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Data Adventures

My personal journey in this intricate world of data and continuous efforts to make it more structured and well understood - Rayis Imayev.

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Chatting with Power BI: Who Benefits from This Conversation?

Posted by Rayis Imayev on November 24, 2025

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Down the Rabbit Hole: Dealing with Ad-Hoc Data Requests

Posted by Rayis Imayev on October 13, 2025

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What a Summer Camp Kitchen Still Teaches Me

Posted by Rayis Imayev on August 04, 2025

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Stepping into Event Streaming with Microsoft Fabric

Posted by Rayis Imayev on July 06, 2025

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Salesforce CDC Data Integration

Posted by Rayis Imayev on June 30, 2025

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Logging in Azure Data Factory data flows

Posted by Rayis Imayev on June 15, 2025

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Dealing with Changing Data Formats: Schema Drift in Azure Data Factory

Posted by Rayis Imayev on February 12, 2025

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Data Masking with Azure Databricks

Posted by Rayis Imayev on January 13, 2025

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What I learned volunteering at a children's summer camp

Posted by Rayis Imayev on September 13, 2024

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Extracting PostgreSQL database metadata for presentation in Excel format

Posted by Rayis Imayev on March 16, 2024

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Don't Trust the Defaults

Posted by Rayis Imayev on March 08, 2024

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Handling physical deletes from the source and continue populating your analytical data store

Posted by Rayis Imayev on October 15, 2023

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Azure DevOps: Enterprise Power BI report deployment with connections to Shared datasets

Posted by Rayis Imayev on July 15, 2023

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Adding microseconds to a timestamp in Azure Data Factory

Posted by Rayis Imayev on April 25, 2023

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Using Azure Data Factory to read and process REST API datasets

Posted by Rayis Imayev on April 10, 2023

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Metadata-driven pipelines in Azure Data Factory | Part 4 - Analytical Processing

Posted by Rayis Imayev on February 20, 2023

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Thinking about data points and more …

Posted by Rayis Imayev on August 28, 2022

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Azure DevOps: Deploying Power BI reports with a parameterized gateway-based Data Source

Posted by Rayis Imayev on August 08, 2022

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Azure DevOps: Merging code to Main branch from a specific branch only

Posted by Rayis Imayev on July 25, 2022

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Including reference data into a database deployment process or passing lookup tables to Production

Posted by Rayis Imayev on June 20, 2022

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Azure Data Factory Azure Power BI Data ADF Pipeline JSON Maps Variables Array

Azure Logic App Data Modeling DevOps CI/CD Excel Metadata Open Data Parameter Power Query SQL Server Visualization continuous integration ADF. Azure Data Factory Azure Function Data Factory Mapping Data Flows continuous delivery Analytics Durable Function Email Notification deployment ArcGIS ArgGIS Map Data Vault Database Databricks Flatten GIS GitHub Latitude Longitude PostgreSQL QGIS SQL Salesforce Shape Map Trigger API Attachment Azure Data Lake Store Azure DevOps Big Data Bing Map Blob CDC CSV Custom Shapefile Data Flow ESRI Error Fabric File Management Load Mapbox PASS Power Automate Flow Python REST API SQL Azure SQL Azure Database SSMS T-SQL Template WGS 84 Webhook Wrangling data flows XML 200 202 3D Map ADLS AI ARM Template Alerts Alter ow Animation Apache Spark Append Variable Architect Architecture Azure Data Studio Azure Databricks Azure Monitor Azure Service Bus Azure Storage Bayeux Blob Storage Container Build CDS CRM Calendar Change Data Capture Cloud Column Column mapping CometD Common Data Services Condition Copilot Custom Maps Custom Solution Data Architecture Data Copy Data Lake Data Source Data Systems Data warehouse DataForACause Database Design Default Value Delete Delete Activity Distance Drain mode Dual axes EPSG 4326 ER Studio Events Eventstream Execution Explode Extension Fail Filled Map Filter Flow map Function Function App Gallery Template Geometry Git Global Fishing Globe Map HDFS HTML HTTP Heatmap IF IF activity ISO Icon Map Ingestion Integration JSONB Lambda Architecture Latency Learning Log Analytics Logging Logic Operation Long polling Look Data M language Macro Maintenance Masking Merge Metrics Microsoft Microsoft Dynamics 365 Migration Monitor Narrative Science Network ODBC Oracle Parameterized Parent Child Nodes Parse Performance Personal Story Polling Polygon Post Deployment Power App Power Map PowerShell Predictive Analytics Primary Secondary axis Pulse Chart Queue Reference Data Reference Layer Release Resource Manager SQL Saturday SQL Server Project SSAS SSRS Scale Controller Schema Drift Send Message SharePoint Shared Dataset Solution Template Source Control Spark Spatial Data Spatial Functions Special character Statistics Status Code Stored Procedure Streamin Data Streaming Support Switch Activity System Variables Table Tabular Template Gallery Temporal table Throughput Time Time Slider TopoJSON Twitter UX Unlearning Upgrade Usability VS Studio Validation Variable Version Web Week arm-template-parameters-definition.json career custom parameter dbdiagram dbeaver er studio data architect erwin data modeler long running process microseconds oracle sql developer data modeler scale in scale out self-hosted agent timestamp

