DP-900: Microsoft Azure Data Fundamentals Study Guide
Microsoft Azure provides an array of services that enable businesses and organizations to undergo digital transformation by making quick and informed decisions. The DP-900 Microsoft Azure Data Fundamentals exam evaluates learners' understanding of data concepts such as relational, non-relational, big data, and analytics. The exam requires learners to demonstrate their knowledge of core data concepts, relational and non-relational data, and Azure data services. Microsoft offers resources such as Microsoft Learn self-paced curriculum, an instructor-led course, and related documentation to help students prepare for the exam. Understanding Azure data principles is vital for more advanced Azure certifications like Azure Database Administrator Associate and Azure Data Engineer Associate.A Step-by-Step Guide to migrate data from Elasticsearch to Azure Data Explorer(ADX) using Logstash
Data migration is the process of transferring data from one source to another. It can be a complex and time-consuming task, especially when dealing with large amounts of data. This article is an extension to an existing article to migrate data from Elastic Search to Azure Data Explorer (ADX) using Logstash pipeline as a step-step-step guide.Devito Book Summer Project with Imperial College London
If you are interested in numerical computation, programming in Python, and/or applied mathematics, and would like to contribute to our open-source textbook, feel free to reach out to us on Slack and check out the devito_book repository on GitHub.Diagnose sudden changes in your app behavior with a click!
Spikes or steps in the telemetry of your app can now be easily diagnosed. When you use Application Insights Analytics to explore app metrics over time, sudden changes, such as spikes or dips, are highlighted. With one click, Smart Diagnostics will find a pattern (a series of data with common values) that correlates with the change, and explain the reason behind it. After you’ve created a time chart that includes an unusual change, click a highlighted data point. Smart Diagnostics finds a filter pattern that explains the data discontinuity – it identifies the pattern in which the discrepancy occurs, isolates it, and displays the result with and without the filter. Read more about it on the Azure blog.
Understanding the Differences Between SWITCHOFFSET and AT TIME ZONE in SQL Server
When working with date and time data in SQL Server, handling different time zones can be a critical aspect, especially for applications with a global user base. SQL Server provides two functions that can be used to handle time zone conversions: SWITCHOFFSET and AT TIME ZONE. Although they might seem similar at first glance, they have distinct differences in functionality and use cases. This article aims to elucidate these differences and help you decide which one to use based on your requirements. SWITCHOFFSET The SWITCHOFFSET function is used to change the time zone offset of a datetimeoffset value without changing the actual point in time that the value represents. Essentially, it shifts the time by the specified offset. Syntax SWITCHOFFSET (DATETIMEOFFSET, time_zone_offset) DATETIMEOFFSET: The date and time value with the time zone offset you want to change. time_zone_offset: The new time zone offset, in the format +HH:MM or -HH:MM. Example DECLARE @dt datetimeoffset = '2023-12-31 23:09:14.4600000 +01:00'; SELECT SWITCHOFFSET(@dt, '+00:00') AS UtcTime; In this example, SWITCHOFFSET converts the time to UTC by applying the +00:00 offset. AT TIME ZONE The AT TIME ZONE function is more advanced and versatile compared to SWITCHOFFSET. It converts a datetime or datetime2 value to a datetimeoffset value by applying the time zone conversion rules of the specified time zone. It can also be used to convert a datetimeoffset value to another time zone. Syntax DATETIME [AT TIME ZONE time_zone] DATETIME: The date and time value to be converted. time_zone: The target time zone name. Example DECLARE @dt datetimeoffset = '2023-12-31 23:09:14.4600000 +01:00'; SELECT @dt AT TIME ZONE 'UTC' AS UtcTime; In this example, AT TIME ZONE converts the datetimeoffset to the UTC time zone. Key Differences Functionality: SWITCHOFFSET only adjusts the time by the specified offset without considering daylight saving rules or historical time zone changes. AT TIME ZONE considers the full time zone conversion rules, including daylight saving changes, making it more accurate for real-world applications. Input and Output: SWITCHOFFSET works with datetimeoffset values and outputs a datetimeoffset value. AT TIME ZONE works with datetime, datetime2, and datetimeoffset values and outputs a datetimeoffset value. Use Cases: Use SWITCHOFFSET when you need a quick offset change without needing full time zone awareness. Use AT TIME ZONE when you need precise and accurate time zone conversions, especially when dealing with historical data and daylight saving time. Performance Considerations When working with large datasets, performance is a crucial aspect to consider. SWITCHOFFSET: