Exploring data is like solving a puzzle. You create queries and receive instant satisfaction when you discover insights, just like adding pieces to complete a puzzle. Imagine you have to repeat the same analysis multiple times, use libraries from an open-source community, share your steps and output with others, and save your work as an artifact. Notebooks helps you create one place to write your queries, add documentation, and save your work as output in a reusable format.
Jupyter Notebook allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. Its includes data cleaning and transformation, numerical simulation, statistical modeling, and machine learning.
We are excited to announce KQL magic commands which extends the functionality of the Python kernel in Jupyter Notebook. In the following example we run a multi-line query and render a pie chart using the ploy. If you are a Python user, you can place the result set into a pandas dataframe.
Our exciting capabilities will allow you to have fun with your data analytics. Blog Big Data. Common use cases Data science : Data scientists use KQL magic to analyze and visualize data from Azure Data Explorer, easily interchange Python code with KQL queries to experiment, train, score machine learning models, and also save notebooks as artifacts. Data analytics : Use KQL magic to query, analyze, and visualize data, with no Python knowledge needed. For Python users, easily query data from Azure Data Explorer and use various open-source libraries from the Python ecosystem.
Business reviews : Use KQL magic for business and product reviews. Create the notebook once and refresh with new values every time you use it. Incident response : Use KQL magic to create operational documents, chain-up your queries for easy investigation, save the notebook for reproducibility and artifacts for remote connectivity analyzer RCA.
Security analytics : Query data from Azure Data Explorer and use the rich Python ecosystem for security analytics to analyze and visualize your data. For example, one of the internal Microsoft security teams uses KQL magic with Juypter for standard analysis patterns to triage security alerts, they have been transforming incident response playbooks into parameterized Jupyter Notebooks to automate repetitive investigation workflows.
Getting started Our exciting capabilities will allow you to have fun with your data analytics.Julie Koesmarno.
Users can now connect and browse their Azure Data Explorer clusters and databases, write and run KQL, as well as author notebooks with Kusto kernel, all equipped with IntelliSense.
By enabling native Kusto KQL experiences in Azure Data Studio, users such as data engineers, data scientists, or data analysts can now quickly discover insights as well as identify trends and anomalies against a massive amount of data stored in Azure Data Explorer.
Users working with heterogeneous data sources can now do data exploration and data analysis from SQL and Big Data Clusters to Azure Data Explorer without breaking their flow.
The Kusto Query Language
By supporting KQL natively with IntelliSense, users can benefit from optimized experience for fast and rich functionalities on a large amount of real-time streaming datasets in Azure Data Explorer. For more interactive data exploration, users can visualize the resultset from the KQL query in SandDance. Combined with the Kusto kernel addition to Notebook in Azure Data Studio, it makes it easy to create reproducible analyses in notebooks.
Notebooks provide the benefits of being able to capture code, results and context on the analysis. Below is an example of pattern detection in Storm Events data using autocluster plugin in Kusto notebook in Azure Data Studio accessing data from Azure Data Explorer databases:.
Engineers working on apps with telemetry connected to Azure Data Explorer can easily create a troubleshooting runbook or playbook in Azure Data Studio with Kusto kernel.
These runbooks or playbooks, detailing how to troubleshoot apps via telemetry data and how to mitigate, can be stored as notebooks with different kernel types, organized as a Jupyter Book.
For example, diagnosis steps and pattern or anomaly detections may be expressed as notebooks with Kusto kernel, and mitigation notebooks in PowerShell or other kernels. Please feel free to submit your suggestions and bugs on GitHub. You must be logged in with your Microsoft Account to post a comment. SQL Server Blog.
Efficiency in data exploration and data analysis Users working with heterogeneous data sources can now do data exploration and data analysis from SQL and Big Data Clusters to Azure Data Explorer without breaking their flow. Reproducible analyses Combined with the Kusto kernel addition to Notebook in Azure Data Studio, it makes it easy to create reproducible analyses in notebooks.
