Azure Data Factory vs BigQuery comparison

"Data Factory itself is great. It's pretty straightforward. You can easily add sources, join and lookup information, etc. The ease of use is pretty good."

"Most of our customers are Microsoft shops and prefer Azure Data Factory because they have good licensing options and a trust factor with Microsoft."

"The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components."

"The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows."

"The data flows were beneficial, allowing us to perform multiple transformations."

"When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit."

"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."

"It is easy to integrate."

"It's similar to a Hadoop cluster, except it's managed by Google."

"The interface is what I find particularly valuable."

"The solution is very useful nowadays for keeping a huge number of records."

"We like the machine learning features and the high-performance database engine."

"BigQuery can be used for any type of company. It has the capability of building applications and storing data. It can be used for OLTP or OLAP. It has many other products within the Google space."

"The product’s most valuable feature is its ability to manage the database on the cloud."

"BigQuery is a powerful tool for managing and analyzing large datasets. The versatility of BigQuery extends to its compatibility with external data visualization tools like Power BI and Tableau. This means you not only get query results but can also seamlessly integrate and visualize your data for better insights."

"The initial setup is straightforward."

"There's space for improvement in the development process of the data pipelines."

"Azure Data Factory uses many resources and has issues with parallel workflows."

"There is always room to improve. There should be good examples of use that, of course, customers aren't always willing to share. It is Catch-22. It would help the user base if everybody had really good examples of deployments that worked, but when you ask people to put out their good deployments, which also includes me, you usually got, "No, I'm not going to do that." They don't have enough good examples. Microsoft probably just needs to pay one of their partners to build 20 or 30 examples of functional Data Factories and then share them as a user base."

"I rate Azure Data Factory six out of 10 for stability. ADF is stable now, but we had problems recently with indexing on an SQL database. It's slow when dealing with a huge volume of data. It depends on whether the database is configured as general purpose or hyperscale."

"The product's technical support has certain shortcomings, making it an area where improvements are required."

"When the record fails, it's tough to identify and log."

"Some of the optimization techniques are not scalable."

"The performance could be better. It would be better if Azure Data Factory could handle a higher load. I have heard that it can get overloaded, and it can't handle it."

"So our challenge in Yemen is convincing many people to go to cloud services."

"I understand that Snowflake has made some improvements on its end to further reduce costs, so I believe BigQuery can catch up."

"Instead of connecting directly to BigQuery, we connect to GCP, Cloud Run, and then to BigQuery, which is a long process."

"The process of migrating from Datastore to BigQuery should be improved."

"I would like to see version-based implementation and a fallback arrangement for data stored in BigQuery storage. These are some features I'm interested in."

"The solution should reduce its pricing."

"The initial setup could be improved making it easier to deploy."

"I rate BigQuery six out of 10 for affordability. It could be cheaper."

"I would rate Data Factory's pricing nine out of ten."

"The pricing is a bit on the higher end."

"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."

"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."

"This is a cost-effective solution."

"Product is priced at the market standard."

"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."

"In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal."

"1 TB is free of cost monthly. If you use more than 1 TB a month, then you need to pay 5 dollars extra for each TB."

"Price-wise, I think that is very reasonable."

"Its cost structure operates on a pay-as-you-go model."

"The product operates on a pay-for-use model. Costs include storage and query execution, which can accumulate based on data volume and complexity."

"The pricing is good and there are no additional costs involved."

"The pricing appears to be competitive for the intended usage scenarios we have in mind."

"The solution's pricing is cheaper compared to other solutions."

"BigQuery is inexpensive."