Google BigQuery vs. RavenDB

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

RavenDB is very well suited for NoSQL beginners to start easily setting up and using a NoSQL database. Also to set up a high performance and high availability cluster is possible without reading tons of documentation. Very straightforward assistant! The performance is really high.

• 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

• Document Database - no Object-Relational Impedance Mismatch
• ACID support that is optimized for performance
• Can be easily integrated into automated tests (unit tests)
• Easily configurable via C# code
• Comes directly with RavenStudio - no SSMS or SQL Developer required
• In general low footprint when it comes to memory and disk consumption
• Useful safety nets for new developers - e.g. by default an exception is thrown when you make too many requests within a session

• 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

• Developing methods is challenging if developers are unfamiliar with the accurate simulation approach.
• It does not allow you to replicating, or authorized access without first acquiring a license.
• The lack of evidence of tracking records in the enterprise systems raises several concerns about RavenDB.

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

We've had an excellent experience using RavenDB. Internally we are testing the newer features in 5.0 such as time series, which will effect the con specified previously dependent on the real world performance. We foresee that BattleCrate will continue to use RavenDB as we grow.

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

Really good .NET client that is very easy to use. The management studio is excellent and puts anything that Microsoft or Oracle have to shame. Very quick to develop with once the complexity hurdle has been overcome. Initially using it can be a bit painful until you fully grasp the event sourced nature of the indexing.

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

Had a question that was answered in minutes. Never used a NoSQL approach before, but was able to be proficient in a matter of hours. Easy to read API Documentation. 5 out 5 support in book, I have never once ran into an issue that wasn't quickly solved by either their support team or myself doing a quick search online.

RavenFS changed along the way and made us change the codes.

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

RavenDB is just smarter than the competitors. The mapping reduction sorting is head and shoulders above everything else I've used. Nothing really approaches comparable in terms of complexity. Because of the searching of predetermined categories, read efficiency is terrible. RavenDB is a storage system designed for the current websites and functional prototypes. It has an easy-to-use interface and enables quick replication and backup installation. Furthermore, technical assistance responds quickly and walks you through the implementation and deployment procedures.

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

• RavenDB has saved my customers a lot of money with their cloud services' tiered model. The database is able to grow with the project/company and can start out small at a low cost.
• RavenDB is free for three nodes and three CPUs, which makes it great for development scenarios. You're able to start rapidly building applications without having to worry about licensing.
• Scaling out has allowed us to use three small cloud servers when starting out and get the performance and throughput of a single larger server.