Below is an example of pattern detection in Storm Events data using autocluster plugin in Kusto notebook in Azure Data Studio accessing data from Azure Data Explorer databases: 3. Writing KQL notebooks. Older post Newer post.Azure Application Insights Tutorial - Amazing telemetry service
Languages such Read more. Download Azure Data Studio and Logged in as.Kqlmagic is a command that extends the capabilities of the Python kernel in Azure Data Studio notebooks. Kqlmagic brings you the benefit of notebooks, data analysis, and rich Python capabilities all in the same location. You can test if Kqlmagic is loaded properly by browsing the help documentation or by checking for the version. If Samples help is asking for a password, then you can leave it blank and press Enter.
This section explains how to run data analysis using Kqlmagic with an Azure Data Explorer cluster.
You use Device Login to authenticate. Copy the code from the output and select authenticate which opens a browser where you need to paste the code. Once you authenticate successfully, you can come back to Azure Data Studio to continue with the rest of the script. Query data using the render operator and visualize data using the ploy.
This query and visualization supplies an integrated experience that uses native KQL. Skip to main content. Contents Exit focus mode. Create a new notebook and change the Kernel to Python 3. When asked, select Yes to upgrade the Python packages. Install Kqlmagic:! Note If Samples help is asking for a password, then you can leave it blank and press Enter. Note Use your mouse to drag on an area of the chart to zoom in to the specific date s.
Is this page helpful? Yes No. Any additional feedback? Skip Submit. Submit and view feedback for This product This page. View all page feedback.Let statements bind names to expressions.
For the rest of the scope, where the let statement appears, the name can be used to refer to its bound value. The let statement may be within a global scope or a function body scope. If that name was previously bound to another value, the "innermost" let statement binding is used. Let statements improve modularity and reuse, since they let you break a potentially complex expression into multiple parts. Each part is bound to a name through the let statement, and together they compose the whole.
They can also be used to create user-defined functions and views. The views are expressions over tables whose results look like a new table.
The tabular expression that is used in the lambda invocation must include but is not limited to all the attributes with the matching types.
Any tabular expression can be used in the lambda invocation and none of its columns can be accessed in the lambda expression. Multiple let statements can be used with the semicolon, ;delimiter between them, like in the following example. Nested let statements are permitted, and can be used inside a lambda expression.
Let statements and arguments are visible in the current and inner scope of the function body. The following example binds the name x to the scalar literal 1and then uses it in a tabular expression statement. This example is similar to the previous one, only the name of the let statement is given using the ['name'] notion. This example uses the let statement with arguments for scalar calculation. The query defines function MultiplyByN for multiplying two numbers.
This example defines two let statements where one statement foo2 uses another foo1. The materialize function lets you cache subquery results during the time of query execution. Skip to main content. Contents Exit focus mode. Note Names bound by let statements must be valid entity names. Note The tabular expression that is used in the lambda invocation must include but is not limited to all the attributes with the matching types.
All tabular arguments should appear before the scalar arguments. Is this page helpful? Yes No. Any additional feedback? Skip Submit. Submit and view feedback for This product This page. View all page feedback. An expression that yields a lambda, an anonymous function declaration that is to be bound to the name.Returns a table with how many sell transactions and the total amount per fruit and sell month.
The output columns show the count of transactions, transaction worth, fruit, and the datetime of the beginning of the month in which the transaction was recorded. A table that shows how many items have prices in each interval [0, This example has a column for the count and one for the price range.
All other input columns are ignored. The input rows are arranged into groups having the same values of the by expressions. Then the specified aggregation functions are computed over each group, producing a row for each group.
The result contains the by columns and also at least one column for each computed aggregate. Some aggregation functions return multiple columns. The result has as many rows as there are distinct combinations of by values which may be zero. If there are no group keys provided, the result has a single record. To summarize over ranges of numeric values, use bin to reduce ranges to discrete values. When using these aggregates over entities which includes null values, the null values will be ignored and won't participate in the calculation see examples below.
