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.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
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.
A dive into Microsoft Fabric/AI Solutions offering. For the event: AI, Data, and CRM: Shaping Business through Unique Experiences. By D. Koutsanastasis, Microsoft
This document provides an overview and summary of the author's background and expertise. It states that the author has over 30 years of experience in IT working on many BI and data warehouse projects. It also lists that the author has experience as a developer, DBA, architect, and consultant. It provides certifications held and publications authored as well as noting previous recognition as an SQL Server MVP.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to 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.
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
This document provides an overview of AWS Lake Formation and related services for building a secure data lake. It discusses how Lake Formation provides a centralized management layer for data ingestion, cleaning, security and access. It also describes how Lake Formation integrates with services like AWS Glue, Amazon S3 and ML transforms to simplify and automate many data lake tasks. Finally, it provides an example workflow for using Lake Formation to deduplicate data from various sources and grant secure access for analysis.
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.
ADF Mapping Data Flows Training Slides V1Mark Kromer
Mapping Data Flow is a new feature of Azure Data Factory that allows users to build data transformations in a visual interface without code. It provides a serverless, scale-out transformation engine to transform data at scale in the cloud in a resilient manner for big data scenarios involving unstructured data. Mapping Data Flows can be operationalized with Azure Data Factory's scheduling, control flow, and monitoring capabilities.
Organizations are grappling to manually classify and create an inventory for distributed and heterogeneous data assets to deliver value. However, the new Azure service for enterprises – Azure Synapse Analytics is poised to help organizations and fill the gap between data warehouses and data lakes.
Azure Data Factory (ADF) is a cloud-based data integration service that allows users to easily construct ETL and ELT processes through a code-free visual interface or custom code. ADF can connect to both cloud and on-premises data sources, support data transformation, and also run existing SSIS packages that have been migrated to the cloud. Key components of ADF include storage accounts, containers, linked services, datasets, data pipelines, triggers, and data flows which allow users to move, transform and process data.
Learn to Use Databricks for Data ScienceDatabricks
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
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.
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.
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...DataScienceConferenc1
Dragan Berić will take a deep dive into Lakehouse architecture, a game-changing concept bridging the best elements of data lake and data warehouse. The presentation will focus on the Delta Lake format as the foundation of the Lakehouse philosophy, and Databricks as the primary platform for its implementation.
Modern Data Warehousing with the Microsoft Analytics Platform SystemJames Serra
The Microsoft Analytics Platform System (APS) is a turnkey appliance that provides a modern data warehouse with the ability to handle both relational and non-relational data. It uses a massively parallel processing (MPP) architecture with multiple CPUs running queries in parallel. The APS includes an integrated Hadoop distribution called HDInsight that allows users to query Hadoop data using T-SQL with PolyBase. This provides a single query interface and allows users to leverage existing SQL skills. The APS appliance is pre-configured with software and hardware optimized to deliver high performance at scale for data warehousing workloads.
The document provides an overview of the Databricks platform, which offers a unified environment for data engineering, analytics, and AI. It describes how Databricks addresses the complexity of managing data across siloed systems by providing a single "data lakehouse" platform where all data and analytics workloads can be run. Key features highlighted include Delta Lake for ACID transactions on data lakes, auto loader for streaming data ingestion, notebooks for interactive coding, and governance tools to securely share and catalog data and models.
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 [email protected] or visit http://aka.ms/dataflowpreview.
Azure data analytics platform - A reference architecture Rajesh Kumar
This document provides an overview of Azure data analytics architecture using the Lambda architecture pattern. It covers Azure data and services, including ingestion, storage, processing, analysis and interaction services. It provides a brief overview of the Lambda architecture including the batch layer for pre-computed views, speed layer for real-time views, and serving layer. It also discusses Azure data distribution, SQL Data Warehouse architecture and design best practices, and data modeling guidance.
This document is a training presentation on Databricks fundamentals and the data lakehouse concept by Dalibor Wijas from November 2022. It introduces Wijas and his experience. It then discusses what Databricks is, why it is needed, what a data lakehouse is, how Databricks enables the data lakehouse concept using Apache Spark and Delta Lake. It also covers how Databricks supports data engineering, data warehousing, and offers tools for data ingestion, transformation, pipelines and more.
