Entering the world of data and deciding which tool to use to solve a given problem can be difficult at times, especially when you have a plethora of options. In this age of Data Transformation, where organizations are constantly looking for ways to improve the day-to-day handling of data produced and methods to reduce the cost of having these operations, it has become critical to handle such data transformations in the Cloud as it is much easier to manage and also cost-efficient.
As a result, companies are increasingly moving to align with such Cloud-based offerings because it provides them with a lower upfront cost, improves scalability, and performance over Traditional On-premise Data Warehousing systems.
This article will teach you about Google BigQuery Timestamp_Diff Function. You will also gain a comprehensive understanding of Google BigQuery, its key features, Date & Time functions, and BigQuery Date Data Types. Continue reading to learn more about BigQuery Timestamp_Diff Function!
Table of Contents
- What is Google BigQuery?
- What are Google BigQuery Date & Time Data Types?
- Timestamp Functions Supported by Google BigQuery
What is Google BigQuery?
Google BigQuery is a Cloud-based Data Warehouse that offers a Big Data Analytic Web Service that can process petabytes of data. It is designed for Large-scale Data Analysis. It is divided into two parts: Storage and Query processing. It uses the Dremel Query Engine to process queries and stores them on the Colossus File System. These two components are decoupled and can be scaled separately and on-demand.
Google Cloud Service providers fully manage Google BigQuery. We don’t need to deploy any resources like CDs or Virtual Machines. It is intended to handle read-only data.
Columnar Storage is used by Dremel and Google BigQuery for quick data scanning, as well as a Tree Architecture for executing ANSI SQL Queries and aggregating results across massive Compute Clusters.
There are various Business Intelligence tools that can be integrated with Google BigQuery to provide Standard SQL Access. Google BigQuery, which uses Distributed Computing Technology within a Serverless Architecture, can quickly process massive amounts of data by using multiple parallel servers to significantly increase processing speed.
Key Features of Google BigQuery
Now that you understand what Google BigQuery is, let’s look at some of its features.
- Provides Access to Serverless Insight: Google BigQuery’s Serverless Architecture automates analytics scaling and easily highlights some of the most important insights.
- Real-time Analytics: The tool supports a High-speed Streaming insertion API, which aids in providing quick Real-time Analytics and assisting businesses in understanding the data.
- Data Transfer Services: Users can easily transfer data from any external source, such as Google Ads, Teradata, YouTube, Google Marketing Platform, Amazon S3, and others.
- Storage Computes Separation: Using Google BigQuery, one can separate their storage. You can design a Data Processing System based on your company’s needs and goals.
- Data Ingestion Formats: Google BigQuery allows users to load data in a variety of formats, including AVRO, CSV, JSON, and others.
- Built-in AI & ML: It supports Predictive Analysis with its Auto ML tables feature, a codeless interface that aids in the development of models with the highest accuracy in class. Another feature is that it supports algorithms such as K means, Logistic Regression, and so on.
What are Google BigQuery Date & Time Data Types?
The following data types are available in Google BigQuery for Time and Date:
- Date Type: It represents a calendar date, regardless of time zone. The following details are included: Date, Year, and Month: YYYY-MM-DD-YYYY-MM-DD-YYYY-MM (e.g. 2021-05-15).
- Time Type: It displays time in the same way that a digital watch does; it is not date-dependent. The format is as follows: HH:MM: SS (e.g. 16:45:14)
- Datetime: A type of data that includes both the calendar date and the time. It is not aware of time zones. The format is as follows: YYYY-MM-DD HH:MM: SS (e.g. 2021-05-15 16:45:23). You can also read our article about Datetime_Diff.
- Timestamps: These contain information such as the date, time, and time zone. If no time zone is specified, the format defaults to UTC. The format is as follows: YYYY-MM-DD [Timezone]. HH:MM:SS (e.g. 2021-05-15 16:45:18 UTC).
Simplify Google BigQuery ETL & Analysis with Hevo’s No-code Data Pipeline
A fully managed No-code Data Pipeline platform like Hevo Data helps you integrate and load data from 100+ Different Sources (including 40+ Free Sources) to a Data Warehouse such as Google BigQuery or Destination of your choice in real-time in an effortless manner. Hevo with its minimal learning curve can be set up in just a few minutes allowing the users to load data without having to compromise performance. Its strong integration with umpteenth sources allows users to bring in data of different kinds in a smooth fashion without having to code a single line.Get Started with Hevo for Free
Check out some of the cool features of Hevo:
- Completely Automated: The Hevo platform can be set up in just a few minutes and requires minimal maintenance.