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Rayis Imayev

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2001 2021 3D Map1 ADF14 ADF. Azure Data Factory5 ADLS1 AI1 Alerts1 Alter ow1 Analytics4

Animation1 Apache Spark1 API2 Append Variable1 ArcGIS3 Architect1 Architecture1 ArgGIS Map3 ARM Template1 arm-template-parameters-definition.json1 Array7 Attachment2 Azure25 Azure Data Factory51 Azure Data Lake Store2 Azure Data Studio1 Azure Databricks1 Azure DevOps2 Azure Function5 Azure Logic App7 Azure Monitor1 Azure Service Bus1 Azure Storage1 Bayeux1 Big Data2 Bing Map2 Blob2 Blob Storage Container1 Build1 Calendar1 career1 CDC2 CDS1 Change Data Capture1 CI/CD6 Cloud1 Column1 Column mapping1 CometD1 Common Data Services1 Condition1 continuous delivery5 continuous integration6 Copilot1 CRM1 CSV2 Custom Maps1 custom parameter1 Custom Shapefile2 Custom Solution1 Data16 Data Architecture1 Data Copy1 Data Factory5 Data Flow2 Data Lake1 Data Modeling7 Data Source1 Data Systems1 Data Vault3 Data warehouse1 Database3 Database Design1 Databricks3 DataForACause1 dbdiagram1 dbeaver1 Default Value1 Delete1 Delete Activity1 deployment4 DevOps7 Distance1 Drain mode1 Dual axes1 Durable Function4 Email Notification4 EPSG 43261 ER Studio1 er studio data architect1 Error2 erwin data modeler1 ESRI2 Events1 Eventstream1 Excel6 Execution1 Explode1 Extension1 Fabric2 Fail1 File Management2 Filled Map1 Filter1 Flatten3 Flow map1 Function1 Function App1 Gallery Template1 Geometry1 GIS3 Git1 GitHub3 Global Fishing1 Globe Map1 HDFS1 Heatmap1 HTML1 HTTP1 Icon Map1 IF1 IF activity1 Ingestion1 Integration1 ISO1 JSON9 JSONB1 Lambda Architecture1 Latency1 Latitude3 Learning1 Load2 Log Analytics1 Logging1 Logic Operation1 Long polling1 long running process1 Longitude3 Look Data1 M language1 Macro1 Maintenance1 Mapbox2 Mapping Data Flows5 Maps8 Masking1 Merge1 Metadata6 Metrics1 microseconds1 Microsoft1 Microsoft Dynamics 3651 Migration1 Monitor1 Narrative Science1 Network1 ODBC1 Open Data6 Oracle1 oracle sql developer data modeler1 Parameter6 Parameterized1 Parent Child Nodes1 Parse1 PASS2 Performance1 Personal Story1 Pipeline11 Polling1 Polygon1 Post Deployment1 PostgreSQL3 Power App1 Power Automate Flow2 Power BI24 Power Map1 Power Query6 PowerShell1 Predictive Analytics1 Primary Secondary axis1 Pulse Chart1 Python2 QGIS3 Queue1 Reference Data1 Reference Layer1 Release1 Resource Manager1 REST API2 Salesforce3 Scale Controller1 scale in1 scale out1 Schema Drift1 self-hosted agent1 Send Message1 Shape Map3 Shared Dataset1 SharePoint1 Solution Template1 Source Control1 Spark1 Spatial Data1 Spatial Functions1 Special character1 SQL3 SQL Azure2 SQL Azure Database2 SQL Saturday1 SQL Server6 SQL Server Project1 SSAS1 SSMS2 SSRS1 Statistics1 Status Code1 Stored Procedure1 Streamin Data1 Streaming1 Support1 Switch Activity1 System Variables1 T-SQL2 Table1 Tabular1 Template2 Template Gallery1 Temporal table1 Throughput1 Time1 Time Slider1 timestamp1 TopoJSON1 Trigger3 Twitter1 Unlearning1 Upgrade1 Usability1 UX1 Validation1 Variable1 Variables8 Version1 Visualization6 VS Studio1 Web1 Webhook2 Week1 WGS 842 Wrangling data flows2 XML2

