Databricks DATEDIFF Function

Databricks is a well-known cloud-based data engineering, processing, and analytics platform. One of its key functions is DATEDIFF(date_diff()) used by data professionals widely. The DATEDIFF function in Databricks is very helpful in analyzing time-based data. Using this function helps the user do complex operations like finding time differences between two date values. It is used for events tracking, trend analysis, and even performance measurement. This very powerful tool helps users make efficient manipulations and comparisons of dates so meaningful insights may be obtained from the data sets. The DATEDIFF function in Databricks makes it easy to get accurate date-related calculations. It’s very useful when performing time-series analysis and in reports where date differences play a crucial role.

In this blog, we will review the fundamentals of Databricks, explain the purpose and benefits of the date_diff() function, and show you how to utilize it efficiently for data analysis tasks.

What is Databricks?

Databricks is a unified cloud-based data science, engineering, and analytics platform that aims to simplify data integration, processing, and analytics with solutions from business intelligence to Generative AI. Databricks was created by the creators of Apache Spark, on top of Apache Spark, leveraging its scalability and performance.

Databricks enables users to integrate data sources into a single platform for processing, storing, sharing, analyzing, modeling, and monetizing with simple, intelligent business and Generative AI solutions according to business requirements.

Key Features of Databricks

Let’s briefly explain the top key features of Databricks:

• Databricks provides a unified interface and tools for most data tasks.
• It provides an unrivaled ETL (extract, transform, load) and data engineering experience.
• Generates dashboards and visualizations with business intelligence.
• Easily manage integration with open-source Data Lake, ML flow, Apache Spark, etc.
• It is a highly available platform that supports multiple cloud providers (AWS, Azure, Google Cloud, IBM Cloud).
• It manages security, governance rules, and regulations, along with disaster recovery.
• Expands functionality to Machine learning (ML) modeling, and model serving as per ML engineers’ needs.
• It offers the most demanding Generative AI and Large Language Models(LLM) solutions.
• It is scalable for large-scale data processing, analytics, and modeling as it handles large data efficiently.

What is Databricks datediff?

Databricks provides an extensive collection of integrated libraries and tools that improve the platform’s analytics and machine-learning capabilities. These wide ranges of libraries and functions can be easily applied to big datasets and thus enable data analysts, data scientists, and ML engineers to build complex models without extensive coding.

DATEDIFF(datediff()) is one of the key in-built functions of Databricks. datediff() function in Databricks is used to calculate the difference between two dates or timestamps, and the results are in a specified unit. It is widely used to perform time-based calculations in the datasets.

The difference in Date is typically measured in days, but in the timestamp version of the datediff() function, the difference can also be measured in other time units such as years, months, weeks, hours, seconds, or even milliseconds.

Syntax of datediff() function

datediff(endDate, startDate) #Date only version

datediff(unit, start, end) #Timestamp version

Why Should You Use Databricks Datediff Function?

In Databricks, the primary purpose of the datediff() function is to measure the elapsed duration between two points in date and time. This capability is useful in various analytical situations, such as:

1. In calculating the total duration of various events and processes.
2. In calculating the customer retention period.
3. To analyze trends and patterns over time.
4. In computing age from birth dates.
5. In determining time-to-resolution for support tickets or issues.
6. In various other date-measuring or time-based metrics scenarios.

Step By Step Guide on How to use Databricks Datediff Function 

There are two versions of the DATEDIFF in Databricks. The date-only version gives results in days only and the timestamp version gives results in hours, months, seconds, and many other time units. Let’s discuss these two versions in detail.

1. Date-only Version

In the date-only version datediff(), the difference between two dates is calculated, and the time component is not considered. The datediff() function returns an integer value that represents the number of days between the start date and the end date.

Let’s examine Databricks DATEDIFF’s function. We’ll review its syntax, the units it works with, and practical applications.

Syntax

datediff(endDate, startDate)

Arguments Explanation

‘endDate’: A DATE expression represents end date.

‘startDate’: A DATE expression represents the start date.

Returns

An INTEGER value represents the number of days between two dates i.e. startDate and endDate.

Example in SQL

> SELECT datediff('2024-11-10', '2024-11-08');

>> 2

> SELECT datediff(''2024-11-09', '2024-11-10');

>> -1

Note: If endDate is before startDate, the result is negative, as shown in the above example.

2. Timestamp Version

In the timestamp version datediff(), the difference between two dates is calculated at a more granular level, including the difference in specific time units. The function returns the value as specified in the function as an argument, which can be the year, month, hour, second, or even milliseconds.

Let’s examine Databricks DATEDIFF’s timestamp function. We’ll go through its syntax, the units it works with, and practical applications.

Syntax

datediff(unit, start, end)

Here, the unit can be one of the following mentioned:

unit

 { MICROSECOND |

   MILLISECOND |

   SECOND |

   MINUTE |

   HOUR |

   DAY |

   WEEK |

   MONTH |

   QUARTER |

   YEAR }

Arguments Explanation

‘unit’: A measuring unit for the return value.

‘start’: A starting TIMESTAMP expression.

‘end’: A ending TIMESTAMP expression.

