Working with Percentile_Cont BigQuery Function Made Easy


Percentile_Cont BigQuery Cover

Data is an increasingly valuable asset for companies, and the question of where to store it becomes more urgent. If you do not have the time or knowledge to maintain your own servers, you can use Google BigQuery. BigQuery’s fast, scalable, and cost-effective storage allows you to work with large amounts of petabytes of data. You can also use standard SQL syntax and user-defined functions to write queries.

This article discusses the Percentile_Cont BigQuery command. You’ll be taken through its type, syntax, and example query.

Table of Contents

What is BigQuery?

Percentile_Cont BigQuery: Logo
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BigQuery is a serverless data warehouse developed by Google. A cloud data warehouse helps businesses store and query their data based on a scalable, enterprise data platform. With BigQuery machine learning, a large amount of data can be uploaded, and using BigQuery’s processing power, you can drill deeper into your data to better understand it. 

The Benefits Of using BigQuery

Now that you have a better understanding of BigQuery, let’s look at how you can utilize it to your advantage.

The Setup is Quick and Easy

Trying to set up a data tool to aggregate all your data can take hours when you’re trying to run your business.

One of the most significant advantages of BigQuery is its ease and speed of setup. The tool processes all your incoming data in real-time as soon as you input it. A Database can be up and running in just a few minutes. BigQuery is an excellent choice for Data Management. With BigQuery, millions of rows of data can be processed in seconds. The warehouse will be ready for querying as soon as it has been created.

It’s User-friendly

One of BigQuery’s most significant features is its simplicity. Data Centres are costly and challenging to scale, and time-consuming to construct. The process is frustrating, and you can waste time understanding the data. BigQuery simplifies the process. Users just upload their data, then pay only for what they use. With it, you don’t have to worry about building your own Data Center and can efficiently process and analyze your data.

Scalability is Seamless

Scalability is one of the biggest challenges in importing data. Companies struggle with sizing their data correctly to make sense of it. You won’t have to worry about scaling with BigQuery. BigQuery stores and computes data separately. As a result of this process, elastic scaling is enabled, resulting in better performance as you scale. It allows for real-time analytics to be performed seamlessly and appropriately scales data so that you can understand it.

Access to Accelerated Insights

With BigQuery, you can see your data in its entirety. By using data tools, you can digest and analyze your data further. Data Studio and Tableau work seamlessly with BigQuery to assist you in exploring your data. These tools can be used to create reports and dashboards. Using BigQuery, you can quickly break down your data since it combines the data processed with these platform’s data tools.

Data Security is Guaranteed

Data is one of the most valuable assets for your business. BigQuery safeguards your data and keeps it secure. The benefit of this process is that you will not have to worry about disaster recovery if your data is compromised or lost, although you should always have a plan in case something happens.

It’s Reasonably Priced

Pricing for BigQuery is flexible. Resources are only charged for as they are used. Google charges you solely according to how much your company uses each tool, whether computing resources or storage space.

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Key Components of BigQuery

Having learned what BigQuery is, it’s time to understand its components. Let’s take a look at some of Google’s BigQuery’s key component features.

A Serverless Model of Service 

Probably one of the main features of BigQuery is its serverless architecture. As one of the most abstract, manageable, and automated technologies in the industry, BigQuery frees you from the constraints of virtual machines and CPU/RAM sizing. This scalable computing power allows BigQuery to scale to tens of thousands of cores in a matter of seconds while charging only for what is consumed; BigQuery is highly accessible, durable, and secure. 

An Opinionated Storage Engine

Using BigQuery, your storage is constantly evolving and optimized for you – for free and without interruption.

Google replaced GFS with Colossus. Colossus is incredibly powerful, scalable, and highly durable. The BigQuery data is stored in Colossus in Atomic Capacitor format. A key feature of BigQuery’s Capacitor is that it performs many optimizations under the hood without compromising query performance or cost.