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Popular Posts

Continuous integration and delivery (CI/CD) in Azure Data Factory using DevOps and GitHub

(2019-Feb- 18) With Azure Data Factory (ADF) continuous integration, you help your team to collaborate and develop data transformation solutions within the same data factory workspace and maintain your combined development efforts in a central code repository. Continuous delivery helps to build and deploy your ADF solution for testing and release purposes. Basically, the CI/CD process helps to establish a good software development practice and aims to build a healthy relationship between development, quality assurance, and other supporting teams. Update 2020-Mar-15: Part 2 of this blog post published in 2020-Jan-28: http://datanrg.blogspot.com/2020/01/continuous-integration-and-delivery.html Update 2019-Jun- 24: Video recording of my webinar session on Continuous Integration and Delivery (CI/CD) in Azure Data Factory at a recent PASS Cloud Virtual Group meeting.

Executing Azure Data Factory pipelines by Power App / Automate Flow

(2021-Jun-30) One of my university professors liked to tell us a quote, “ The Sleep of Reason Produces Monsters ”, in a way to help us, his students, to stay active in our thinking process. I’m not sure if Francisco Goya , had a similar aspiration when he was creating his artwork with the same name. So, let me explain my reasons to create a solution to trigger Azure Data Factory (ADF) pipelines from a Power App and why it shouldn't be considered as a monster :-)

Data Masking with Azure Databricks

“ Alice : Would you tell me, please, which way I ought to go from here? The Cheshire Cat : That depends a good deal on where you want to get to.” — Lewis Carroll, Alice’s Adventures in Wonderland (2025-Jan-13) Working on the data masking project has been a long journey; writing about it will take some time. I’m fine if you’re not a big fan of long reads and prefer scrolling, skipping, or engaging in shorter, bite-sized learning experiences. I will keep this writing for myself as a chronicle of my journey along a path that may seem overwhelming. However, the experience throughout this journey and the joy of reaching the final destination will be hard to forget. When I think of data masking, or the term "masking" in particular — the intention to “hide” something from someone comes to mind. I picture a man in a mask, presumably to conceal his identity, or an image blurred beyond recognition. In the world of computer data, masking represents the process of obfuscating, redacting...

Append Variable activity in Azure Data Factory: Story of combining things together

(2018-Oct-29) There are only a few sentences in the official Microsoft web page that describe newly introduced activity task ( Append Variable) to add a value to an existing array variable defined in Azure Data Factory - Append Variable Activity in Azure Data Factory But it significantly improves your ability to control a workflow of the data transformation activities of your Data Factory pipeline.

