Actian Ingres vs BigQuery comparison

"The most valuable features for us are reliability and performance."

"The deployment of our solution across a number of servers using Ingres .NET has meant that we can protect the database server behind a highly secure firewall and deploy the front end solutions on a normal web server."

"The product is serverless. We only need to write SQL queries to analyze the data. We need to pay based on the number of queries. The retrieval time is very less. Even if you write large queries, the tool is able to bring back data in a few seconds."

"It's similar to a Hadoop cluster, except it's managed by Google."

"The initial setup process is easy."

"It's straightforward to set up."

"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 has a well-structured suite of complimentary tools for data integration and so forth."

"The product’s most valuable feature is its ability to manage the database on the cloud."

"BigQuery allows for very fast access, and it is efficient in handling large datasets compared to other SQL databases."

"The seamless integration is a bit of an issue."

"The ability to reset the log file without stopping the DBMS would be helpful for us."

"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."

"We'd like to see more local data residency."

"Instead of connecting directly to BigQuery, we connect to GCP, Cloud Run, and then to BigQuery, which is a long process."

"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."

"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."

"Some of the queries are complex and difficult to understand."

"When I execute a query, the dashboard doesn't always present the output seamlessly. Troubleshooting requires opening each pipeline individually, which is time-consuming."

"It can be slower and more problematic compared to other platforms such as Snowflake."

"BigQuery pricing can increase quickly. It's a high-priced solution."

"The solution is pretty affordable and quite cheap in comparison to PDP or Cloudera."

"The pricing is good and there are no additional costs involved."

"The tool has competitive pricing."

"One terabyte of data costs $20 to $22 per month for storage on BigQuery and $25 on Snowflake. Snowflake is costlier for one terabyte, but BigQuery charges based on how much data is inserted into the tables. BigQuery charges you based on the amount of data that you handle and not the time in which you handle it. This is why the pricing models are different and it becomes a key consideration in the decision of which platform to use."

"The solution's pricing is cheaper compared to other solutions."

"The platform is inexpensive."

"The pricing is adaptable, ensuring that organizations can tailor their usage and costs based on their specific requirements and configurations within the Google Cloud Platform."