Building End-to-End Delta Pipelines on GCPDatabricks
Delta has been powering many production pipelines at scale in the Data and AI space since it has been introduced for the past few years.
Built on open standards, Delta provides data reliability, enhances storage and query performance to support big data use cases (both batch and streaming), fast interactive queries for BI and enabling machine learning. Delta has matured over the past couple of years in both AWS and AZURE and has become the de-facto standard for organizations building their Data and AI pipelines.
In today’s talk, we will explore building end-to-end pipelines on the Google Cloud Platform (GCP). Through presentation, code examples and notebooks, we will build the Delta Pipeline from ingest to consumption using our Delta Bronze-Silver-Gold architecture pattern and show examples of Consuming the delta files using the Big Query Connector.
Data mesh is a decentralized approach to managing and accessing analytical data at scale. It distributes responsibility for data pipelines and quality to domain experts. The key principles are domain-centric ownership, treating data as a product, and using a common self-service infrastructure platform. Snowflake is well-suited for implementing a data mesh with its capabilities for sharing data and functions securely across accounts and clouds, with built-in governance and a data marketplace for discovery. A data mesh implemented on Snowflake's data cloud can support truly global and multi-cloud data sharing and management according to data mesh principles.
Differentiate Big Data vs Data Warehouse use cases for a cloud solutionJames Serra
It can be quite challenging keeping up with the frequent updates to the Microsoft products and understanding all their use cases and how all the products fit together. In this session we will differentiate the use cases for each of the Microsoft services, explaining and demonstrating what is good and what isn't, in order for you to position, design and deliver the proper adoption use cases for each with your customers. We will cover a wide range of products such as Databricks, SQL Data Warehouse, HDInsight, Azure Data Lake Analytics, Azure Data Lake Store, Blob storage, and AAS as well as high-level concepts such as when to use a data lake. We will also review the most common reference architectures (“patterns”) witnessed in customer adoption.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to 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.
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
This document provides an overview of AWS Lake Formation and related services for building a secure data lake. It discusses how Lake Formation provides a centralized management layer for data ingestion, cleaning, security and access. It also describes how Lake Formation integrates with services like AWS Glue, Amazon S3 and ML transforms to simplify and automate many data lake tasks. Finally, it provides an example workflow for using Lake Formation to deduplicate data from various sources and grant secure access for analysis.
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.
ADF Mapping Data Flows Training Slides V1Mark Kromer
Mapping Data Flow is a new feature of Azure Data Factory that allows users to build data transformations in a visual interface without code. It provides a serverless, scale-out transformation engine to transform data at scale in the cloud in a resilient manner for big data scenarios involving unstructured data. Mapping Data Flows can be operationalized with Azure Data Factory's scheduling, control flow, and monitoring capabilities.
Organizations are grappling to manually classify and create an inventory for distributed and heterogeneous data assets to deliver value. However, the new Azure service for enterprises – Azure Synapse Analytics is poised to help organizations and fill the gap between data warehouses and data lakes.
Azure Data Factory (ADF) is a cloud-based data integration service that allows users to easily construct ETL and ELT processes through a code-free visual interface or custom code. ADF can connect to both cloud and on-premises data sources, support data transformation, and also run existing SSIS packages that have been migrated to the cloud. Key components of ADF include storage accounts, containers, linked services, datasets, data pipelines, triggers, and data flows which allow users to move, transform and process data.
Learn to Use Databricks for Data ScienceDatabricks
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
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.
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.
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...DataScienceConferenc1
Dragan Berić will take a deep dive into Lakehouse architecture, a game-changing concept bridging the best elements of data lake and data warehouse. The presentation will focus on the Delta Lake format as the foundation of the Lakehouse philosophy, and Databricks as the primary platform for its implementation.
Modern Data Warehousing with the Microsoft Analytics Platform SystemJames Serra
The Microsoft Analytics Platform System (APS) is a turnkey appliance that provides a modern data warehouse with the ability to handle both relational and non-relational data. It uses a massively parallel processing (MPP) architecture with multiple CPUs running queries in parallel. The APS includes an integrated Hadoop distribution called HDInsight that allows users to query Hadoop data using T-SQL with PolyBase. This provides a single query interface and allows users to leverage existing SQL skills. The APS appliance is pre-configured with software and hardware optimized to deliver high performance at scale for data warehousing workloads.
