1- Introduction of Azure data factory.pptxBRIJESH KUMAR
Azure Data Factory is a cloud-based data integration service that allows users to easily construct extract, transform, load (ETL) and extract, load, transform (ELT) processes without code. It offers job scheduling, security for data in transit, integration with source control for continuous delivery, and scalability for large data volumes. The document demonstrates how to create an Azure Data Factory from the Azure portal.
Azure Data Factory is a cloud data integration service that allows users to create data-driven workflows (pipelines) comprised of activities to move and transform data. Pipelines contain a series of interconnected activities that perform data extraction, transformation, and loading. Data Factory connects to various data sources using linked services and can execute pipelines on a schedule or on-demand to move data between cloud and on-premises data stores and platforms.
Azure Data Factory is a data integration service that allows for data movement and transformation between both on-premises and cloud data stores. It uses datasets to represent data structures, activities to define actions on data with pipelines grouping related activities, and linked services to connect to external resources. Key concepts include datasets representing input/output data, activities performing actions like copy, and pipelines logically grouping activities.
This document provides an overview of Azure Data Factory (ADF), including why it is used, its key components and activities, how it works, and differences between versions 1 and 2. It describes the main steps in ADF as connect and collect, transform and enrich, publish, and monitor. The main components are pipelines, activities, datasets, and linked services. Activities include data movement, transformation, and control. Integration runtime and system variables are also summarized.
Azure Data Factory Mapping Data Flow allows users to stage and transform data in Azure during a limited preview period beginning in February 2019. Data can be staged from Azure Data Lake Storage, Blob Storage, or SQL databases/data warehouses, then transformed using visual data flows before being landed to staging areas in Azure like ADLS, Blob Storage, or SQL databases. For information, contact adfdataflowext@microsoft.com or visit http://aka.ms/dataflowpreview.
Azure Data Factory is a cloud-based data integration service that orchestrates and automates the movement and transformation of data. In this session we will learn how to create data integration solutions using the Data Factory service and ingest data from various data stores, transform/process the data, and publish the result data to the data stores.
1- Introduction of Azure data factory.pptxBRIJESH KUMAR
Azure Data Factory is a cloud-based data integration service that allows users to easily construct extract, transform, load (ETL) and extract, load, transform (ELT) processes without code. It offers job scheduling, security for data in transit, integration with source control for continuous delivery, and scalability for large data volumes. The document demonstrates how to create an Azure Data Factory from the Azure portal.
Azure Data Factory is a cloud data integration service that allows users to create data-driven workflows (pipelines) comprised of activities to move and transform data. Pipelines contain a series of interconnected activities that perform data extraction, transformation, and loading. Data Factory connects to various data sources using linked services and can execute pipelines on a schedule or on-demand to move data between cloud and on-premises data stores and platforms.
Azure Data Factory is a data integration service that allows for data movement and transformation between both on-premises and cloud data stores. It uses datasets to represent data structures, activities to define actions on data with pipelines grouping related activities, and linked services to connect to external resources. Key concepts include datasets representing input/output data, activities performing actions like copy, and pipelines logically grouping activities.
This document provides an overview of Azure Data Factory (ADF), including why it is used, its key components and activities, how it works, and differences between versions 1 and 2. It describes the main steps in ADF as connect and collect, transform and enrich, publish, and monitor. The main components are pipelines, activities, datasets, and linked services. Activities include data movement, transformation, and control. Integration runtime and system variables are also summarized.
Azure Data Factory Mapping Data Flow allows users to stage and transform data in Azure during a limited preview period beginning in February 2019. Data can be staged from Azure Data Lake Storage, Blob Storage, or SQL databases/data warehouses, then transformed using visual data flows before being landed to staging areas in Azure like ADLS, Blob Storage, or SQL databases. For information, contact adfdataflowext@microsoft.com or visit http://aka.ms/dataflowpreview.
Azure Data Factory is a cloud-based data integration service that orchestrates and automates the movement and transformation of data. In this session we will learn how to create data integration solutions using the Data Factory service and ingest data from various data stores, transform/process the data, and publish the result data to the data stores.