Determine what unique combinations of ActivityType and CompletionStatus there are in a table. There are no aggregation functions, just group-by keys. The output will just show the columns for those results:. Finds the minimum and maximum timestamp of all records in the Activities table. There is no group-by clause, so there is just one row in the output:. Create a row for each continent, showing a count of the cities in which activities occur.
Because there are few values for "continent", no grouping function is needed in the 'by' clause:. The following example calculates a histogram for each activity type.
Because Duration has many values, use bin to group its values into minute intervals:.We already created the environment in the previous section, and now, we will extend our knowledge by first creating the tables using the Kusto explorer, and then import the data in the table from an external source.
This is technically called data ingestion. After creating tables and ingesting data to them we can move forward and use Kusto Query Language aka KQL to explore the data. We can use such queries to discover patterns, identify anomalies and outliers, create statistical modeling and more.
At the end you should get your data validated by SMEs or stakeholders. This is where you would wish to share the data.
Finally, after the data has been validated, the visualized data needs to be presented. There are multiple different ways to share the visualized data. KQL Kusto Query Language was developed with certain key principals in mind, like — easy to read and understand syntax, provide high-performance through scaling, and the one that can transition smoothly from simple to complex query. Interestingly KQL is a read-only query language, which processes the data and returns results. It is very similar to SQL with a sequence of statements, where the statements are modeled as a flow of tabular data output from the previous statement to the next statement.
These statements are concatenated with a pipe character. We will see how this works shortly. KQL is the query language and the Kusto Engine is the engine that receives the queries in KQL to execute them, and specifically the large datasets from Azure, like —. Apart from these, the data can be ingested from external sources as well. It can be done using the custom code in any preferred language like Python. Net SDK, R, etc. You definitely need to know and learn about entity typesData typestabular operatorsscalar operatorsfunctionsscalar functionstime-series analysisand other important KQL control commands.
They provide sample weather data from previous years in the CSV format for data analysis purposes. We are using the StormEvents.
There are certain demo platforms that are provided by Microsoft, which can be used free of cost for practice purposes. They are for —. These platforms also have saved queries that can be used to get an insight into how queries are formed and complex queries can be built. You can save your queries as well. There are two dedicated courses by Robert Cain on the Kusto Query Language on Pluralsight, which gives you deeper insight into KQL and that course is highly recommended for you as a data engineer as it details out the different kinds of commands and capabilities of KQL.
They are —.This article shows you a list of functions and their descriptions to help get you started using Kusto Query Language. Skip to main content.
Contents Exit focus mode. For example, ago 1h is one hour before the current clock's reading. Use for structuring unstructured data. Values can be lambda expressions to create ad-hoc functions as part of the query. Use let to create expressions over tables whose results look like a new table. Yes No. Any additional feedback? Skip Submit. Submit and view feedback for This product This page.
View all page feedback. Is this page helpful? Find relevant data by filtering or searching. Contains : Looks for any substring match Has : Looks for a specific word better performance. Returns the specified number of records. Produces a table with the distinct combination of the provided columns of the input table. Returns the time offset relative to the time the query executes. Returns data in various date formats. T extend [ColumnName ColumnName[, Restructure the data by sorting or grouping them in meaningful ways.
Sorts the rows of the input table by one or more columns in ascending or descending order.
T sort by expression1 [asc desc], expression2 [asc desc], …. Returns the first N rows of the dataset when the dataset is sorted using by.
T top numberOfRows by expression [asc desc] [nulls first last]. Groups the rows according to the by group columns, and calculates aggregations over each group. Counts records in the input table for example, T This operator is shorthand for summarize count. Merges the rows of two tables to form a new table by matching values of the specified column s from each table. Supports a full range of join types: flouterinnerinneruniqueleftantileftantisemileftouterleftsemirightantirightantisemirightouterrightsemi.
Restructure the data to output in a useful way. Extends the columns of a fact table with values looked-up in a dimension table. Evaluates a string expression and parses its value into one or more calculated columns. Binds a name to expressions that can refer to its bound value. Operations that display the data in a graphical format.