything explained and almost every function has an example that illustrates in detail how a particular functionality works. Just like PowerShell has the ability to show you an example of how some cmdlet works, that is the case also here, and in my opinion, it is a very good practice and I like it.

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It was good

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The online training is request based. Had there been recorded videos available online for potential users to benefit from, I could have rated it higher. The online documentation however is very helpful. The online documentation PDF is downloadable and allows users to pace their own learning. With examples and code snippets, the documentation is great starting point.

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very hands on and detailed training

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The online documentation of the PostgreSQL product is elaborate and takes users step by step.

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Other than SQL taking quite a bit of time to actually install there are no problems with installation. Even on hardware that has good performance SQL can still take close to an hour to install a typical server with management and reporting services.

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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.

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Although the competition between the different databases is increasingly aggressive in the sense that they provide many improvements, new functionalities, compatibility with complementary components or environments, in some cases it requires that it be followed within the same family of applications that performs the company that develops it and that is not all bad, but being able to adapt or configure different programs, applications or other environments developed by third parties apart is what gives PostgreSQL a certain advantage and this diversification in the components that can be joined with it, is the reason why it is a great option to choose.

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[Microsoft] SQL Server has a much better community and professional support and is overall just a more reliable system with Microsoft behind it. I've used MySQL in the past and SQL Server has just become more comfortable for me and is my go to RDBMS.

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None so far. Very satisfied with the transparency on contract terms and pricing model.

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.

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The DB is reliable, scalable, easy to use and resolves most DB needs

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SQL server does handle growing demands of a mid sized company.

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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.

• 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.

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• Easy to administer so our DevOps team has only ever used minimal time to setup, tune, and maintain.
• Easy to interface with so our Engineering team has only ever used minimal time to query or modify the database. Getting the data is straightforward, what we do with it is the bigger concern.
• It's free. You can't beat that.

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• Increased accuracy - We went from multiple users having different versions of an Excel spreadsheet to a single source of truth for our reporting.
• Increased Efficiency - We can now generate reports at any time from a single source rather than multiple users spending their time collating data and generating reports.
• Improved Security - Enterprise level security on a dedicated server rather than financial files on multiple laptop hard drives.

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