Google BigQuery vs. PhpStorm

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.

Incentivized

PhpStorm is well suited for any PHP development. It integrates well with Symfony, Laravel, CodeIgniter, Cake & Twig. I have used it very successfully in the past and despite not being my go-to editor, I will still use it when working on PHP heavy frameworks.

Incentivized

• Its serverless architecture and underlying Dremel technology are incredibly fast even on complex datasets. I can get answers to my questions almost instantly, without waiting hours for traditional data warehouses to churn through the data.
• Previously, our data was scattered across various databases and spreadsheets and getting a holistic view was pretty difficult. Google BigQuery acts as a central repository and consolidates everything in one place to join data sets and find hidden patterns.
• Running reports on our old systems used to take forever. Google BigQuery's crazy fast query speed lets us get insights from massive datasets in seconds.

Incentivized

• Version Control: Git, Subversion, and Others.
• Alerts about code being developed, alerts like errors, discontinuation of some function, suggestions for improvements.
• Application database access can run SQL commands directly from PhpStorm without having to have any other clients open.
• Shortcut keys that assist in the coding process.

Incentivized

• It is challenging to predict costs due to BigQuery's pay-per-query pricing model. User-friendly cost estimation tools, along with improved budget alerting features, could help users better manage and predict expenses.
• The BigQuery interface is less intuitive. A more user-friendly interface, enhanced documentation, and built-in tutorial systems could make BigQuery more accessible to a broader audience.

Incentivized

• There is a bug sometimes, when you pull that the directory structure it forgets all the folders
• It's not free
• When I copy paste the default is to not keep the same spacing/tab pattern of the original, which I'm not a fan of

Incentivized

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

web UI is easy and convenient. Many RDBMS clients such as aqua data studio, Dbeaver data grid, and others connect. Range of well-documented APIs available. The range of features keeps expanding, increasing similar features to traditional RDBMS such as Oracle and DB2

PhpStorm is very easy to use, once you get the hang of it. It can take a while to get the hang of it because there's so many options, some of which are buried in the imposing settings panel. It could use some help with multi-cursor, especially multi-file editing but that's a minor gripe.

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

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

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

The JetBrains community is all about helping others succeed, even in the most obscure setups. I have never had a question go unanswered, or I have never been able to come up with empty results in searching for the answer. My questions or concerns are typically address from other users in the community, so timing is pretty quick for a response

Incentivized

Google BigQuery of course collects a much much larger array of raw data and can handle (practically) an unlimited amount of data. For a large enterprise like ours that relies on large-scale analytics, this is absolutely imperative. Google BigQuery can also combine GA4 data with external sources (like CRM tools), so our analytics can be unified. Due to our heavy reliance on GA4, Google BigQuery is the natural choice since it is a Google product and has better integration.

Incentivized

Each one of the products I've listed are great in their own right, but non of them provide as complete a solution as PhpStorm in my opinion. Very few products offer all of the features that PhpStorm does and those that do don't have the greatest performance in my experience. Some have a great text editor but lack other critical features while others have a bounty of features, but the text editor is garbage. Before I tried PhpStorm, I stuck to using simple text editors because the features the IDEs offered didn't outweigh performance hit of running such a large, slow program. PhpStorm isn't lightweight by any means but I haven't noticed any of the common performance issues that I experienced with other IDEs.

Incentivized

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

• In some places, Google BigQuery has helped us save some money by avoiding the need for expensive infrastructure and reducing some of the operational costs.
• Scalability is up-to-date and really helpful in multiple places.
• Knowledge transfer is easy as it is very user-friendly, so the learning curve has been reduced.
• Also, it gives us more insights from our data, helping us make smarter decisions for our business.

Incentivized

• Recent AI advancements have saved us time to build by integrating it direct with the IDE allowing build to deployment to happen at a more rapid pace than previously possible
• Integrated AI commit message generation saves time and effort from team members shorting time writing documentation allowing more time spent in development
• Integrated git development causes less friction across team with version control and merge conflict resolution shortening development work flows

Incentivized