Azure Data Factory: Extracting array first element

(2019-Apr- 28) Full credit goes to Microsoft for the latest efforts updating Azure products with new features and documenting corresponding changes for the end users. Azure Data Factory (ADF) is a great example of this. A user recently asked me a question on my previous blog post ( Setting Variables in Azure Data Factory Pipelines ) about possibility extracting the first element of a variable if this variable is set of elements (array). So as a spoiler alert, before writing a blog post and adding a bit more clarity to the existing Microsoft ADF documentation, here is a quick answer to this question. The first element of an ArrayVariable is extracted by this expression: @variables(' ArrayVariable ')[0] . I created a simple ADF pipeline to test this out:

Transforming JSON data with the help of Azure Data Factory - Part 4 - Passing Arrays around

(2020-May- 24) It has never been my plan to write a series of articles about how I can work with JSON files in Azure Data Factory (ADF). While working with one particular ADF component I then had discovered other possible options to use richness and less constrained JSON file format, which in a nutshell is just a text file with one or more ( " key " : " value " ) pair elements. Sometimes you could have an array of those pair elements or an array of other arrays; now you see where I'm going with the name of my current blog post :-)

Transforming JSON to CSV with the help of Flatten task in Azure Data Factory

(2020-Mar- 19) Recently, Microsoft introduced a new Flatten task to the existing set of powerful transformations available in the Azure Data Factory (ADF) Mapping Data Flows - https://docs.microsoft.com/en-us/azure/data-factory/data-flow-flatten . What this new task does it helps to transform/transpose/flatten your JSON structure into a denormalized flatten datasets that you can upload into a new or existing flat database table. 2020-Mar-26 Update: Part 2 : Transforming JSON to CSV with the help of Flatten task in Azure Data Factory - Part 2 (Wrangling data flows) I like the analogy of the Transpose function in Excel that helps to rotate your vertical set of data pairs ( name : value ) into a table with the column name s and value s for corresponding objects. And when this vertical JSON structural set contains several similar sets (array) then ADF Mapping Data Flows Flatten does a really good job by transforming it into a table with several rows (records)...

Passing JSON arrays between pipelines in Azure Data Factory

(2021-Feb-15) This post a continuation of my personal experience working with arrays (or simply JSON structures) in Azure Data Factory (ADF). It just happened that the more I work with JSON in ADF, the more interesting cases I have a chance to discover. In retrospect, these are some of my previous discoveries: Working with Arrays Setting default values for Array parameters/variables Extracting array first element Passing array of arrays as a parameter and the role of an @item element in this This next post came out of an error message during my attempt to pass a hard-coded array value between pipelines. Strangely, this use-case worked well in the pipeline that was already deployed in ADF, however, I was getting an error message while trying to test and execute this very same pipeline in a Debug mode. Photo by Zen Chung from Pexels

Working with Arrays in Azure Data Factory

(2019-June- 06) I remember that I had a professor at my university who would often encourage his students to learn and get more experienced with simple things first. Learn the basics in and out and then move forward to more complicated concepts and practices; that was his motto, and he really tried to share this idea with us. Previously published blog posts about using Variables in Azure Data Factory: - Setting Variables in Azure Data Factory Pipelines - Append Variable activity in Azure Data Factory: Story of combining things together - System Variables in Azure Data Factory: Your Everyday Toolbox - Azure Data Factory: Extracting array first element Simple things sometimes can be overlooked as well. With the addition of Variables in Azure Data Factory Control Flow (there were not available there at the beginning), Arrays have become one of those simple things to me. Image by Magnascan from Pixabay

Send a Database Query Result as Email via Logic App in HTML format using Azure Data Factory

(2020-June- 07) It is a very simple concept, you have a database query result, either a direct database table output or a stored procedure returned result that needs to be sent as email notification. This email could serve as a status report of your daily data processing job or alert notification with some metrics that require immediate attention, and you want to be as user-friendly as possible with this message aka HTML format for your dataset is required.

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