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.
Analyst field reports on top 15 MDM solutions - Aaron Zornes (NYC 2021)Aaron Zornes
The document provides field reports on various Master Data Management (MDM) solutions. It lists the top evaluation criteria for MDM solutions and the "Top 15" MDM vendors. For several vendors, it summarizes the strengths and caveats of their solutions based on recent versions. It also lists notable customer references for some vendors.
BI: new of the buzz words that everyone is talking about but what is it? How can it be used to make a impact in my organization? How do I get started? In this session, we will talk about it and show you a live example in Office 365's SharePoint Online.
Objectives/Outcomes: In this session, participants will learn:
1. What is BI
2. What is Microsoft's Power BI
3. Case Studies
4. How can I get it
At wetter.com we build analytical B2B data products and heavily use Spark and AWS technologies for data processing and analytics. I explain why we moved from AWS EMR to Databricks and Delta and share our experiences from different angles like architecture, application logic and user experience. We will look how security, cluster configuration, resource consumption and workflow changed by using Databricks clusters as well as how using Delta tables simplified our application logic and data operations.
The document discusses six key questions organizations should ask about data governance: 1) Do we have a government structure in place to oversee data governance? 2) How can we assess our current data governance situation? 3) What is our data governance strategy? 4) What is the value of our data? 5) What are our data vulnerabilities? 6) How can we measure progress in data governance? It provides details on each question, highlighting the importance of leadership, benchmarks, strategic planning, risk assessment, and metrics in developing an effective data governance program.
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.
An introduction to IBM Data Lake by Mandy Chessell CBE FREng CEng FBCS, Distinguished Engineer & Master Inventor.
Learn more about IBM Data Lake: https://ibm.biz/Bdswi9
Big data requires service that can orchestrate and operationalize processes to refine the enormous stores of raw data into actionable business insights. Azure Data Factory is a managed cloud service that's built for these complex hybrid extract-transform-load (ETL), extract-load-transform (ELT), and data integration projects.
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This document provides an overview of building a modern cloud analytics solution using Microsoft Azure. It discusses the role of analytics, a history of cloud computing, and a data warehouse modernization project. Key challenges covered include lack of notifications, logging, self-service BI, and integrating streaming data. The document proposes solutions to these challenges using Azure services like Data Factory, Kafka, Databricks, and SQL Data Warehouse. It also discusses alternative implementations using tools like Matillion ETL and Snowflake.
This document summarizes a research study that assessed the data management practices of 175 organizations between 2000-2006. The study had both descriptive and self-improvement goals, such as understanding the range of practices and determining areas for improvement. Researchers used a structured interview process to evaluate organizations across six data management processes based on a 5-level maturity model. The results provided insights into an organization's practices and a roadmap for enhancing data management.
Oracle SOA Suite Overview - Integration in a Service-Oriented WorldOracleContractors
The document discusses Oracle SOA Suite, which provides integration capabilities in a service-oriented world. It outlines key SOA standards and components of Oracle's integration and SOA platform, including adapters, the enterprise service bus, and BPEL. It also summarizes a sample SOA credit request demo that uses the ESB, BPEL, rules, and BAM.
1. Enterprise Data Management (EDM) is the ability of an organization to precisely define, easily integrate and effectively retrieve data for both internal applications and external communication. It involves managing various types of data across the enterprise.
2. EDM includes areas like master data management, reference data management, metadata management, data governance, data quality, data analytics, data privacy, data integration, and data architecture.
3. The document discusses definitions and concepts for each of these areas, including roles, processes, and technologies involved. It provides overviews of fundamental concepts, principles, dimensions and processes for data quality, data governance, data privacy and other areas.
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
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.
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.
This document provides an overview and instructions for attending a "Dashboard in a Day" workshop on Microsoft Power BI. It includes prerequisites for the workshop, such as having a computer that meets minimum system requirements and having a Power BI account. The agenda outlines the topics to be covered, including introductions, building reports and dashboards in Power BI Desktop and the Power BI service, and opportunities for questions. Resources are provided on connecting to the workshop wireless network and engaging with the broader Power BI community.
DataOps for the Modern Data Warehouse on Microsoft Azure @ NDCOslo 2020 - Lac...Lace Lofranco
Talk Description:
The Modern Data Warehouse architecture is a response to the emergence of Big Data, Machine Learning and Advanced Analytics. DevOps is a key aspect of successfully operationalising a multi-source Modern Data Warehouse.
While there are many examples of how to build CI/CD pipelines for traditional applications, applying these concepts to Big Data Analytical Pipelines is a relatively new and emerging area. In this demo heavy session, we will see how to apply DevOps principles to an end-to-end Data Pipeline built on the Microsoft Azure Data Platform with technologies such as Data Factory, Databricks, Data Lake Gen2, Azure Synapse, and AzureDevOps.
Resources: https://aka.ms/mdw-dataops
As part of this session, I will be giving an introduction to Data Engineering and Big Data. It covers up to date trends.
