课程

Time Series Analysis in SQL Server

中级技能水平

更新时间 2025年11月

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

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课程描述

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.

先决条件

Intermediate SQL Server

1

Working with Dates and Times

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

50 经验值

Break out a date into year, month, and day

100 经验值

Break a date and time into component parts

100 经验值

Date math and leap years

100 经验值

Rounding dates

100 经验值

Formatting dates for reporting

50 经验值

Formatting dates with CAST() and CONVERT()

100 经验值

Formatting dates with FORMAT()

100 经验值

Working with calendar tables

50 经验值

The benefits of calendar tables

50 经验值

Try out a calendar table

100 经验值

Joining to a calendar table

100 经验值

2

Converting to Dates and Times

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

Building dates from parts

50 经验值

Build dates from parts

100 经验值

Build dates and times from parts

100 经验值

Build dates and times with offsets from parts

100 经验值

Translating date strings

50 经验值

Cast strings to dates

100 经验值

Convert strings to dates

100 经验值

Parse strings to dates

100 经验值

Working with offsets

50 经验值

Changing a date's offset

100 经验值

Using the time zone DMV to look up times

100 经验值

Converting to a date offset

100 经验值

Handling invalid dates

50 经验值

Try out type-safe date functions

100 经验值

Convert imported data to dates with time zones

100 经验值

Test type-safe conversion function performance

100 经验值

3

Aggregating Time Series Data

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

50 经验值

Summarize data over a time frame

100 经验值

Calculating distinct counts

100 经验值

Calculating filtered aggregates

100 经验值

Statistical aggregate functions

50 经验值

Working with statistical aggregate functions

100 经验值

Calculating median in SQL Server

100 经验值

Downsampling and upsampling data

50 经验值

Downsample to a daily grain

100 经验值

Downsample to a weekly grain

100 经验值

Downsample using a calendar table

100 经验值

Grouping by ROLLUP, CUBE, and GROUPING SETS

50 经验值

Generate a summary with ROLLUP

100 经验值

View all aggregations with CUBE

100 经验值

Generate custom groupings with GROUPING SETS

100 经验值

Combine multiple aggregations in one query

100 经验值

4

Answering Time Series Questions with Window Functions

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

50 经验值

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

100 经验值

Aggregate window functions

100 经验值

Calculating running totals and moving averages

50 经验值

Running totals with SUM()

100 经验值

Investigating window frames

100 经验值

Calculating moving averages

100 经验值

Working with LAG() and LEAD()

50 经验值

Seeing prior and future periods

100 经验值

Seeing the prior three periods

100 经验值

Calculating days elapsed between incidents

100 经验值

Finding maximum levels of overlap

50 经验值

Analyze client data for potential fraud

100 经验值

Build a stream of events

100 经验值

Complete the fraud analysis

100 经验值

Wrapping up

50 经验值

Time Series Analysis in SQL Server

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