Scuba Tech Library

What is an Ad Hoc Query?

When digging into data, sometimes information is needed on a single case basis. An ad hoc query is used to get specific information from a database when the need arises, as opposed to standard queries that are predefined and processed on a regular, recurring basis. 

What are ad hoc queries used for?

Ad hoc queries are single questions or requests for a database written in SQL or another query language by the user on-demand--typically when the user needs information outside of regular reporting or predefined queries. 

In a nutshell, an ad hoc query is any kind of "question" you can ask a data system off the top of your head. For example, if a business tracks daily average users and finds one day that it's 3 percent lower than the previous day, a user would write a series of ad-hoc queries to try and identify why. You might check to see if specific regions or platforms (iOS/Android) were dramatically lower. A public holiday for one region, such as India, could explain it. Or an iOS issue that cut traffic to your site for iPhone users for half the day could explain it. The opposite of an ad hoc query is regular reporting via panels or dashboards that are numbers you're constantly tracking and aware of.

Benefits of ad hoc queries

  • Customization: Arguably the most obvious benefit of ad hoc queries and one of the primary reasons for using them is their customization. Ad hoc queries help a user dig deeper into data beyond the usual reports to find answers to more niche business questions. 
  • Flexibility: Similar to customization, ad hoc queries enable flexibility for users to find answers to specific questions on a case-by-case basis depending on their objectives at the time. 
  • More nimble decision-making: Ad hoc querying is done on-demand, which enables businesses to make data-driven decisions faster by quickly responding to dynamic changes. 

Cons of ad hoc queries

Like every technology, ad hoc queries have some drawbacks in addition to their benefits. Primarily, consideration needs to be taken for a potential increase in IT workload. If users with less advanced technical skills do not have the training or ability to run ad hoc queries on their own, it can be a time-consuming task for data scientists to take on. Additionally, depending on the complexity of the ad hoc query, they can come at a significant cost to a system’s processing speed and runtime memory.

Examples of ad hoc queries

Here are some examples of how ad hoc queries can be used in industry-specific cases: 

  • Retail: Ad hoc inquiries could be used to determine why sales lagged on a specific day or week. 
  • Sales: A business could use ad hoc queries to find data on sales from specific territories, or for specific items.
  • Healthcare: A mental health facility could use ad hoc queries to find the number of people admitted to psychiatric hospitalizations on a specific day or week.

Using ad hoc queries with Scuba

Ad hoc queries help businesses quickly dig deeper into their data and get answers to specific questions on demand, as needed. Data analytics tools, like the ones offered by Scuba Analytics, are designed to amplify the benefits of ad hoc queries by making data analytics more accessible and easier to digest for all stakeholders in a business--and enabling fast ad hoc queries on the fly without code or SQL.

Learn more about Scuba’s no-code queries, real-time reporting, and customizable dashboards and templates here

Data Science

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Data lakes and data warehouses are both commonly used for storing data, but there are key differences between the two that make them unique in their own way. Learn which fits your business purposes best and if there is a better solution.

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The term “data ecosystem” collectively refers to all the programming languages, algorithms, applications, and the general infrastructure used to collect, analyze and store data.

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Data modeling is a means of creating a conceptual framework for your data in preparation for storage in a data warehouse. The resulting model is a visual representation of the data which maps out the relationships between data, and the rules.

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What is Lambda Architecture?

Lambda architecture processes data through a hybrid combination of batch processing and stream processing.

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What is an ETL Pipeline?

ETL is a method to collect raw data from various sources, clean it up, and translate it so it can be used to inform decision making.

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What is Data Governance?

Data governance allows organizations to ensure high-quality data through formalized processes for management, monitoring, and control of data assets.

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What is Hadoop?

Apache Hadoop is one of the most widely used open source frameworks designed to address the problem of storing and processing big data.

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What is Parquet?

Unlike row-based formats such as CSV, Parquet is a columnar data file storage format.

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What is Querying?

A query is a question or request for a database written in a code the database can understand, in order to retrieve or modify the correct information.

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What is Metadata Storage?

When building a database, all data requires some description to help identify its uniqueness, which is where metadata comes in.

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What is a Data Platform?

Data platforms are tools that allow businesses to collect, analyze, and present data.

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What is an Enterprise Data Warehouse?

An EDW is a database that centralizes data from across the business so it can be analyzed and used in decision making.

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What is Time-Series Data?

Time-series data analysis serves critical functions in most modern industries, and is a powerful method to glean accurate analysis.

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What is Data Sovereignty?

Data sovereignty defines the regulations data is subject to. Fortunately, there are actionable steps brands can take to ensure compliance.

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What is Self-Service Analytics?

Self-service analytics empower non-technical teams to interact with data, perform queries, and glean helpful business insights.

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