Returns

A BIGINT value that represents the difference between start and end timestamps.

With a DAY of 86400 seconds, the function counts entire elapsed units depending on UTC. When the calendar month has expanded and the day and time are equal to or larger than the beginning, one month is said to have elapsed. From then it’s weeks, quarters, and years. 

Example in SQL

> SELECT datediff(MONTH, TIMESTAMP'2024-10-10 12:00:00', TIMESTAMP'2021-11-10 11:59:59');
>> 0    #one second short of a month elapsed

> SELECT datediff(MONTH, TIMESTAMP'2024-9-10 12:00:00', TIMESTAMP'2021-11-10 12:00:00');
>> 2   

> SELECT datediff(MINUTE,'2024-11-10 00:00:00','2024-11-10 00:59:59');
>> 59    

> SELECT datediff(SECOND,'2024-11-10 00:00:00','2024-11-10 00:59:59');
>> 3599   

> SELECT datediff(MILLISECOND,'2024-11-10 00:00:00','2024-11-10 00:59:59');
>> 3599000  

> SELECT datediff(YEAR, DATE'2024-01-01', DATE'2001-11-11');
>> -22

Note: If the ‘start’ is greater than the ‘end’ then the result is negative as shown in the above example.

Benefits of Using Databricks Datediff Function 

DATEDIFF function in Databricks offers several benefits for data processing and analysis. Let’s explore its advantages in detail:

1. Efficient Handling of Time-Based Calculations

The Databricks DATEDIFF function is super efficient and flawless in managing time-based computations. Its distributed computing model speeds up the process of analyzing big datasets and shoots up the computation performance.

The Databricks DATEDIFF function gives consistent results across big datasets and handles date and time complex logic calculations.

2. Scalable

As data grows and its scale goes up, Databricks DATEDIFF scales with it. It keeps up with millions or billions of records and performs efficiently.

3. Enhanced Data Analysis Capabilities

Databricks DATEDIFF makes it much simpler to identify patterns and trends across time by enhancing data analysis. datediff() enhances data analysis capabilities in the following manner:

• Trends identification like seasonal sales spikes, growth rates, and site usage surges by calculating the time difference.
• Recognizing patterns by identifying time cycles, customer behavior, or system performance fluctuations.
• Get more detailed insights by segmenting data by time.
• Data is grouped by users or events by similar time frames or intervals for cohort analysis.

4. Data Workflows Automation

The DATEDIFF function of Databricks helps in various aspects of data workflow automation. It automates the following aspects of data workflows:

• It autocalculates time-based metrics during ETL processes.
• In reports, scheduling datediff() is used
• Helps in providing time-based KPIs consistently.
• Keep a quality check for anomalies in time-stamped data and track unreasonable long processing time.

5. Flexible and Versatile

Databricks DATEDIFF is super flexible in terms of date and time-based calculations as it gives results in a wide range of units like years, months, days, quarters, weeks, hours, seconds, minutes, milliseconds, and microseconds as per requirements.

Databricks DATEDIF functions work with both positive and negative time intervals. It can analyze what happened in the past and can also forecast what can happen in the future.

Databricks DATEDIFF is super versatile as it can combine with other functions easily and do more complex calculations and analyses.

6. Standardization and Consistency

Databricks DATEDIFF supports standardization as the entire team is calculating time differences the same way. It also works consistently for data analysis and handles tricky situations, resulting in error reduction.

Explore how Databricks’ architecture supports advanced functionalities like the DATEDIFF function in our detailed guide on Understanding Databricks Architecture.

Limitations of Databricks Datediff Function 

Along with the super benefits offered by Databricks DATEDIFF function, it also has some limitations. Let’s see the limitations of Databricks DATEDIFF function.

• It always returns whole numbers and does not support fractional numbers.
• Databricks DATEDIFF does not account for Daylight Saving Time (DST) changes as its calculations are based on UTC.
• When your start date or time is later than your end date or time, Databricks DATEDIFF gives you a negative answer when the start date or timestamp is later than the end date or timestamp. So, engineers need to be careful with data analysis in case of negative results as it can create confusion.
• Databricks DATEDIFF doesn’t handle time zone conversions inherently.

Conclusion

Databricks DATEDIFF function is a well-known function when it comes to works with dates and timestamps. It can return value in the number of days, years, quarters, months, weeks, hours, minutes, seconds, milliseconds, or even microseconds. It is crucial to understand dates and timestamps in depth for time-series analytics, trends over time, pattern recognition across time, and automated date-related problems. By leveraging this function, data professionals can do thorough time-based analysis and make quick and efficient data-driven decisions. The DATEDIFF() is a revolutionary function that will change the way data professionals manage data workflow while implementing business intelligence solutions.

Companies need to analyze their business data stored in multiple data sources. The data needs to be loaded to the Databricks to get a holistic view of the data. Hevo Data is a No-code Data Pipeline solution that helps to transfer data from 150+ sources to desired Data Warehouse. It fully automates the process of transforming and transferring data to a destination without writing a single line of code.

Want to take Hevo for a spin? Try Hevo’s 14-day free trial and experience the feature-rich Hevo suite first hand.

FAQs