For example, BigQuery can work with storage by automatically re-materializing data in cases where your tables contain too many small files. One whole generation of DBAs has been plagued by the “many small files” problem.

Standard SQL & Dremel Execution Engine

BigQuery is built on top of Dremel. However, it should be noted that Dremel has evolved a bit since it was first released. Queries in Dremel are like short-term tenants, whose scheduler performs Cirque Du Soleil-style gymnastics to keep them running smoothly. Due to the nature of Dremel, individual nodes are also immune to failure. In essence, Dremel is a multi-tenant computing cluster. 

The Jupiter Network 

Jupiter is the glue that holds everything together. Known as Google’s inter-data center network, Jupiter can handle a Petabit of bi-directional traffic, allowing BigQuery to transfer data from storage to compute without a hitch. 

Data Sharing at Enterprise Level

Combined with BigQuery’s ability to separate storage from computation and Colossus’ excellent capabilities, users can share Exabyte-scale datasets much like they can with Google Docs or Sheets. Data silos, created by copying data across disparate clusters, are not an anti-pattern in some architectures. Data Silos are problematic because they increase operational complexity, create an inefficient infrastructure, and, in the end, increase your expenses.

BigQuery does not use virtual machines as its storage layer (not even as accelerators), so you won’t have to worry about race conditions, locking, or hot-spotting, and you can forget about CLONE and SWAP commands entirely.

Furthermore, BigQuery’s serverless nature makes it possible to share data with other companies without requiring them to set up their own clusters. There is no hidden cost, and you pay for storage, and they pay for queries. 

Streaming and Batch Ingest

A unique feature of BigQuery is its Streaming API. Millions of rows per second can be streamed into BigQuery, and the data can be analyzed almost immediately. The solution to this problem is quite tricky for analytic databases, so kudos to the team for making it happen.

Percentile_Cont BigQuery: Streaming Ingestion
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Now that you’re quite familiar with BigQuery, it’s now time to understand the PERCENTILE_CONT BigQuery function.

Understanding Percentile_Cont BigQuery Function

As an Analyst, you will often find yourself wanting to find one number representative of the sample so that you can briefly describe it. In BigQuery, there are several ways to perform this task quickly. In some cases, you might decide between an analytic function, which computes values over a group of rows and provides a single result for each row, and an aggregate function, which provides a single result for a group of rows.

PERCENTILE_CONT BigQuery analytic function helps to compute the percentile of a continuous distribution using linear interpolation. You can use the PERCENTILE_CONT BigQuery function to calculate minimum, maximum, median, or any percentile values within a distribution. 

PERCENTILE_CONT ignores any NULL values in a dataset, and all interpolation between two NULL values will return NULL, while interpolation between two non-NULL values will return the non-NULL value. 

Value_expressions of functions must either be NUMERIC, BIGNUMERIC, or FLOAT64, while percentiles must be literals between 0 and 1.


In the following example, the value for some percentiles from a column of values is computed while respecting and ignoring nulls.

PERCENTILE_CONT BigQuery Example A: Calculation of some percentiles while ignoring nulls.

Percentile_Cont BigQuery: Example 1
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PERCENTILE_CONT BigQuery Example B: Calculation of some percentiles while respecting nulls.

Percentile_Cont BigQuery: Example 2
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BigQuery is the most trusted source for processing your data. It allows you to process relevant data securely and cost-effectively to generate actionable insights for your business. Businesses can process data more quickly and efficiently using BigQuery.

Using the PERCENTILE_CONT BigQuery function, you can easily calculate any percentile of your data distribution using linear interpolation.  By using this method, you can quickly check how one value compares to others in your data set.

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Share your experience of working with PERCENTILE_CONT BigQuery in the comments section below.

Samuel Salimon
Freelance Technical Content Writer, Hevo Data

Samuel specializes in freelance writing within the data industry, adeptly crafting informative and engaging content centered on data science by merging his problem-solving skills.

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