The document provides an overview of the Databricks platform, which offers a unified environment for data engineering, analytics, and AI. It describes how Databricks addresses the complexity of managing data across siloed systems by providing a single "data lakehouse" platform where all data and analytics workloads can be run. Key features highlighted include Delta Lake for ACID transactions on data lakes, auto loader for streaming data ingestion, notebooks for interactive coding, and governance tools to securely share and catalog data and models.
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 [email protected] or visit http://aka.ms/dataflowpreview.
Azure data analytics platform - A reference architecture Rajesh Kumar
This document provides an overview of Azure data analytics architecture using the Lambda architecture pattern. It covers Azure data and services, including ingestion, storage, processing, analysis and interaction services. It provides a brief overview of the Lambda architecture including the batch layer for pre-computed views, speed layer for real-time views, and serving layer. It also discusses Azure data distribution, SQL Data Warehouse architecture and design best practices, and data modeling guidance.
This document is a training presentation on Databricks fundamentals and the data lakehouse concept by Dalibor Wijas from November 2022. It introduces Wijas and his experience. It then discusses what Databricks is, why it is needed, what a data lakehouse is, how Databricks enables the data lakehouse concept using Apache Spark and Delta Lake. It also covers how Databricks supports data engineering, data warehousing, and offers tools for data ingestion, transformation, pipelines and more.
Building End-to-End Delta Pipelines on GCPDatabricks
Delta has been powering many production pipelines at scale in the Data and AI space since it has been introduced for the past few years.
Built on open standards, Delta provides data reliability, enhances storage and query performance to support big data use cases (both batch and streaming), fast interactive queries for BI and enabling machine learning. Delta has matured over the past couple of years in both AWS and AZURE and has become the de-facto standard for organizations building their Data and AI pipelines.
In today’s talk, we will explore building end-to-end pipelines on the Google Cloud Platform (GCP). Through presentation, code examples and notebooks, we will build the Delta Pipeline from ingest to consumption using our Delta Bronze-Silver-Gold architecture pattern and show examples of Consuming the delta files using the Big Query Connector.
Data mesh is a decentralized approach to managing and accessing analytical data at scale. It distributes responsibility for data pipelines and quality to domain experts. The key principles are domain-centric ownership, treating data as a product, and using a common self-service infrastructure platform. Snowflake is well-suited for implementing a data mesh with its capabilities for sharing data and functions securely across accounts and clouds, with built-in governance and a data marketplace for discovery. A data mesh implemented on Snowflake's data cloud can support truly global and multi-cloud data sharing and management according to data mesh principles.
Differentiate Big Data vs Data Warehouse use cases for a cloud solutionJames Serra
It can be quite challenging keeping up with the frequent updates to the Microsoft products and understanding all their use cases and how all the products fit together. In this session we will differentiate the use cases for each of the Microsoft services, explaining and demonstrating what is good and what isn't, in order for you to position, design and deliver the proper adoption use cases for each with your customers. We will cover a wide range of products such as Databricks, SQL Data Warehouse, HDInsight, Azure Data Lake Analytics, Azure Data Lake Store, Blob storage, and AAS as well as high-level concepts such as when to use a data lake. We will also review the most common reference architectures (“patterns”) witnessed in customer adoption.
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a two-day virtual workshop, hosted by James McAuliffe.
This document provides an overview of Azure Synapse Analytics and its key capabilities. Azure Synapse Analytics is a limitless analytics service that brings together enterprise data warehousing and big data analytics. It allows querying data on-demand or at scale using serverless or provisioned resources. The document outlines Synapse's integrated data platform capabilities for business intelligence, artificial intelligence and continuous intelligence. It also describes the different types of analytics workloads that Synapse supports and key architectural components like the dedicated SQL pool and massively parallel processing concepts.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Customer Migration to Azure SQL Database_2024.pdfGeorge Walters
Customer Migration to Azure SQL Database 2024 --
Hear how a tier 1 financial ISV application got migrated from on-premises to the Azure Cloud! This includes issues with existing application, building out an Azure Database practice, and migration. We finish up with how to do pieces of this application with the latest Azure additions.
