Google BigQuery vs. Navicat vs. Oracle Autonomous Transaction Processing

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

Navicat Premium is a very well-rounded tool with a reasonable licensing fee given its feature set. It is primarily suited for technical resources who must work with a variety of different database systems, but who may not be database or command line tool wizards. However, its query building and job batch scheduling feature support end-users as they build competency, and it continues to hold its value even as users outgrow their training wheels. Navicat is probably not so appropriate for DBAs and others for whom database development and management is already a career path; these folks have their preferred toolsets, both vendor-supplied and homegrown, and they might consider the additional licensing cost to outweigh the value they'd personally get out of Navicat.

Incentivized

If your company's value is not increased by maintaining databases, you should use the ATP.

• 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

• Really helpful auto-completion.
• Can connect to multiple (different) database types simultaneously.
• Can generate some really impressive charts.
• The new data dictionary tool is great!

• Quick and simple to deploy (less than 5 steps to create a new instance)
• No administrative tasks (backup, re-sizing) required

Incentivized

• 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

• Remote deploy databases
• Clone databases

Incentivized

• Connectivity between ATP and Oracle Kuberentes Engine cluster seems to drop randomly
• The ATP could come in an even smaller shape as the smallest shape is already quite big . But if must be more than always free, as that version does have connectivity limitations about 20 connections

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

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

There is quite a lot to learn because Navicat Premium is a very comprehensive database management and development tool, but it's User Interface is intuitive enough to pick up fairly quickly. I have also referred to the documentation on occasion, and can attest that it is quite good. I feel that it works best on a large monitor, but that's fairly common for this sort of software.

You get almost all the features of Oracle Database, including the ability to deploy APEX applications, without having to worry about system, storage or database configuration and maintenance, backups, etc. Also, you can start for free and move up adding resources as you need.

Incentivized

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

Very robust online community and well as excellent support staff. Easy to follow video tutorials. Detailed sample configurations are provided with guided walk-throughs via remote classroom instruction. Very fast response from support staff. No internal hires were necessary for support as vendor support staff fully met all technical expertise. 24/7 incident reporting and tracking is great.

Incentivized

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

Navicat allows for easier querying of data while still allowing for visualization and modeling

Both Azure SQL Database and Oracle Autonomous Transaction Processing offer a powerful and robust database platform. The advantage of Autonomous Transaction Processing is the ability to host APEX applications for free and without any special/complicated deployment or configuration process. Having a rapid development environment embedded for free was the main advantage for choosing Oracle as my cloud database provider.

Incentivized

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.

Incentivized

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.

Incentivized

• Assisting new employees with understanding our DB
• Reduce debugging and testing time with being able to visualize data/queries
• Allowing non-SQL users to execute queries to get real-time data.

Incentivized

• Approx 100 times faster spin up than manual setup of an Oracle DB
• 3h annual maintenance for big version releases (18c to 19c) compared to est. 500h annual maintenance with manual database updates