Venturing into Data Science and deciding on a tool to use to solve a given problem can be challenging at times especially when you have a wide array of choices. In this age of data transformation where organizations are constantly seeking out ways to improve the day to day handling of data being produced and looking for methods to minimize the cost of having these operations, it has become imperative to handle such data transformations in the Cloud as it is a lot easier to manage and is also cost-efficient.
Data Warehousing architectures have rapidly changed over the years and most of the notable service providers are now Cloud-based. Therefore, companies are increasingly on the move to align with such offerings on the Cloud as it provides them with a lower upfront cost, enhances scalability, and performance as opposed to traditional On-premise Data Warehousing systems. Google BigQuery is among one of the well-known and widely accepted Cloud-based Data Warehouse Applications.
With data being the driving force behind all the strategic as well as operational decisions in the modern-day enterprise, providing the right information, at the right time, to the right person is the key. With the ever-increasing flowing information, both relevant as well as irrelevant, grasping the crux is of utmost importance. Google BigQuery provides its users the autonomy to extract, visualize and analyze only the relevant data in any given dataset.
In this article, you will gain information about BigQuery SUBSTR function. You will also gain a holistic understanding of Google BigQuery, its key features, substrings, and the significance of using the BigQuery SUBSTR function. Read along to find out in-depth information about the BigQuery SUBSTR function.
Table of Contents
- Introduction to Google BigQuery
- Understanding Substrings
- Understanding the BigQuery SUBSTR Function
- Working with BigQuery Left and Right Functions
Introduction to Google BigQuery
Google BigQuery is a Cloud-based Data Warehouse that provides a Big Data Analytic Web Service for processing petabytes of data. It is intended for analyzing data on a large scale. It consists of two distinct components: Storage and Query Processing. It employs the Dremel Query Engine to process queries and is built on the Colossus File System for storage. These two components are decoupled and can be scaled independently and on-demand.
Google BigQuery is fully managed by Cloud service providers. We don’t need to deploy any resources, such as discs or virtual machines. It is designed to process read-only data. Dremel and Google BigQuery use Columnar Storage for quick data scanning, as well as a tree architecture for executing queries using ANSI SQL and aggregating results across massive computer clusters. Furthermore, owing to its short deployment cycle and on-demand pricing, Google BigQuery is serverless and designed to be extremely scalable.
For further information about Google Bigquery, follow the Official Documentation.
Key Features of Google BigQuery
Some of the key features of Google BigQuery are as follows:
- Data Types
- Data Loading
- Data Recovery
- Pricing Models
Partitioning is supported by BigQuery, which improves Query performance. The data may be readily queried using SQL or Open Database Connectivity (ODBC).
Being quite elastic, BigQuery separates computation and storage, allowing customers to scale processing and memory resources according to their needs. The tool has significant vertical and horizontal scalability and runs real-time queries on petabytes of data in a very short period.
When a third-party authorization exists, users can utilize OAuth as a standard approach to get the cluster. By default, all data is encrypted and in transit. Cloud Identity and Access Management (IAM) allows for fine-tuning administration.
Google BigQuery is a highly user-friendly platform that requires a basic understanding of SQL commands, ETL tools, etc.
5) Data Types
It supports JSON and XML file formats.
6) Data Loading
It employs the conventional ELT/ETL Batch Data Loading techniques by employing standard SQL dialect, as well as Data Streaming to load data row by row using Streaming APIs.
In addition to operational databases, the system supports integration with a wide range of data integration tools, business intelligence (BI), and artificial intelligence (AI) solutions. It also works with Google Workspace and Cloud Platform.
8) Data Recovery
Data backup and disaster recovery are among the services provided by Google BigQuery. Users can query point-in-time snapshots of data changes from the last seven days.
9) Pricing Models
The Google BigQuery platform is available in both on-demand and flat-rate subscription models. Although data storage and querying will be charged, exporting, loading, and copying data is free. It has separated computational resources from storage resources. You are only charged when you run queries. The quantity of data processed during searches is billed.
With the ever-growing digitization, enterprises are investing big in Analysis and Insights Extraction. Powered by Google, BigQuery is a high-performance managed data house following the Platform as a Service (PaaS) business model. It essentially lays its foundations on the serverless architecture and utilizes the cloud-based network/storage for enabling the enterprise to take data-driven optimal decisions.
As the data increases in its scale and scope, the aspects of business that aren’t required as such for planning and strategy also get recorded, resulting in data clutter. Data clutter, though doesn’t sound much, can overshadow some critical insights and subsequently can result in considerable opportunity costs. Major components of any dataset can be strings, integers, floats, constants, booleans, and special characters, etc.; Google BigQuery provides users with the functionality to cut the clutter and display only the requisite information. Not only clutter, sometimes selective data is required to get better insights out of it. For the string and bytes data, users can visualize, update and modify certain parts of it using the BigQuery SUBSTR function.