The document discusses Azure Data Factory V2 data flows. It will provide an introduction to Azure Data Factory, discuss data flows, and have attendees build a simple data flow to demonstrate how they work. The speaker will introduce Azure Data Factory and data flows, explain concepts like pipelines, linked services, and data flows, and guide a hands-on demo where attendees build a data flow to join customer data to postal district data to add matching postal towns.
This document provides an overview of using Azure Data Factory (ADF) for ETL workflows. It discusses the components of modern data engineering, how to design ETL processes in Azure, an overview of ADF and its components. It also previews a demo on creating an ADF pipeline to copy data into Azure Synapse Analytics. The agenda includes discussions of data ingestion techniques in ADF, components of ADF like linked services, datasets, pipelines and triggers. It concludes with references, a Q&A section and a request for feedback.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Azure DataBricks for Data Engineering by Eugene PolonichkoDimko Zhluktenko
This document provides an overview of Azure Databricks, a Apache Spark-based analytics platform optimized for Microsoft Azure cloud services. It discusses key components of Azure Databricks including clusters, workspaces, notebooks, visualizations, jobs, alerts, and the Databricks File System. It also outlines how data engineers can leverage Azure Databricks for scenarios like running ETL pipelines, streaming analytics, and connecting business intelligence tools to query data.
Azure Data Factory | Moving On-Premise Data to Azure Cloud | Microsoft Azure ...Edureka!
** Microsoft Azure Certification Training : https://www.edureka.co/microsoft-azure-training **
This Edureka "Azure Data Factory” tutorial will give you a thorough and insightful overview of Microsoft Azure Data Factory and help you understand other related terms like Data Lakes and Data Warehousing.
Following are the offering of this tutorial:
1. Why Azure Data Factory?
2. What Is Azure Data Factory?
3. Data Factory Concepts
4. What is Azure Data Lake?
5. Data Lake Concepts
6. Data Lake Vs Data Warehouse
7. Demo- Moving On-Premise Data To Cloud
Check out our Playlists: https://goo.gl/A1CJjM
The document discusses Azure Data Factory v2. It provides an agenda that includes topics like triggers, control flow, and executing SSIS packages in ADFv2. It then introduces the speaker, Stefan Kirner, who has over 15 years of experience with Microsoft BI tools. The rest of the document consists of slides on ADFv2 topics like the pipeline model, triggers, activities, integration runtimes, scaling SSIS packages, and notes from the field on using SSIS packages in ADFv2.
Microsoft Azure Data Factory Hands-On Lab Overview SlidesMark Kromer
This document outlines modules for a lab on moving data to Azure using Azure Data Factory. The modules will deploy necessary Azure resources, lift and shift an existing SSIS package to Azure, rebuild ETL processes in ADF, enhance data with cloud services, transform and merge data with ADF and HDInsight, load data into a data warehouse with ADF, schedule ADF pipelines, monitor ADF, and verify loaded data. Technologies used include PowerShell, Azure SQL, Blob Storage, Data Factory, SQL DW, Logic Apps, HDInsight, and Office 365.
Azure Data Factory is a cloud-based data integration service that orchestrates and automates the movement and transformation of data. Key concepts in Azure Data Factory include pipelines, datasets, linked services, and activities. Pipelines contain activities that define actions on data. Datasets represent data structures. Linked services provide connection information. Activities include data movement and transformation. Azure Data Factory supports importing data from various sources and transforming data using technologies like HDInsight Hadoop clusters.
This document provides an overview of Azure Databricks, including:
- Azure Databricks is an Apache Spark-based analytics platform optimized for Microsoft Azure cloud services. It includes Spark SQL, streaming, machine learning libraries, and integrates fully with Azure services.
- Clusters in Azure Databricks provide a unified platform for various analytics use cases. The workspace stores notebooks, libraries, dashboards, and folders. Notebooks provide a code environment with visualizations. Jobs and alerts can run and notify on notebooks.
- The Databricks File System (DBFS) stores files in Azure Blob storage in a distributed file system accessible from notebooks. Business intelligence tools can connect to Databricks clusters via JDBC
This document discusses strategies for migrating applications to the Azure cloud platform. It covers choosing a porting model like moving web sites to web roles. Tips are provided like enabling full IIS, moving configuration out of web.config, and rewriting native code ISAPI filters. Stateful and stateless services running on worker roles or VM roles are also discussed. The document provides additional migration tips around logging, SQL, and monitoring applications in the cloud.