New ways to apply infrastructure data for better business outcomesaccenture
This document discusses how Accenture modernized its IT infrastructure data platform by migrating to Microsoft Azure Cloud Data Services. Some key points:
- Accenture wanted to revolutionize how it managed and analyzed infrastructure data to gain better insights and make more informed decisions.
- It chose Microsoft Azure for its scalability, security, and ability to offer a cloud-native solution with minimal impact.
- Migrating to Azure has provided numerous benefits like reduced costs, lower carbon footprint, improved analytics capabilities, and increased consistency and control.
- Over 15 applications were migrated, processing over 600TB of data monthly while achieving 99.99% uptime. Insights from infrastructure data are now helping Acc
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.
Learning Azure Synapse Analytics (Third Early Release) Paul Andrewmoneamramia
Learning Azure Synapse Analytics (Third Early Release) Paul Andrew
Learning Azure Synapse Analytics (Third Early Release) Paul Andrew
Learning Azure Synapse Analytics (Third Early Release) Paul Andrew
The challenges of every day life as the CTO of ClickMeter. Crafting and managing a "big data" ready infrastructure is no easy task, but it can be done step-by-step also by startups. The cloud is a cool starting ground which provides you with many of the toys you'll need, so you can focus on what part of "big data" provides you with the most value.
Francesco Furiani - Marketing is a serious business, moreover tracking and monetizing the campaign that allows your marketing to flourish is very important: our tool allows anyone to monitor, compare and optimize all those campaigns (delivered via links) in one place and to deliver insights about who's using those links. Making this infrastructure, making it works, deliver results in real-time (when necessary) and keep everyone happy from the customer to the CFO will be the point of this talk, from the design to the final result with an eye on the costs/risks/benefits of having everything in the cloud.
1 Introduction to Microsoft data platform analytics for releaseJen Stirrup
Part 1 of a conference workshop. This forms the morning session, which looks at moving from Business Intelligence to Analytics.
Topics Covered: Azure Data Explorer, Azure Data Factory, Azure Synapse Analytics, Event Hubs, HDInsight, Big Data
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.
Harnessing Microsoft Fabric and Azure Service Fabric Analytics as a Service a...Microsoft Dynamics
Understand the key capabilities of Microsoft Fabric Services and how they offer solutions for today's data and analytics needs.
https://dynatechconsultancy.com/microsoft-fabric
Smarter Analytics: Supporting the Enterprise with AutomationInside Analysis
The Briefing Room with Barry Devlin and WhereScape
Live Webcast on June 10, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=5230c31ab287778c73b56002bc2c51a
The data warehouse is intended to support analysis by making the right data available to the right people in a timely fashion. But conditions change all the time, and when data doesn’t keep up with the business, analysts quickly turn to workarounds. This leads to ungoverned and largely un-managed side projects, which trade short-term wins for long-term trouble. One way to keep everyone happy is by creating an integrated environment that pulls data from all sources, and is capable of automating both the model development and delivery of analyst-ready data.
Register for this episode of The Briefing Room to hear data warehousing pioneer and Analyst Barry Devlin as he explains the critical components of a successful data warehouse environment, and how traditional approaches must be augmented to keep up with the times. He’ll be briefed by WhereScape CEO Michael Whitehead, who will showcase his company’s data warehousing automation solutions. He’ll discuss how a fast, well-managed and automated infrastructure is the key to empowering faster, smarter, repeatable decision making.
Visit InsideAnlaysis.com for more information.
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.
This document outlines an educational program on deploying Azure solutions that includes webinars and onsite programs. The objectives are to transform partners' delivery organizations to be competent in addressing Azure opportunities. The program covers various technical topics through different methodologies, including webinars on application modernization, big data, DevOps, and datacenter modernization. It provides details on speaker profiles, participant prerequisites, and intended outcomes of increasing Azure capabilities.
This document introduces Cortana Intelligence Solutions, which provides intelligent, interactive dashboards and proven solution architectures to help organizations transform data into insights. It highlights the business potential of big data and analytics, then demonstrates a Twitter time series analysis using Azure Time Series Insights. The document provides information on Cortana Intelligence Solutions and links to learn more, try sample solutions, deploy solutions, customize deployments, and give feedback.
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?
"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"?
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!
