Has anyone found a solution to adding source control to Azure Log Analytics Workspace Functions? (KQL queries saved on the Azure GUI)
I have a number complex queries and I would like to track changes over time. So far my searches have not come up with anything.
Azure has a vast support in REST API
Docs / Log Analytics / Saved Searches / Saved Searches - Create Or Update
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Can anyone share a Kusto query (KQL) that I can use in log analytics that would return some usage tracking stats?
I am trying to identify which "Views" and "Tables" are used the most. Also trying to find out who the power users are and commands/query that is run against the "Tables".
Any insights would be appreciated.
You can use below functions to gather the useage statics
DiagnosticMetricsExpand()
DiagnosticLogsExpand()
ActivityLogRecordsExpand()
And create target tables to store the function data to analyse the useage information.
Refer the Azure documentation for complete details https://learn.microsoft.com/en-us/azure/data-explorer/ingest-data-no-code?tabs=activity-logs
Tutorial: Ingest monitoring data in Azure Data Explorer without code
In this tutorial, you learn how to ingest monitoring data to Azure Data Explorer without one line of code and query that data.
I can create an Azure data lake database with pre-built tables using Azure Synapse database templates from the Synapse Studio UI, but is there a way to use these templates programmatically? So far from my research I have not found a command, API, or SDK for this. Perhaps I could create the database and tables via the UI, then generate the associated spark sql creation scripts, but don't see a way how to do that either. Does anyone have any ideas on how to do either of the prior?
You can create the data lake storage, tables and data insertion programmatically using Azure SDKs. But these templates have been made available to overcome these series of manual tasks. Using these templates save your time and efforts to create an environment and sample data for your development.
Therefore, asking to deploy these templates programmatically challenging the complete concept of templates. If you want to deploy these resources manually, you can use Azure SDKs.
I want to load many tables which is in aws rds mysql server by using cloud data fusion. each table storage is more than about 1gb. also I found the plugin which name is "multiple database table" to load multi table. but i got a fail. Also basically when I used database source I can check my tables' schema. However, in multiple database table, i can 't find how to check table's schema. how can i use this plugin? or is there any other way to load many tables in data fusion service?
My pipeline setting was as follows.
I'm posting this Community Wiki as OP didn't provide enough details to reproduce but the below information might help someone.
There are few ways to get your data using Cloud Data Fusion, you can use pipeline, plugin, driver and a few others depending on your needs.
On the internet you can find two very well described guides with examples.
If you would like to find some information about Cloud Data Fusion with GCP products you should read Bahadir Bulut guide - How I used Google Cloud Data Fusion to create a data warehouse - Part 1 and Part 2. Also Data Fusion allows to use 150+ preconfigured connectors and transformations like Amazons S3, SQS, etc. Azure services and many more.
Another well described (which I guess would help OP) is to configure both Amazon and GCP resources and using pipelines. This guide is Building a Simple Batch Data Pipeline from AWS RDS to Google BigQuery — Part 1: Setting UP AWS Data pipeline and second part Building a Simple Batch Data Pipeline from AWS RDS to Google BigQuery — Part 2: Setting up BigQuery Transfer Service and Scheduled Query.. In short this guide describes 2 main steps:
Extract data from MYSQL RDS and bring into S3 using AWS data pipeline service
From S3, bring the file inside Bigquery using BigqQuery transfer service.
There is an option to connect a Cloud mySQL instance from BigQuery. I just wanted to know how we can connect a Cloud SQL Server instance to BigQuery.
SQL Server:
There are a bunch of third-party extensions/tools that provide this service. One of them is SSIS Data Flow Source & Destination for Google BigQuery, which is Visual Studio extension that connects SQL Server with Google BigQuery data through SSIS Workflows.:
https://www.cdata.com/drivers/bigquery/ssis/
https://marketplace.visualstudio.com/items?itemName=CDATASOFTWARE.SSISDataFlowSourceDestinationforGoogleBigQuery
In regards to using SQL Server Integration Services to load the data from the on-premises SQL Server to BigQuery, you can take a look for this site. You can also perform ETL from a relational database into BigQuery using Cloud Dataflow, the official documentation details how it can be done, you might need to use Cloud Storage as an intermediate data sink.
Cloud SQL:
BigQuery allows to query data from Cloud SQL by using federated query. The connection must be created within the same project where your Cloud SQL instance is located. If you want to query your data stored in your Cloud SQL instance from BigQuery located in another project, please follow the steps listed below:
Enable the BigQuery API and the BigQuery connection API within your project.
Create a connection to your Cloud SQL instance within the project by following this documentation.
Once you have created the connection, please locate and select it within BigQuery.
Click on the SHARE CONNECTION button and grant permissions to the users that will be use that connection. Please note that the BigQuery Connection User role is the only needed to use a shared connection.
Additionally, please notice that the "Cloud SQL federated queries" feature is in a Beta stage and might change or have limited support (is no available for certain regions, in which case, it is required to use one the supported options mentioned here). Please remember, that to use Cloud SQL Federated queries in BigQuery, the intances need to have a public IP.
If you are limited e.g. by region, one good option might be exporting the data from CloudSQL to Storage as a CSV, and then load it into BigQuery. If you need, it is possible to automate this process using Cloud Composer, refer to this article.
Other approach is to extract information from Cloud SQL (with exports) and import it into BigQuery through load jobs, or streaming inserts.
I hope you find the above pieces of information useful.
It is possible, but be warned the feature is currently Beta
https://cloud.google.com/bigquery/docs/cloud-sql-federated-queries
Not great at programming so not all that confident of being able to pull data from the Google API.
Basically what I'm trying to do is pull everything from Google Analytics, all data and dump it into a table on a SQL Server (2008 R2). What would be the best way of achieving this?
Would like to automate the process so that it could be scheduled say once a month.
Cheers
There an existing add-on specifically for accessing Google Analytics.
Haven't used it but from what I read seems like it will exactly what you are looking for
http://www.google.com/analytics/apps/about?app_id=1325007
There is a specific SSIS DataSource Component for that – https://analyticssource.codeplex.com
It is open source and free.