- Transformations: Hevo provides preload transformations through Python code. It also allows you to run transformation code for each event in the Data Pipelines you set up. Hevo also offers drag and drop transformations like Date and Control Functions, JSON, and Event Manipulation to name a few. These can be configured and tested before putting them to use.
- Connectors: Hevo supports 100+ integrations to SaaS platforms, files, Databases, analytics, and BI tools. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake Data Warehouses; Amazon S3 Data Lakes; MySQL, SQL Server, TokuDB, DynamoDB, PostgreSQL Databases to name a few.
- Real-Time Data Transfer: Hevo provides real-time data migration, so you can have analysis-ready data always.
- 100% Complete & Accurate Data Transfer: Hevo’s robust infrastructure ensures reliable data transfer with zero data loss.
- Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (including 40+ free sources) that can help you scale your data infrastructure as required.
- 24/7 Live Support: The Hevo team is available round the clock to extend exceptional support to you through chat, email, and support calls.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Live Monitoring: Hevo allows you to monitor the data flow so you can check where your data is at a particular point in time.
Timestamp Functions Supported by Google BigQuery
Google BigQuery supports four groups of Date and Time functions: DATE, TIME, DATETIME, and TIMESTAMP. These groups include more specific functions like CURRENT DATETIME, DATE SUB, EXTRACT, FORMAT TIME, and so on.
These functions allow Google BigQuery users to manipulate date and time data types. They can, for example, retrieve a portion of a date or time expression, add an interval to a date or time, and so on.
TIMESTAMP(string_expression[, timezone]) TIMESTAMP(date_expression[, timezone]) TIMESTAMP(datetime_expression[, timezone])
- string expression[, timezone]: Converts a STRING expression to the data type TIMESTAMP.
- date expression[, timezone]: Converts a DATE Object to a TIMESTAMP Data Type.
- datetime expression[, timezone]: Converts a DATETIME object to a TIMESTAMP Data Type.
SELECT TIMESTAMP("2008-12-25 15:30:00+00") AS timestamp_str; -- Display of results may differ, depending upon the environment and time zone where this query was executed. +-------------------------+ | timestamp_str | +-------------------------+ | 2008-12-25 15:30:00 UTC | +-------------------------+
To learn more about Timestamp Functions, you can visit here! for more information
How to use the Google BigQuery Timestamp_Diff Function?
- Syntax of the Google BigQuery Timestamp_Diff Function
- Example Queries for Google BigQuery Timestamp_Diff Function
Syntax of the BigQuery Timestamp_Diff function
TIMESTAMP_DIFF(timestamp_expression, timestamp_expression, date_part)
The whole number specified by the date_part intervals between two timestamps objects i.e Timestamp a – Timestamp b is returned. The later date is represented by the first timestamp_expression; If the first timestamp_expression is earlier than the second timestamp_expression, the output is negative.
If the computation overflows the result type, such as if the difference in nanoseconds between the Two Timestamps would exceed an INT64 Value, Google BigQuery will throw an error.
For the date_part, the Google BigQuery Timestamp_Diff Function accepts the following values:
Example Queries for Google BigQuery Timestamp_Diff Function
SELECT TIMESTAMP "2010-07-07 10:20:00 UTC" as later_timestamp, TIMESTAMP "2008-12-25 15:30:00 UTC" as earlier_timestamp, TIMESTAMP_DIFF(TIMESTAMP "2010-07-07 10:20:00 UTC", TIMESTAMP "2008-12-25 15:30:00 UTC", HOUR) AS hours; +------------------------+------------------------+-------+ | later_timestamp | earlier_timestamp | hours | +------------------------+------------------------+-------+ | 2010-07-07 10:20:00+00 | 2008-12-25 15:30:00+00 | 13410 | +------------------------+------------------------+-------+ In the following example, the first timestamp occurs before the second timestamp, resulting in a negative output. SELECT TIMESTAMP_DIFF(TIMESTAMP "2018-08-14", TIMESTAMP "2018-10-14", DAY); +---------------+ | negative_diff | +---------------+ | -61 | +---------------+
You’ve learned about Google BigQuery, its key features, and Timestamps in this article. This article also covered the different types of Date & Type functions, as well as how to use the Google BigQuery Timestamp_Diff Function.
Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources and a wide variety of Desired Destinations such as Google BigQuery with a few clicks.Visit our Website to Explore Hevo
Hevo Data with its strong integration with 100+ Data Sources (including 40+ Free Sources) allows you to not only export data from your desired data sources & load it to the destination of your choice such as Google BigQuery, but also transform & enrich your data to make it analysis-ready so that you can focus on your key business needs and perform insightful analysis using BI tools.
Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing pricing, which will assist you in selecting the best plan for your requirements.
Share your experience of understanding the BigQuery Timestamp Diff Function in the comment section below! We would love to hear your thoughts.