We performed a comparison between Azure Data Factory and BigQuery based on real PeerSpot user reviews.
Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."What I like best about Azure Data Factory is that it allows you to create pipelines, specifically ETL pipelines. I also like that Azure Data Factory has connectors and solves most of my company's problems."
"Its integrability with the rest of the activities on Azure is most valuable."
"For developers that are very accustomed to the Microsoft development studio, it's very easy for them to complete end-to-end data integration."
"The most important feature is that it can help you do the multi-threading concepts."
"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."
"The function of the solution is great."
"It's extremely consistent."
"The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."
"The solution's reporting, dashboard, and out-of-the-box capabilities match exactly our requirements."
"The main thing I like about BigQuery is storage. We did an on-premise BigQuery migration with trillions of records. Usually, we have to deal with insufficient storage on-premises, but in BigQuery, we don't get that because it's like cloud storage, and we can have any number of records. That is one advantage. The next major advantage is the column length. We have some limits on column length on-premises, like 10,000, and we have to design it based on that. However, with BigQuery, we don't need to design the column length at all. It will expand or shrink based on the records it's getting. I can give you a real-life example based on our migration from on-premises to GCP. There was a dimension table with a general number of records, and when we queried that on-premises, like in Apache Spark or Teradata, it took around half an hour to get those records. In BigQuery, it was instant. As it's very fast, you can get it in two or three minutes. That was very helpful for our engineers. Usually, we have to run a query on-premises and go for a break while waiting for that query to give us the results. It's not the case with BigQuery because it instantly provides results when we run it. So, that makes the work fast, it helps a lot, and it helps save a lot of time. It also has a reasonable performance rate and smart tuning. Suppose we need to perform some joins, BigQuery has a smart tuning option, and it'll tune itself and tell us the best way a query can be done in the backend. To be frank, the performance, reliability, and everything else have improved, even the downtime. Usually, on-premise servers have some downtime, but as BigQuery is multiregional, we have storage in three different locations. So, downtime is also not getting impacted. For example, if the Atlantic ocean location has some downtime, or the server is down, we can use data that is stored in Africa or somewhere else. We have three or four storage locations, and that's the main advantage."
"The product’s most valuable feature is its ability to manage the database on the cloud."
"It's straightforward to set up."
"We like the machine learning features and the high-performance database engine."
"The setup is simple."
"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."
"We basically used it to store server data and generate reports for enterprise architects. It was a valuable tool for our enterprise design architect."
"There should be a way that it can do switches, so if at any point in time I want to do some hybrid mode of making any data collections or ingestions, I can just click on a button."
"In the next release, it's important that some sort of scheduler for running tasks is added."
"Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters."
"Azure Data Factory can improve by having support in the drivers for change data capture."
"The user interface could use improvement. It's not a major issue but it's something that can be improved."
"Data Factory's monitorability could be better."
"The thing we missed most was data update, but this is now available as of two weeks ago."
"Areas for improvement in Azure Data Factory include connectivity and integration. When you use integration runtime, whenever there's a failure, the backup process in Azure Data Factory takes time, so this is another area for improvement."
"With other columnar databases like Snowflake, you can actually increase your VM size or increase your machine size, and you can buy more memory and it will start working faster, but that's not available in BigQuery. You have to actually open a ticket and then follow it up with Google support."
"As a product, BigQuery still requires a lot of maturity to accommodate other use cases and to be widely acceptable across other organizations."
"It would be helpful if they could provide some dashboards where you can easily view charts and information."
"There is a good amount of documentation out there, but they're consistently making changes to the platform, and, like, their literature hasn't been updated on some plans."
"Some of the queries are complex and difficult to understand."
"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 product’s performance could be much faster."
"The process of migrating from Datastore to BigQuery should be improved."
Azure Data Factory is ranked 3rd in Cloud Data Warehouse with 81 reviews while BigQuery is ranked 5th in Cloud Data Warehouse with 31 reviews. Azure Data Factory is rated 8.0, while BigQuery is rated 8.2. The top reviewer of Azure Data Factory writes "The data factory agent is quite good but pricing needs to be more transparent". On the other hand, the top reviewer of BigQuery writes "Expandable and easy to set up but needs more local data residency". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and Microsoft Azure Synapse Analytics, whereas BigQuery is most compared with Snowflake, Teradata, Oracle Autonomous Data Warehouse, Vertica and Apache Hadoop. See our Azure Data Factory vs. BigQuery report.
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