We performed a comparison between BigQuery and IBM Netezza Performance Server 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."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."
"What I like most about BigQuery is that it's fast and flexible. Another advantage of BigQuery is that it's easy to learn."
"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 feature called calibrating the capacity is valuable."
"The initial setup is straightforward."
"The product’s most valuable feature is its ability to manage the database on the cloud."
"The setup is simple."
"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."
"The performance is most important to me, and it helps our ability to make business decisions quickly."
"Distribution concurrency control."
"The data governance prospect... from what I've seen, that is a really powerful tool as well, to help with data lineage and keeping track of that."
"The benefit is really because of the additional speed that we have and, truth be told, the more updated ETL processes and the revamped scheduler in general."
"IBM Netezza Performance Server is a cost-effective solution."
"The underlying hardware that IBM provides with this appliance is made for a specific purpose, to serve performance on a large amount of data, and to do analytics as well. It is faster, when you compare it to any other product."
"The most valuable feature would be the fact that it has been running for awhile in an appliance format."
"The most valuable features of the IBM Netezza Performance Server are the NPS server because of the reduced maintenance and overall good performance."
"Some of the queries are complex and difficult to understand."
"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."
"We would like to be able to calibrate the solution to run on top of a raw file."
"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."
"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."
"As a product, BigQuery still requires a lot of maturity to accommodate other use cases and to be widely acceptable across other organizations."
"I rate BigQuery six out of 10 for affordability. It could be cheaper."
"The initial setup could be improved making it easier to deploy."
"Oracle Exadata's security features, like TDE encryption, are missing in IBM Netezza Performance Server."
"The scalability is not as expected. The capacity in the black box is not enough."
"Concurrency limit needs to be increased somewhat."
"We are not able to scale. The only way to scale is to get another appliance, but we have a customers who would need us to hydrate the data between the two appliances, and Netezza does not do that."
"Our main problem with it is concurrency. When there are too many users running Netezza at the same time, this is when we have the most complaints."
"LIke Teradata, we can’t add a node/SPU to the existing appliance."
"The only issue is that it's not expandable."
"In terms of features that I would like to see, one is the ability to actually scale out an architecture. Right now, if you buy one, it's fixed. There is no scale-up availability at all."
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BigQuery is ranked 5th in Cloud Data Warehouse with 31 reviews while IBM Netezza Performance Server is ranked 10th in Data Warehouse with 33 reviews. BigQuery is rated 8.2, while IBM Netezza Performance Server is rated 8.0. 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 IBM Netezza Performance Server writes "A cost-effective data warehousing tool, but security features like TDE encryption are missing". BigQuery is most compared with Snowflake, Teradata, Oracle Autonomous Data Warehouse and Vertica, whereas IBM Netezza Performance Server is most compared with Oracle Exadata, Oracle Database, Snowflake, Teradata and SQL Server. See our BigQuery vs. IBM Netezza Performance Server report.
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