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Understanding the BigQuery SUBSTR Function
Strings and bytes data types can be sorted by many techniques in Google BigQuery but the most widely used parameter in the ANSI SQL model is the BigQuery SUBSTR function.
- BigQuery SUBSTR Function: Syntax
- BigQuery SUBSTR Function: Usage
- BigQuery SUBSTR Function: Examples Code/Queries
The syntax for the BigQuery SUBSTR function is very user-intuitive and is given as follows:
SUBSTR(value, position[, length])
It will return the substring component of the given value. The position argument belongs to the integer family and specifies the starting point of the substring with the value of position = 1 indicating the first character or byte. If the position is entered as -1, the substring will display the results from the last character of the given string or byte value.
The length argument indicates the maximum number of characters for STRING arguments or Bytes for BYTE arguments in the specified Substring parameter.
If position is a position at the left end of the STRING (position = 0 or position < -LENGTH(value), the function begins at position = 1. It returns less than length characters if the length exceeds the length of the value. The BigQuery SUBSTR function returns an error if the length is less than 0.
The use of the built-in BigQuery SUBSTR function can make complex calculations and visualizations very easy for the end-users. Not only it helps reduce the clutter during the analytics but also takes a smaller toll on the processing capacity of the system. Primarily used by the developers, the BigQuery SUBSTR function can be utilized in conjunction with other BigQuery parameters to enhance the performance.
C) Examples Code/Queries
The following example illustrates the usage of the BigQuery SUBSTR function:
SUBSTR('email@example.com', 9, 6)
Working with BigQuery Left and Right Functions
In normal SQL syntax, LEFT and RIGHT functions are supported which return specific values on the left or right side of the input parameter.
BigQuery LEFT Function
The LEFT function returns the value (can be STRING or BYTES type) consisting of leftmost characters from the given content or parameters.
The subject function is used to trim the characters from the complex entries in the dataset from the left side. The usefulness of this function becomes pretty evident while dealing with specific data types e.g., the email addresses, IP addresses, etc.
C) Example Queries
A very easy-to-understand example of the use of the LEFT function, quoted from Google BigQuery’s Official Library, is given as follows:
WITH examples AS
(SELECT 'apple' as example
SELECT 'banana' as example
SELECT 'абвгд' as example
SELECT example, LEFT(example, 3) AS left_example
| example | left_example |
| apple | app |
| banana | ban |
| абвгд | абв |
BigQuery Right Function
The RIGHT function returns the value (can be STRING or BYTES type) consisting of leftmost characters from the given content or parameters.
- BigQuery RIGHT Function: Syntax
- BigQuery RIGHT Function: Usage
- BigQuery RIGHT Function: Example Queries
The subject function is used to trim the characters from the complex entries in the dataset from the right side. The usefulness of this function becomes pretty evident while dealing with specific data types e.g., alphanumeric values, the ones involving special characters, etc.
C) Examples Queries
A very easy-to-understand example of the use of the RIGHT function, quoted from Google BigQuery’s Official Library, is given as follows:
WITH examples AS
(SELECT 'apple' as example
SELECT 'banana' as example
SELECT 'абвгд' as example
SELECT example, RIGHT(example, 3) AS right_example
| example | right_example |
| apple | ple |
| banana | ana |
| абвгд | вгд |
Key Consideration in using LEFT and RIGHT Functions
While working with Google BigQuery, unfortunately, some versions of SQL don’t support simple LEFT and RIGHT functions, rather they are used via the BigQuery SUBSTR function which in many aspects enhances the applicability of the parameters. The queries can be updated by making some minor changes in the syntax by replacing the LEFT function with BigQuery SUBSTR function as discussed below:
|Function||BigQuery Use with BigQuery SUBSTR||Description|
|LEFT (manuscript, A)||SUBSTR (manuscript, 1, A)||The 2nd parameter in the function will always be 1. The requisite length of the output can be entered in the 3rd argument i.e., A.|
|RIGHT (manuscript, B)||SUBSTR (text, –B, B)||The 2nd parameter in the function is the negative of the number of values the user wants in the output followed by the actual number.|
The use-cases of the aforementioned syntax i.e., RIGHT and LEFT functions with BigQuery SUBSTR function are given as follows:
|Function||BigQuery Use with BigQuery SUBSTR||Output|
|LEFT (“Commands”, 3)||SUBSTR (“Commands”,1,3)||Com|
|RIGHT (“Commands”, 4)||SUBSTR (“Commands”,-4,4)||ands|
In this article, you have learned about Google BigQuery, its key features, and Substrings. This article also provided information on the BigQuery SUBSTR function, their significance, and how they can replace LEFT and RIGHT functions.
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