Google BigQuery

What differentiates Google BigQuery from other similar products?

In our company, we were searching for a dependable and user-friendly cloud-based data warehousing solution. Additionally, its potential to integrate with other technologies was what drove the purchase. It was, in my opinion, a great tool for securely handling huge data queries and storing data in a warehouse.

What is your favorite aspect of this product?

Because BIgQuery is self-managed, it is highly straightforward to develop and build a data model without having to spend time allocating resources for every possible use case. Additionally, they provide a variety of price alternatives to meet diverse needs, protect data, and provide configuration options for appropriate governance.

What do you dislike most about this product?

But for us, Google Cloud BigQuery's scalability, cloud-native design, connection with other services on the Google Cloud Platform, usability, and quick query performance more than make up for these potential limitations. All the data was processed promptly using a secure method. Small problems are not a huge deal.

What recommendations would you give to someone considering this product?

Bigquery can be used in conjunction with SQL and allows you to express complicated reasoning in JavaScript. We utilize Colab to create rapid prototypes, and using Google Colab to connect to Bigquery was quite simple. Give it a try; its capabilities will let you operate more efficiently and save massive amounts of data in real-time.

What differentiates Google BigQuery from other similar products?

It offers links to BI tools like Looker or PowerBI among others, and it includes API interactions with various GCP tools. BigQuery effectively addresses the needs of many customers that want to create their data solutions while considering performance, usability, security, and cost.

What is your favorite aspect of this product?

We had a terrific experience with it. Because BigQuery is self-managed, it is highly straightforward to develop and build a data model without having to spend time allocating resources for every possible use case. Additionally, they provide a variety of price alternatives to meet diverse needs, protect data, and provide configuration options for appropriate governance.

What do you dislike most about this product?

After the table is built, more columns cannot be indexed. Once inserted, a row cannot be modified. Additionally, once the table is established, the table schema cannot be changed. Because of this, to add a new column, one must first create a new table, copy all of the existing columns, and then add the new column to the new table only.

What recommendations would you give to someone considering this product?

BigQuery is an effective solution that makes it simple and quick to use SQL to examine massive datasets. Because of how simple to use and how well it interfaces with other Google Cloud services, it is a tremendous asset for our company. I advise you to do this for excellent work.

What differentiates Google BigQuery from other similar products?

There isn't really anything that differentiates Google BigQuery from others other than maybe SQL syntax-specific quirks. The capabilities and features are on par with others,

What is your favorite aspect of this product?

Since BigQuery's page layout is similar to Redshift and Snowflake, it was an easy transition. In addition, it's very easy to create snapshots. In addition, it's very off-hands, meaning that resources and power can automatically be managed. Also, BigQuery lets you know how expensive a query is prior to running it.

What do you dislike most about this product?

While there isn't anything that I dislike, I think that the platform could do a better job in notifying users of changes that could be made to further optimize our use of BigQuery (whether it's writing queries better, organizing schemas better, and so on).

What recommendations would you give to someone considering this product?

BigQuery gets the job done just like any other competitor like Redshift or Snowflake. However, in rare situations where it matters, most people come from a background having used Redshift or Snowflake only, and therefore, when writing SQL, some things specific to BigQuery might take time getting used to. Other than that, I would take time to architect how you want the infrastructure to look prior to creating. However, if mistakes are made, they are easily correctable.