Google BigQuery vs. Panoply

It can easily fetch the App ratings and reviews from the marketplaces. You can slide and dice the data and share the results with different data personas with varying access levels. It is very well suited to the Looker BI solution and is just a few clicks away. It can run and schedule queries easily and dump the data in a preferred location.

Incentivized

Panoply is well suited for companies with large amounts of data that do not want all users of the company to see or view. It is appropriate for companies that have to store data for several years in case clients need the data. It is not made to constantly be downloading or looking at the documents.

Incentivized

• First and foremost - Google BigQuery is great at quickly analyzing large amounts of data, which helps us understand things like customer behavior or product performance without waiting for a long time.
• It is very easy to use. Anyone in our team can easily ask questions about our data using simple language, like asking ChatGPT a question. This means everyone can find important information from our data without needing to be a data expert.
• It plays nicely with other tools we use, so we can seamlessly connect it with things like Google Cloud Storage for storing data or Data Studio for creating visual reports. This makes our work smoother and helps us collaborate better across different tasks.

Incentivized

• Ease of data integrations. Importing data into the data warehouse is a point-and-click effort.
• Automated data modeling. The data is modeled in a way that facilitates analysis without having to look into it myself.
• Query speed. Automated query optimization has really sped up our dashboards.

• It is challenging to predict costs due to BigQuery's pay-per-query pricing model. User-friendly cost estimation tools, along with improved budget alerting features, could help users better manage and predict expenses.
• The BigQuery interface is less intuitive. A more user-friendly interface, enhanced documentation, and built-in tutorial systems could make BigQuery more accessible to a broader audience.

Incentivized

• The user interface could use some improvement for analytical tools. Apart from that, it’s great.

We have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.

Incentivized

web UI is easy and convenient. Many RDBMS clients such as aqua data studio, Dbeaver data grid, and others connect. Range of well-documented APIs available. The range of features keeps expanding, increasing similar features to traditional RDBMS such as Oracle and DB2

It’s a good choice for building a quick, multipurpose data stack for a variety of businesses needs.

BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.

Incentivized

First and foremost, Google BigQuery's pricing structure, based on data processing and storage, is more cost-effective for our needs. Secondly, since we already use other Google Cloud services, its tight integration with them especially, with Cloud Storage and Dataflow was a big plus and streamlined data transfer and simplified our workflows. Apart from that, as my team deals with large datasets and complex queries, we need a serverless architecture technology that has an edge in terms of query speed and scalability for our specific needs.

Incentivized

Panoply has much more storage and better organization tools for searching. There are better folders and tools for keeping documents secure and stored for a long period of time. It is better to offload data in mass than singular or Zip drive documents like you would do in Google Drive.

Incentivized

None so far. Very satisfied with the transparency on contract terms and pricing model.

Google Support has kindly provide individual support and consultants to assist with the integration work. In the circumstance where the consultants are not present to support with the work, Google Support Helpline will always be available to answer to the queries without having to wait for more than 3 days.

• Integrate well with google data visualization tools that can save a lot of money on licensing of other tools.
• Requires basic SQL skills which are transferrable and saves money spent on training.
• Gives an all out data retrieval and storage service which makes it a lot easier for employees across organization to fetch data.

Incentivized

• Saved time on Redshift setup
• Saved support costs (monetary and temporal) by not having to set up on-premise
• Widens the BI tool use in organization; lowers the barrier to entry and makes organization more productive in analytical terms