This document provides an introduction and overview of Azure Data Lake. It describes Azure Data Lake as a single store of all data ranging from raw to processed that can be used for reporting, analytics and machine learning. It discusses key Azure Data Lake components like Data Lake Store, Data Lake Analytics, HDInsight and the U-SQL language. It compares Data Lakes to data warehouses and explains how Azure Data Lake Store, Analytics and U-SQL process and transform data at scale.
The document discusses migrating a data warehouse to the Databricks Lakehouse Platform. It outlines why legacy data warehouses are struggling, how the Databricks Platform addresses these issues, and key considerations for modern analytics and data warehousing. The document then provides an overview of the migration methodology, approach, strategies, and key takeaways for moving to a lakehouse on Databricks.
Azure Databricks - An Introduction (by Kris Bock)Daniel Toomey
Azure Databricks is a fast, easy to use, and collaborative Apache Spark-based analytics platform optimized for Azure. It allows for interactive collaboration through a unified workspace, enables sharing of insights through integration with Power BI, and provides native integration with other Azure services. It also offers enterprise-grade security through integration with Azure Active Directory and compliance features.
Snowflake: Your Data. No Limits (Session sponsored by Snowflake) - AWS Summit...Amazon Web Services
Snowflake is a cloud-based data warehouse that is built for the cloud. It was founded in 2012 and has raised $1 billion in funding. Snowflake's architecture separates storage, compute, and metadata services, allowing it to offer unlimited scalability, multiple clusters that can access shared data with no downtime, and full transactional consistency across the system. Snowflake has over 2000 customers including large enterprises that use it for analytics, data science, and sharing large volumes of data securely.
Big Data and Data Warehousing Together with Azure Synapse Analytics (SQLBits ...Michael Rys
SQLBits 2020 presentation on how you can build solutions based on the modern data warehouse pattern with Azure Synapse Spark and SQL including demos of Azure Synapse.
Here are the slides for my talk "An intro to Azure Data Lake" at Techorama NL 2018. The session was held on Tuesday October 2nd from 15:00 - 16:00 in room 7.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Understanding Azure Data Factory: The What, When, and Why (NIC 2020)Cathrine Wilhelmsen
The document is a presentation on Azure Data Factory that discusses what it is, when and why it would be used, and how to work with it. It defines Azure Data Factory as a data integration service that can copy and transform data. It demonstrates how to use Azure Data Factory to copy data between cloud and on-premises data stores, transform data using mapping and wrangling data flows, and schedule data pipelines using triggers. Common data architectures that use Azure Data Factory are also presented.
The document discusses Azure Data Factory V2 data flows. It will provide an introduction to Azure Data Factory, discuss data flows, and have attendees build a simple data flow to demonstrate how they work. The speaker will introduce Azure Data Factory and data flows, explain concepts like pipelines, linked services, and data flows, and guide a hands-on demo where attendees build a data flow to join customer data to postal district data to add matching postal towns.
This document provides an overview of using Azure Data Factory (ADF) for ETL workflows. It discusses the components of modern data engineering, how to design ETL processes in Azure, an overview of ADF and its components. It also previews a demo on creating an ADF pipeline to copy data into Azure Synapse Analytics. The agenda includes discussions of data ingestion techniques in ADF, components of ADF like linked services, datasets, pipelines and triggers. It concludes with references, a Q&A section and a request for feedback.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Azure DataBricks for Data Engineering by Eugene PolonichkoDimko Zhluktenko
This document provides an overview of Azure Databricks, a Apache Spark-based analytics platform optimized for Microsoft Azure cloud services. It discusses key components of Azure Databricks including clusters, workspaces, notebooks, visualizations, jobs, alerts, and the Databricks File System. It also outlines how data engineers can leverage Azure Databricks for scenarios like running ETL pipelines, streaming analytics, and connecting business intelligence tools to query data.
Azure Data Factory | Moving On-Premise Data to Azure Cloud | Microsoft Azure ...Edureka!
** Microsoft Azure Certification Training : https://www.edureka.co/microsoft-azure-training **
This Edureka "Azure Data Factory” tutorial will give you a thorough and insightful overview of Microsoft Azure Data Factory and help you understand other related terms like Data Lakes and Data Warehousing.