Generally faster for simple offset changes as it performs a straightforward arithmetic operation. AT TIME ZONE: May incur additional overhead due to the complexity of applying time zone rules, but it provides accurate results for real-world time zone conversions. Example with a Large Dataset Suppose you have a Users table with 200,000 records, each having a CreatedDate column with datetimeoffset values in various time zones. Converting these to UTC using both methods can illustrate performance differences. -- Using SWITCHOFFSET SELECT COUNT(*) FROM Users WHERE CAST(SWITCHOFFSET(CreatedDate, '+00:00') AS date) = '2024-01-01'; -- Using AT TIME ZONE SELECT COUNT(*) FROM Users WHERE CONVERT(date, CreatedDate AT TIME ZONE 'UTC') = '2024-01-01'; In scenarios like this, benchmarking both methods on your specific dataset and SQL Server environment is advisable to understand the performance implications fully. CPU Times vs Total Duration Let's analyze the efficiency of the two alternatives (SWITCHOFFSET and AT TIME ZONE) when working with a table containing 200,000 records with different time zones in the datetimeoffset field named CreatedDate. Example Table Preparation First, create an example table Users with a CreatedDate field of type datetimeoffset and insert 200,000 records with different time zones. sql -- Create the example table CREATE TABLE Users ( UserID INT IDENTITY(1,1) PRIMARY KEY, CreatedDate DATETIMEOFFSET ); -- Insert 200,000 records with different time zones DECLARE @i INT = 1; WHILE @i <= 200000 BEGIN INSERT INTO Users (CreatedDate) VALUES (DATEADD(MINUTE, @i, SWITCHOFFSET(SYSDATETIMEOFFSET(), CONCAT('+', RIGHT('0' + CAST((@i % 24) AS VARCHAR(2)), 2), ':00')))); SET @i = @i + 1; END; Measuring Efficiency Now, measure the two alternatives for converting the CreatedDate field to UTC and then projecting it as date. Option 1: SWITCHOFFSET sql SET STATISTICS TIME ON; SELECT CAST(SWITCHOFFSET(CreatedDate, '+00:00') AS date) AS UTCDate FROM Users; SET STATISTICS TIME OFF; Option 2: AT TIME ZONE sql SET STATISTICS TIME ON; SELECT CONVERT(date, CreatedDate AT TIME ZONE 'UTC') AS UTCDate FROM Users; SET STATISTICS TIME OFF; Execution Plan and Timing Analysis After running both queries, compare the CPU times and the total duration reported by SET STATISTICS TIME ON to evaluate efficiency. Possible Efficiency Differences SWITCHOFFSET: SWITCHOFFSET is likely more efficient in this scenario because it performs a single operation to adjust the time zone and then projects it as date. This operation is done in a single step, which can reduce overhead. AT TIME ZONE: AT TIME ZONE might introduce a slight overhead because it first changes the time zone and then converts it to date. However, AT TIME ZONE is clearer and can handle multiple time zones more explicitly. Recommendation Although the real efficiency can depend on the specific environment and the detailed execution plan, generally, SWITCHOFFSET is expected to be more efficient for large datasets when only adjusting the time zone and projecting the date is required. Code for Testing in SQL Server sql -- Option 1: SWITCHOFFSET SET STATISTICS TIME ON; SELECT CAST(SWITCHOFFSET(CreatedDate, '+00:00') AS date) AS UTCDate FROM Users; SET STATISTICS TIME OFF; -- Option 2: AT TIME ZONE SET STATISTICS TIME ON; SELECT CONVERT(date, CreatedDate AT TIME ZONE 'UTC') AS UTCDate FROM Users; SET STATISTICS TIME OFF; Comparing Results CPU Times: Compare the CPU times reported by both queries. Total Duration: Compare the total duration of execution of both queries. Evaluating the results from the time statistics will help determine which option is more efficient for your specific case. Additional Considerations Indexes: Ensure that the CreatedDate column is indexed if large volumes of data are expected to be read. Parallelism: SQL Server can handle the query in parallel to improve performance, but parallelism settings might affect the results. Real-World Workload: Conduct tests in an environment as close to production as possible to obtain more accurate results. Conclusion Choosing between SWITCHOFFSET and AT TIME ZONE depends on your specific needs: Use SWITCHOFFSET for simple, quick offset changes where historical accuracy and daylight saving adjustments are not critical. Use AT TIME ZONE for comprehensive and accurate time zone conversions, especially in applications dealing with users across multiple time zones and needing historical accuracy. Understanding these differences will help you make informed decisions in your SQL Server applications, ensuring both performance and accuracy in your date and time data handling.Power BI Embedded dashboards with Azure Stream Analytics
Azure Stream Analytics is a fully managed “serverless” PaaS service in Azure built for running real-time analytics on fast moving streams of data. Today, a significant portion of Stream Analytics customers use Power BI for real-time dynamic dashboarding. Support for Power BI Embedded has been a repeated ask from many of our customers, and today we are excited to share that it is now generally available. Read about it in the Azure blog.
