We performed a comparison between Amazon EMR 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."One of the valuable features about this solution is that it's managed services, so it's pretty stable, and scalable as much as you wish. It has all the necessary distributions. With some additional work, it's also possible to change to a Spark version with the latest version of EMR. It also has Hudi, so we are leveraging Apache Hudi on EMR for change data capture, so then it comes out-of-the-box in EMR."
"This is the best tool for hosts and it's really flexible and scalable."
"Amazon EMR's most valuable features are processing speed and data storage capacity."
"Amazon EMR is a good solution that can be used to manage big data."
"The solution is pretty simple to set up."
"The project management is very streamlined."
"The initial setup is straightforward."
"The ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions."
"The integrated data storage features are good."
"It has a proprietary way of storing and accessing data in its own data store and is 100% managed without you needing to install anything. There is no need to arrange for any infrastructure to be able to use this solution."
"Even non-coders can review the data in BigQuery."
"It has a well-structured suite of complimentary tools for data integration and so forth."
"It's straightforward to set up."
"I like that we can synch and run a large query. I also like that we can work with a large amount of data. You don't need to work separately, as it's a ready-made solution. It also comes with a built-in machine-learning feature. Once we start inputting the data, it will suggest some things related to the data, and we can come up with nice dashboards and statistics from a vast amount of data."
"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."
"It stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions."
"The product must add some of the latest technologies to provide more flexibility to the users."
"The problem for us is it starts very slow."
"We don't have much control. If we have multiple users, if they want to scale up, the cost will go and increase and we don't know how we can restrict that price part."
"The legacy versions of the solution are not supported in the new versions."
"The product's features for storing data in static clusters could be better."
"As people are shifting from legacy solutions to other technologies, Amazon EMR needs to add more features that give more flexibility in managing user data."
"The most complicated thing is configuring to the cluster and ensure it's running correctly."
"Modules and strategies should be better handled and notified early in advance."
"It would be beneficial to integrate additional tools, particularly from a business intelligence perspective."
"It would be helpful if they could provide some dashboards where you can easily view charts and information."
"The processing capability can be an area of improvement."
"It would be better if BigQuery didn't have huge restrictions. For example, when we migrate from on-premises to on-premise, the data which handles all ebook characters can be handled on-premise. But in BigQuery, we have huge restrictions. If we have some symbols, like a hash or other special characters, it won't accept them. Not in all cases, but it won't accept a few special characters, and when we migrate, we get errors. We need to use Regexp or something similar to replace that with another character. This isn't expected from a high-range technology like BigQuery. It has to adapt all products. For instance, if we have a TV Showroom, the TV symbol will be there in the shop name. Teradata and Apache Spark accept this, but BigQuery won't. This is the primary concern that we had. In the next release, it would be better if the query on the external table also had cache. Right now, we are using a GCS bucket, and in the native table, we have cache. For example, if we query the same table, it won't cost because it will try to fetch the records from the cached result. But when we run queries on the external table a number of times, it won't be cached. That's a major drawback of BigQuery. Only the native table has the cache option, and the external table doesn't. If there is an option to have an external table for cache purposes, it'll be a significant advantage for our organization."
"The solution should reduce its pricing."
"So our challenge in Yemen is convincing many people to go to cloud services."
"We'd like to see more local data residency."
"An area for improvement in BigQuery is its UI because it's not working very well. Pricing for the solution is also very high."
Amazon EMR is ranked 9th in Cloud Data Warehouse with 20 reviews while BigQuery is ranked 5th in Cloud Data Warehouse with 31 reviews. Amazon EMR is rated 7.8, while BigQuery is rated 8.2. The top reviewer of Amazon EMR writes "Provides efficient data processing features and has good scalability ". On the other hand, the top reviewer of BigQuery writes "Expandable and easy to set up but needs more local data residency". Amazon EMR is most compared with Snowflake, Cloudera Distribution for Hadoop, Azure Data Factory, Amazon Redshift and Apache Spark, whereas BigQuery is most compared with Snowflake, Teradata, Oracle Autonomous Data Warehouse, Vertica and Apache Hadoop. See our Amazon EMR vs. BigQuery report.
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