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."The most valuable features of BigQuery is that it supports standard SQL and provides good performance."
"One of the most significant advantages lies in the decoupling of storage and compute which allows to independently scale storage and compute resources, with the added benefit of extremely cost-effective storage akin to object storage solutions."
"The initial setup is straightforward."
"We basically used it to store server data and generate reports for enterprise architects. It was a valuable tool for our enterprise design architect."
"The product’s most valuable feature is its ability to manage the database on the cloud."
"The most valuable features of this solution, in my opinion, are speed and performance, as well as cost-effectiveness."
"The interface is what I find particularly valuable."
"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."
"A very good, open-source platform."
"The loading speed is very good."
"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."
"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."
"Very fast for query processing."
"Pivotal Greenplum's shared-nothing architecture."
"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."
"The most valuable feature for us is horizontal scaling."
"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."
"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."
"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."
"The main challenges are in the areas of performance and cost optimizations."
"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."
"The solution hinges on Google patterns so continued improvement is important."
"The primary hurdle in this migration lies in the initial phase of moving substantial volumes of data to cloud-based platforms."
"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."
"I saw some limitation with respect to the column store, and removing this would be an improvement."
"One of the disadvantages, not a disadvantage with the product itself, but overall, is the expertise in the marketplace. It's not easy to find a Greenplum administrator in the market, compared to other products such as Oracle."
"Lacks sufficient inbuilt machine-learning functions for complex use cases."
"We would like to see Greenplum maintain a closer relationship with and parity to features implemented in PostgreSQL."
"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."
"VMware Tanzu Greenplum needs improvement in the memory area and improved methods for quick access to the disc. So, one of the quick goals of Greenplum must work on enhancing access to the disc by adding hints in the database."
"It will be very useful if we could communicate with other database types from Greenplum (using a database link)."
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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