Following are the offering of this tutorial:
1. Why Azure Data Factory?
2. What Is Azure Data Factory?
3. Data Factory Concepts
4. What is Azure Data Lake?
5. Data Lake Concepts
6. Data Lake Vs Data Warehouse
7. Demo- Moving On-Premise Data To Cloud
Check out our Playlists: https://goo.gl/A1CJjM
The document discusses Azure Data Factory v2. It provides an agenda that includes topics like triggers, control flow, and executing SSIS packages in ADFv2. It then introduces the speaker, Stefan Kirner, who has over 15 years of experience with Microsoft BI tools. The rest of the document consists of slides on ADFv2 topics like the pipeline model, triggers, activities, integration runtimes, scaling SSIS packages, and notes from the field on using SSIS packages in ADFv2.
Microsoft Azure Data Factory Hands-On Lab Overview SlidesMark Kromer
This document outlines modules for a lab on moving data to Azure using Azure Data Factory. The modules will deploy necessary Azure resources, lift and shift an existing SSIS package to Azure, rebuild ETL processes in ADF, enhance data with cloud services, transform and merge data with ADF and HDInsight, load data into a data warehouse with ADF, schedule ADF pipelines, monitor ADF, and verify loaded data. Technologies used include PowerShell, Azure SQL, Blob Storage, Data Factory, SQL DW, Logic Apps, HDInsight, and Office 365.
Azure Data Factory is a cloud-based data integration service that orchestrates and automates the movement and transformation of data. Key concepts in Azure Data Factory include pipelines, datasets, linked services, and activities. Pipelines contain activities that define actions on data. Datasets represent data structures. Linked services provide connection information. Activities include data movement and transformation. Azure Data Factory supports importing data from various sources and transforming data using technologies like HDInsight Hadoop clusters.
This document provides an overview of Azure Databricks, including:
- Azure Databricks is an Apache Spark-based analytics platform optimized for Microsoft Azure cloud services. It includes Spark SQL, streaming, machine learning libraries, and integrates fully with Azure services.
- Clusters in Azure Databricks provide a unified platform for various analytics use cases. The workspace stores notebooks, libraries, dashboards, and folders. Notebooks provide a code environment with visualizations. Jobs and alerts can run and notify on notebooks.
- The Databricks File System (DBFS) stores files in Azure Blob storage in a distributed file system accessible from notebooks. Business intelligence tools can connect to Databricks clusters via JDBC
This document discusses strategies for migrating applications to the Azure cloud platform. It covers choosing a porting model like moving web sites to web roles. Tips are provided like enabling full IIS, moving configuration out of web.config, and rewriting native code ISAPI filters. Stateful and stateless services running on worker roles or VM roles are also discussed. The document provides additional migration tips around logging, SQL, and monitoring applications in the cloud.
This document provides an introduction and overview of Azure Data Lake. It describes Azure Data Lake as a single store of all data ranging from raw to processed that can be used for reporting, analytics and machine learning. It discusses key Azure Data Lake components like Data Lake Store, Data Lake Analytics, HDInsight and the U-SQL language. It compares Data Lakes to data warehouses and explains how Azure Data Lake Store, Analytics and U-SQL process and transform data at scale.
The document discusses migrating a data warehouse to the Databricks Lakehouse Platform. It outlines why legacy data warehouses are struggling, how the Databricks Platform addresses these issues, and key considerations for modern analytics and data warehousing. The document then provides an overview of the migration methodology, approach, strategies, and key takeaways for moving to a lakehouse on Databricks.
Azure Databricks - An Introduction (by Kris Bock)Daniel Toomey
Azure Databricks is a fast, easy to use, and collaborative Apache Spark-based analytics platform optimized for Azure. It allows for interactive collaboration through a unified workspace, enables sharing of insights through integration with Power BI, and provides native integration with other Azure services. It also offers enterprise-grade security through integration with Azure Active Directory and compliance features.
Snowflake: Your Data. No Limits (Session sponsored by Snowflake) - AWS Summit...Amazon Web Services
Snowflake is a cloud-based data warehouse that is built for the cloud. It was founded in 2012 and has raised $1 billion in funding. Snowflake's architecture separates storage, compute, and metadata services, allowing it to offer unlimited scalability, multiple clusters that can access shared data with no downtime, and full transactional consistency across the system. Snowflake has over 2000 customers including large enterprises that use it for analytics, data science, and sharing large volumes of data securely.