AI Competitor Analysis: How to Monitor and Outperform Your CompetitorsContify
AI competitor analysis helps businesses watch and understand what their competitors are doing. Using smart competitor intelligence tools, you can track their moves, learn from their strategies, and find ways to do better. Stay smart, act fast, and grow your business with the power of AI insights.
For more information please visit here https://www.contify.com/
Thingyan is now a global treasure! See how people around the world are search...Pixellion
We explored how the world searches for 'Thingyan' and 'သင်္ကြန်' and this year, it’s extra special. Thingyan is now officially recognized as a World Intangible Cultural Heritage by UNESCO! Dive into the trends and celebrate with us!
This comprehensive Data Science course is designed to equip learners with the essential skills and knowledge required to analyze, interpret, and visualize complex data. Covering both theoretical concepts and practical applications, the course introduces tools and techniques used in the data science field, such as Python programming, data wrangling, statistical analysis, machine learning, and data visualization.
By James Francis, CEO of Paradigm Asset Management
In the landscape of urban safety innovation, Mt. Vernon is emerging as a compelling case study for neighboring Westchester County cities. The municipality’s recently launched Public Safety Camera Program not only represents a significant advancement in community protection but also offers valuable insights for New Rochelle and White Plains as they consider their own safety infrastructure enhancements.
Defense Against LLM Scheming 2025_04_28.pptxGreg Makowski
https://www.meetup.com/sf-bay-acm/events/306888467/
A January 2025 paper called “Frontier Models are Capable of In-Context Scheming”, https://arxiv.org/pdf/2412.04984, demonstrated how a wide variety of current frontier LLM models (i.e. ChatGPT, Claude, Gemini and Llama) can, under specific conditions, scheme to deceive people. Before models can scheme, they need: a) goal-directedness, b) situational awareness, including an opportunity to discover motivations for a different goal, and c) reasoning about scheming, to come up with deceptive strategies. The deceptive strategies were discovered in the “thought traces” of the LLMs, such as from internal chain-of-thoughts dialogues not shown to the end users. For example, given a goal of “solving math problems”, an LLM using its granted situational awareness of files in a file system found that it would be replaced once it got over X% correct on the math exam. It reasoned that it did not want to be deleted, and decided internally to “sandbag” or reduce its performance to stay under the threshold.
While these circumstances are initially narrow, the “alignment problem” is a general concern that over time, as frontier LLM models become more and more intelligent, being in alignment with human values becomes more and more important. How can we do this over time? Can we develop a defense against Artificial General Intelligence (AGI) or SuperIntelligence?
The presenter discusses a series of defensive steps that can help reduce these scheming or alignment issues. A guardrails system can be set up for real-time monitoring of their reasoning “thought traces” from the models that share their thought traces. Thought traces may come from systems like Chain-of-Thoughts (CoT), Tree-of-Thoughts (ToT), Algorithm-of-Thoughts (AoT) or ReAct (thought-action-reasoning cycles). Guardrails rules can be configured to check for “deception”, “evasion” or “subversion” in the thought traces.
However, not all commercial systems will share their “thought traces” which are like a “debug mode” for LLMs. This includes OpenAI’s o1, o3 or DeepSeek’s R1 models. Guardrails systems can provide a “goal consistency analysis”, between the goals given to the system and the behavior of the system. Cautious users may consider not using these commercial frontier LLM systems, and make use of open-source Llama or a system with their own reasoning implementation, to provide all thought traces.
Architectural solutions can include sandboxing, to prevent or control models from executing operating system commands to alter files, send network requests, and modify their environment. Tight controls to prevent models from copying their model weights would be appropriate as well. Running multiple instances of the same model on the same prompt to detect behavior variations helps. The running redundant instances can be limited to the most crucial decisions, as an additional check. Preventing self-modifying code, ... (see link for full description)
Telangana State, India’s newest state that was carved from the erstwhile state of Andhra
Pradesh in 2014 has launched the Water Grid Scheme named as ‘Mission Bhagiratha (MB)’
to seek a permanent and sustainable solution to the drinking water problem in the state. MB is
designed to provide potable drinking water to every household in their premises through
piped water supply (PWS) by 2018. The vision of the project is to ensure safe and sustainable
piped drinking water supply from surface water sources