Interactive Course

Time Series Analysis in SQL Server

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

  • 5 hours
  • 16 Videos
  • 60 Exercises
  • 1,112 Participants
  • 5,200 XP

Loved by learners at thousands of top companies:

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Course Description

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.

  1. 1

    Working with Dates and Times

    Free

    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.

  2. 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.

  3. Converting to Dates and Times

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

  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.

  1. 1

    Working with Dates and Times

    Free

    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.

  2. Converting to Dates and Times

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

  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.

  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.

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Kevin Feasel
Kevin Feasel

CTO, Envizage

Kevin Feasel is a Microsoft Data Platform MVP and CTO at Envizage, where he specializes in data analytics with T-SQL and R, forcing Spark clusters to do his bidding, fighting with Kafka, and pulling rabbits out of hats on demand. He is the lead contributor to Curated SQL (https://curatedsql.com) and author of PolyBase Revealed (forthcoming). A resident of Durham, North Carolina, he can be found cycling the trails along the triangle whenever the weather's nice enough.

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