Big Data and Data Warehousing Together with Azure Synapse Analytics (SQLBits ...Michael Rys
SQLBits 2020 presentation on how you can build solutions based on the modern data warehouse pattern with Azure Synapse Spark and SQL including demos of Azure Synapse.
Here are the slides for my talk "An intro to Azure Data Lake" at Techorama NL 2018. The session was held on Tuesday October 2nd from 15:00 - 16:00 in room 7.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Understanding Azure Data Factory: The What, When, and Why (NIC 2020)Cathrine Wilhelmsen
The document is a presentation on Azure Data Factory that discusses what it is, when and why it would be used, and how to work with it. It defines Azure Data Factory as a data integration service that can copy and transform data. It demonstrates how to use Azure Data Factory to copy data between cloud and on-premises data stores, transform data using mapping and wrangling data flows, and schedule data pipelines using triggers. Common data architectures that use Azure Data Factory are also presented.
Creating Visual Transformations in Azure Data Factory (dataMinds Connect)Cathrine Wilhelmsen
This document discusses visual data transformations in Azure Data Factory. It introduces Mapping Data Flows, which allow transforming data at scale visually without code. It explores the capabilities of Mapping Data Flows, the various transformations available, using expressions, debugging transformations, and handling schema drift. The document includes a demonstration of transforming sample data using Mapping Data Flows.
Pipelines and Packages: Introduction to Azure Data Factory (24HOP)Cathrine Wilhelmsen
This document summarizes a presentation on Azure Data Factory (ADF) given by Cathrine Wilhelmsen. The presentation provided an overview of ADF and how it compares to SQL Server Integration Services (SSIS). It demonstrated how to lift and shift existing SSIS packages to ADF and how to map data flows between different data stores using ADF data flows. The presentation concluded with lessons learned and encouraged attendees to ask any remaining questions.
Biml for Beginners: Script and Automate SSIS development (SQLSaturday Finland)Cathrine Wilhelmsen
The document discusses a presentation about using Biml, a markup language for Business Intelligence projects, to automate and script SSIS development. Biml allows generating SSIS packages from database metadata and reusing code to implement changes across multiple packages with just a few clicks. The presentation will cover basic Biml syntax and tools, demonstrate generating packages from Biml and converting existing SSIS packages to Biml, and discuss using BimlScript code blocks to import metadata and dynamically generate packages.
Pipelines and Packages: Introduction to Azure Data Factory (Techorama NL 2019)Cathrine Wilhelmsen
This document discusses Azure Data Factory (ADF) and how it can be used to build and orchestrate data pipelines without code. It describes how ADF is a hybrid data integration service that improves on its previous version. It also explains how existing SSIS packages can be "lifted and shifted" to ADF to modernize solutions while retaining investments. The document demonstrates creating pipelines and data flows in ADF, handling schema drift, and best practices for development.
"I can't keep up!" - Turning Discomfort into Personal Growth in a Fast-Paced ...Cathrine Wilhelmsen
"I can't keep up!" - Turning Discomfort into Personal Growth in a Fast-Paced World (Presented at SQLBits on March 17th, 2023)
Do you sometimes think the world is moving so fast that you're struggling to keep up?
Does it make you feel a little uncomfortable?
Awesome!
That means that you have ambitions. You want to learn new things, take that next step in your career, achieve your goals. You can do anything if you set your mind to it.
It just might not be easy.
All growth requires some discomfort. You need to manage and balance that discomfort, find a way to push yourself a little bit every day without feeling overwhelmed. In a fast-paced world, you need to know how to break down your goals into smaller chunks, how to prioritize, and how to optimize your learning.
Are you ready to turn your "I can't keep up" into "I can't believe I did all of that in just one year"?
AWS Analytics Services - When to use what? | AWS Summit Tel Aviv 2019Amazon Web Services
AWS provides a wide range of data analytics tools with the power to analyze vast volumes of customer, business, and transactional data quickly and at low cost.
