This chapter covers date and time functionality in SQL Server, including building dates from component parts, formatting dates for reporting, and working with calendar tables.

Building dates

Break out a date into year, month, and day

Break a date and time into component parts

Date math and leap years

Rounding dates

Formatting dates for reporting

Formatting dates with CAST() and CONVERT()

Formatting dates with FORMAT()

Working with calendar tables

The benefits of calendar tables

Try out a calendar table

Joining to a calendar table

Working with Dates and Times

Here, we'll be converting strings and other inputs to date and time data types.

Building dates from parts

Build dates from parts

Build dates and times from parts

Build dates and times with offsets from parts

Translating date strings

Cast strings to dates

Convert strings to dates

Parse strings to dates

Working with offsets

Changing a date's offset

Using the time zone DMV to look up times

Converting to a date offset

Handling invalid dates

Try out type-safe date functions

Convert imported data to dates with time zones

Test type-safe conversion function performance

Converting to Dates and Times

In this chapter, we will learn techniques to aggregate data over time.  We will briefly review aggregation functions and statistical aggregation functions.  We will cover upsampling and downsampling of data.  Finally, we will look at the grouping operators.

Basic aggregate functions

Summarize data over a time frame

Calculating distinct counts

Calculating filtered aggregates

Statistical aggregate functions

Working with statistical aggregate functions

Calculating median in SQL Server

Downsampling and upsampling data

Downsample to a daily grain

Downsample to a weekly grain

Downsample using a calendar table

Grouping by ROLLUP, CUBE, and GROUPING SETS

Generate a summary with ROLLUP

View all aggregations with CUBE

Generate custom groupings with GROUPING SETS

Combine multiple aggregations in one query

Aggregating Time Series Data

In this chapter, we will learn how to use window functions to perform calculations over time, including calculating running totals and moving averages, calculating intervals, and finding the maximum levels of overlap.

Using aggregation functions over windows

Contrasting ROW_NUMBER(), RANK(), and DENSE_RANK()

Aggregate window functions

Calculating running totals and moving averages

Running totals with SUM()

Options

Running Total

Sum Across the Entire Range

Row-Based Frames

Investigating window frames

Calculating moving averages

Working with LAG() and LEAD()

Seeing prior and future periods

Seeing the prior three periods

Calculating days elapsed between incidents

Finding maximum levels of overlap

Analyze client data for potential fraud

Build a stream of events

Complete the fraud analysis

Wrapping up

Answering Time Series Questions with Window Functions

Calendar

Day Spa Rollup

Day Spa Visit

Incident Rollup

Incident Type

Imported Time

SQL Server has a robust set of tools to prepare, aggregate, and query time series data. This course will show you how to build and work with dates, parse dates from strings (and deal with invalid strings), and format dates for reporting. From there, you will see how SQL Server's built-in aggregation operators and window functions can solve important business problems like calculating running totals, finding moving averages, and displaying month-over-month differences using realistic sample data sets. You will also see how taking a different perspective on your data can solve difficult problems.

Intermediate SQL Server

Explore ways to work with date and time data in SQL Server for time series analysis

Time Series Analysis in SQL Server

Desenvolvedor do SQL Server

Fundamentos do SQL Server

Senior Data Science Content Developer

Start with some data

Reasoning through the problem

Algorithm, step 1

Algorithm, step 2

Algorithm, step 3

Let's practice!

https://s3.amazonaws.com/assets.datacamp.com/production/course_16943/subtitles/course_16943_518123e93943fccc977a43ff2d2fc998.vtt

Answering Time Series Questions with Window Functions - Finding maximum levels of overlap

Finding maximum levels of overlap

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