We performed a comparison between BigQuery and VMware Tanzu Greenplum 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."It's straightforward to set up."
"It stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions."
"It's similar to a Hadoop cluster, except it's managed by Google."
"The feature called calibrating the capacity is valuable."
"When integrating their system into the cloud-based solutions, we were able to increase their efficiency and overall productivity twice compared with their on-premises option."
"Even non-coders can review the data in BigQuery."
"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 most valuable features of BigQuery is that it supports standard SQL and provides good performance."
"Scalability is simple because it's an MPP database. If you need more processing power or you need more storage, you just add a few more nodes in the cluster. It works on common commodity hardware. You can use any type of server. You don't need to have proprietary hardware. It's fairly flexible."
"The parallel load features mean that Greenplum is capable of high-volume data loading in parallel to all of the cluster segments, which is really valuable."
"Scalable (Massive) Parallel Processing (MPP) – The ability to bring to bear large amounts of compute against large data sets with Greenplum and the EMC DCA has proven itself to be very effective."
"Helps us to achieve large-scale analytics."
"The most valuable feature for us is horizontal scaling."
"The loading speed is very good."
"Pivotal Greenplum's shared-nothing architecture."
"It's one of the fastest databases in the market. It's easy to use. From a maintenance perspective it's a good product. The segmentation, or architecture of the product is different than other databases such as Oracle. So even in 10 years, the data distribution for such segments will not affect other segments. The query performance of the product, for complex queries, is very good. It has good integration with Hadoop."
"The solution should reduce its pricing."
"An area for improvement in BigQuery is its UI because it's not working very well. Pricing for the solution is also very high."
"The initial setup could be improved making it easier to deploy."
"We would like to be able to calibrate the solution to run on top of a raw file."
"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."
"For greater flexibility and ease of use, it would be beneficial if BigQuery offered more third-party add-ons and connectors, particularly for databases that don't have built-in integration options."
"I understand that Snowflake has made some improvements on its end to further reduce costs, so I believe BigQuery can catch up."
"It would be helpful if they could provide some dashboards where you can easily view charts and information."
"The initial setup is somewhat complex and the out-of-the-box configuration requires optimization."
"We would like to see Greenplum maintain a closer relationship with and parity to features implemented in PostgreSQL."
"Tanzu Greenplum's compression for GPText could be made more efficient."
"They should add more analytics. Their documentation could also be improved so that I don't have to bother my co-workers and tech support so often."
"Initial setup is a little complex. It took around two weeks to deploy."
"They need to enhance integration with other Big Data products... to integrate with Big Data platforms, and to open a bi-directional connection between Greenplum and Big Data."
"they need to interact more with customers. They need to explain the features, especially when there are new releases of Greenplum. I know just from information I've found that it has other features, it can be used to for analytics, for integration with Big Data, Hadoop. They need to focus on this part with the customer."
"Extra filters would be helpful."
BigQuery is ranked 5th in Cloud Data Warehouse with 31 reviews while VMware Tanzu Greenplum is ranked 9th in Data Warehouse with 36 reviews. BigQuery is rated 8.2, while VMware Tanzu Greenplum is rated 7.8. The top reviewer of BigQuery writes "Expandable and easy to set up but needs more local data residency". On the other hand, the top reviewer of VMware Tanzu Greenplum writes "Very efficient at large scale analytics; lacks inbuilt machine-learning functions for complex use cases". BigQuery is most compared with Snowflake, Teradata, Oracle Autonomous Data Warehouse, Vertica and IBM Db2 Warehouse, whereas VMware Tanzu Greenplum is most compared with Oracle Exadata, Vertica, Oracle Database Appliance, Apache Hadoop and Presto. See our BigQuery vs. VMware Tanzu Greenplum report.
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