In this session, we provide an overview of AWS analytics services and discuss how customers are using these services today. We will also discuss the new database and analytics services and features we launched in the last year.
AWS Analytics Services - When to use what? | AWS Summit Tel Aviv 2019AWS Summits
AWS provides a wide range of data analytics tools with the power to analyze vast volumes of customer, business, and transactional data quickly and at low cost.
In this session, we provide an overview of AWS analytics services and discuss how customers are using these services today. We will also discuss the new database and analytics services and features we launched in the last year.
Realize True Business Value With ThousandEyesThousandEyes
ThousandEyes monitoring provides visibility into SaaS environments to help businesses realize true value. With hybrid workforces and cloud adoption increasing, enterprises are struggling to understand user experience for SaaS apps using traditional monitoring. ThousandEyes removes blind spots in the digital supply chain and provides end-to-end visibility from network to cloud.
Exact, a Dutch accounting software company, migrated their Exact Online software to AWS to improve scalability, reduce costs, and ensure business continuity after Brexit. They conducted a proof of concept migration of Exact Online in Germany to AWS in winter 2018. This was followed by the full migration of Exact Online for the rest of the world to AWS in summer 2018. The migration involved replicating SQL Server databases between AWS and their previous hosting provider Rackspace for redundancy.
In this webinar you'll learn how to quickly and easily improve your business using Snowflake and Matillion ETL for Snowflake. Webinar presented by Solution Architects Craig Collier (Snowflake) adn Kalyan Arangam (Matillion).
In this webinar:
- Learn to optimize Snowflake and leverage Matillion ETL for Snowflake
- Discover tips and tricks to improve performance
- Get invaluable insights from data warehousing pros
Enabling the Future of Networks, Enterprises & Clouds - PTC 2014 - Steve Smit...Equinix
Steve Smith, CEO and president, delivered a keynote speech on growth projections for network and cloud services on Tuesday, January 21 at 9:30 AM HST.
The keynote, entitled “Mapping the Future of Networks and Clouds Inside Equinix,” utilized data based on connectivity patterns within the company’s set of 95+ global data centers to provide a unique perspective on the trends that map the future of network and cloud service providers.
Transforming to a digital business with cloud as the foundation supports a more agile, resilient and responsive business.
We introduce the fundamentals of an optimized AWS Cloud migration approach. And we’ll take a closer look at Vodafone, which by making the bold move to transition the majority of its legacy infrastructure to cloud, is pointing the way ahead for its industry.
Similar to Pipelines and Packages: Introduction to Azure Data Factory (DATA:Scotland 2019) (20)
Website Analytics in My Pocket using Microsoft Fabric (SQLBits 2024)Cathrine Wilhelmsen
The document is about how the author Cathrine Wilhelmsen built her own website analytics dashboard using Microsoft Fabric and Power BI. She collects data from the Cloudflare API and stores it in Microsoft Fabric. This allows her to visualize and access the analytics data on her phone through a mobile app beyond the 30 days retention offered by Cloudflare. In her presentation, she demonstrates how she retrieves the website data, processes it with Microsoft Fabric pipelines, and visualizes it in Power BI for a self-hosted analytics solution.
Data Integration with Data Factory (Microsoft Fabric Day Oslo 2023)Cathrine Wilhelmsen
Cathrine Wilhelmsen gave a presentation on using Microsoft Data Factory for data integration within Microsoft Fabric. Data Factory allows users to define data pipelines to ingest, transform and orchestrate data workflows. Pipelines contain activities that can copy or move data between different data stores. Connections specify how to connect to these data stores. Dataflows Gen2 provide enhanced orchestration capabilities, including defining activity dependencies and schedules. The presentation demonstrated how to use these capabilities in Data Factory for complex data integration scenarios.
The Battle of the Data Transformation Tools (PASS Data Community Summit 2023)Cathrine Wilhelmsen
The Battle of the Data Transformation Tools (Presented as part of the "Batte of the Data Transformation Tools" Learning Path at PASS Data Community Summit on November 16th, 2023)
Visually Transform Data in Azure Data Factory or Azure Synapse Analytics (PAS...Cathrine Wilhelmsen
Visually Transform Data in Azure Data Factory or Azure Synapse Analytics (Presented as part of the "Batte of the Data Transformation Tools" Learning Path at PASS Data Community Summit on November 15th, 2023)
Building an End-to-End Solution in Microsoft Fabric: From Dataverse to Power ...Cathrine Wilhelmsen
Building an End-to-End Solution in Microsoft Fabric: From Dataverse to Power BI (Presented at SQLSaturday Oregon & SW Washington on November 11th, 2023)
Website Analytics in my Pocket using Microsoft Fabric (AdaCon 2023)Cathrine Wilhelmsen
The document is about how the author created a mobile-friendly dashboard for her website analytics using Microsoft Fabric and Power BI. She collects data from the Cloudflare API and stores it in Microsoft Fabric. Then she visualizes the data in Power BI which can be viewed on her phone. This allows her to track website traffic and see which pages are most popular over time. She demonstrates her dashboard and discusses future improvements like comparing statistics across different time periods.
Stressed, Depressed, or Burned Out? The Warning Signs You Shouldn't Ignore (S...Cathrine Wilhelmsen
Stressed, Depressed, or Burned Out? The Warning Signs You Shouldn't Ignore (Presented at SQLBits on March 18th, 2023)
We all experience stress in our lives. When the stress is time-limited and manageable, it can be positive and productive. This kind of stress can help you get things done and lead to personal growth. However, when the stress stretches out over longer periods of time and we are unable to manage it, it can be negative and debilitating. This kind of stress can affect your mental health as well as your physical health, and increase the risk of depression and burnout.
The tricky part is that both depression and burnout can hit you hard without the warning signs you might recognize from stress. Where stress barges through your door and yells "hey, it's me!", depression and burnout can silently sneak in and gradually make adjustments until one day you turn around and see them smiling while realizing that you no longer recognize your house. I know, because I've dealt with both. And when I thought I had kicked them out, they both came back for new visits.
I don't have the Answers™️ or Solutions™️ to how to keep them away forever. But in hindsight, there were plenty of warning signs I missed, ignored, or was oblivious to at the time. In this deeply personal session, I will share my story of dealing with both depression and burnout. What were the warning signs? Why did I miss them? Could I have done something differently? And most importantly, what can I - and you - do to help ourselves or our loved ones if we notice that something is not quite right?
Lessons Learned: Implementing Azure Synapse Analytics in a Rapidly-Changing S...Cathrine Wilhelmsen
Lessons Learned: Implementing Azure Synapse Analytics in a Rapidly-Changing Startup (Presented at SQLBits on March 11th, 2022)
What happens when you mix one rapidly-changing startup, one data analyst, one data engineer, and one hypothesis that Azure Synapse Analytics could be the right tool of choice for gaining business insights?
We had no idea, but we gave it a go!
Our ambition was to think big, start small, and act fast – to deliver business value early and often.
Did we succeed?
Join us for an honest conversation about why we decided to implement Azure Synapse Analytics alongside Power BI, how we got started, which areas we completely messed up at first, what our current solution looks like, the lessons learned along the way, and the things we would have done differently if we could start all over again.
6 Tips for Building Confidence as a Public Speaker (SQLBits 2022)Cathrine Wilhelmsen
6 Tips for Building Confidence as a Public Speaker (Presented at SQLBits on March 10th, 2022)
Do you feel nervous about getting on stage to deliver a presentation?
That was me a few years ago. Palms sweating. Hands shaking. Voice trembling. I could barely breathe and talked at what felt like a thousand words per second. Now, public speaking is one of my favorite hobbies. Sometimes, I even plan my vacations around events! What changed?
There are no shortcuts to building confidence as a public speaker. However, there are many things you can do to make the journey a little easier for yourself. In this session, I share the top tips I have learned over the years. All it takes is a little preparation and practice.
You can do this!
202406 - Cape Town Snowflake User Group - LLM & RAG.pdfDouglas Day
Content from the July 2024 Cape Town Snowflake User Group focusing on Large Language Model (LLM) functions in Snowflake Cortex. Topics include:
Prompt Engineering.
Vector Data Types and Vector Functions.
Implementing a Retrieval
Augmented Generation (RAG) Solution within Snowflake
Dive into the details of how to leverage these advanced features without leaving the Snowflake environment.
Interview Methods - Marital and Family Therapy and Counselling - Psychology S...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!