* Introduction to Data Engineering
* Role of Big Data in Data Engineering
* Key Skills related to Data Engineering
* Role of Big Data in Data Engineering
* Overview of Data Engineering Certifications
* Free Content and ITVersity Paid Resources
Don't worry if you miss the video - you can click on the below link to go through the video after the schedule.
https://youtu.be/dj565kgP1Ss
* Upcoming Live Session - Overview of Big Data Certifications (Spark Based) - https://www.meetup.com/itversityin/events/271739702/
Relevant Playlists:
* Apache Spark using Python for Certifications - https://www.youtube.com/playlist?list=PLf0swTFhTI8rMmW7GZv1-z4iu_-TAv3bi
* Free Data Engineering Bootcamp - https://www.youtube.com/playlist?list=PLf0swTFhTI8pBe2Vr2neQV7shh9Rus8rl
* Join our Meetup group - https://www.meetup.com/itversityin/
* Enroll for our labs - https://labs.itversity.com/plans
* Subscribe to our YouTube Channel for Videos - http://youtube.com/itversityin/?sub_confirmation=1
* Access Content via our GitHub - https://github.com/dgadiraju/itversity-books
* Lab and Content Support using Slack
Snowflake + Power BI: Cloud Analytics for EveryoneAngel Abundez
This document discusses architectures for using Snowflake and Power BI together. It begins by describing the benefits of each technology. It then outlines several architectural scenarios for connecting Snowflake to Power BI, including using a Power BI gateway, without a gateway, and connecting to Analysis Services. The document also provides examples of usage scenarios and developer best practices. It concludes with a section on data governance considerations for architectures with and without a Power BI gateway.
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.
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
Power BI is a collection of services from Microsoft used for modeling, analyzing, and visualizing data. It involves data modeling by organizing and preparing data, data visualization through interactive reports and visuals to develop business insights, and a workflow that includes connecting data sources, loading data into a data model, and building visualizations. The Power BI desktop application is used to create data models and reports which can then be saved locally.
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.
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 Factory ETL Patterns in the CloudMark Kromer
This document discusses ETL patterns in the cloud using Azure Data Factory. It covers topics like ETL vs ELT, the importance of scale and flexible schemas in cloud ETL, and how Azure Data Factory supports workflows, templates, and integration with on-premises and cloud data. It also provides examples of nightly ETL data flows, handling schema drift, loading dimensional models, and data science scenarios using Azure data services.
Data platform modernization with Databricks.pptxCalvinSim10
The document discusses modernizing a healthcare organization's data platform from version 1.0 to 2.0 using Azure Databricks. Version 1.0 used Azure HDInsight (HDI) which was challenging to scale and maintain. It presented performance issues and lacked integrations. Version 2.0 with Databricks will provide improved scalability, cost optimization, governance, and ease of use through features like Delta Lake, Unity Catalog, and collaborative notebooks. This will help address challenges faced by consumers, data engineers, and the client.
Azure Days 2019: Business Intelligence auf Azure (Marco Amhof & Yves Mauron)Trivadis
In dieser Session stellen wir ein Projekt vor, in welchem wir ein umfassendes BI-System mit Hilfe von Azure Blob Storage, Azure SQL, Azure Logic Apps und Azure Analysis Services für und in der Azure Cloud aufgebaut haben. Wir berichten über die Herausforderungen, wie wir diese gelöst haben und welche Learnings und Best Practices wir mitgenommen haben.
Big data requires service that can orchestrate and operationalize processes to refine the enormous stores of raw data into actionable business insights. Azure Data Factory is a managed cloud service that's built for these complex hybrid extract-transform-load (ETL), extract-load-transform (ELT), and data integration projects.
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This document provides an overview of building a modern cloud analytics solution using Microsoft Azure. It discusses the role of analytics, a history of cloud computing, and a data warehouse modernization project. Key challenges covered include lack of notifications, logging, self-service BI, and integrating streaming data. The document proposes solutions to these challenges using Azure services like Data Factory, Kafka, Databricks, and SQL Data Warehouse. It also discusses alternative implementations using tools like Matillion ETL and Snowflake.
This document summarizes a research study that assessed the data management practices of 175 organizations between 2000-2006. The study had both descriptive and self-improvement goals, such as understanding the range of practices and determining areas for improvement. Researchers used a structured interview process to evaluate organizations across six data management processes based on a 5-level maturity model. The results provided insights into an organization's practices and a roadmap for enhancing data management.
Oracle SOA Suite Overview - Integration in a Service-Oriented WorldOracleContractors
The document discusses Oracle SOA Suite, which provides integration capabilities in a service-oriented world. It outlines key SOA standards and components of Oracle's integration and SOA platform, including adapters, the enterprise service bus, and BPEL. It also summarizes a sample SOA credit request demo that uses the ESB, BPEL, rules, and BAM.
1. Enterprise Data Management (EDM) is the ability of an organization to precisely define, easily integrate and effectively retrieve data for both internal applications and external communication. It involves managing various types of data across the enterprise.
2. EDM includes areas like master data management, reference data management, metadata management, data governance, data quality, data analytics, data privacy, data integration, and data architecture.
3. The document discusses definitions and concepts for each of these areas, including roles, processes, and technologies involved. It provides overviews of fundamental concepts, principles, dimensions and processes for data quality, data governance, data privacy and other areas.
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
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.
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.
This document provides an overview and instructions for attending a "Dashboard in a Day" workshop on Microsoft Power BI. It includes prerequisites for the workshop, such as having a computer that meets minimum system requirements and having a Power BI account. The agenda outlines the topics to be covered, including introductions, building reports and dashboards in Power BI Desktop and the Power BI service, and opportunities for questions. Resources are provided on connecting to the workshop wireless network and engaging with the broader Power BI community.
DataOps for the Modern Data Warehouse on Microsoft Azure @ NDCOslo 2020 - Lac...Lace Lofranco
Talk Description:
The Modern Data Warehouse architecture is a response to the emergence of Big Data, Machine Learning and Advanced Analytics. DevOps is a key aspect of successfully operationalising a multi-source Modern Data Warehouse.
While there are many examples of how to build CI/CD pipelines for traditional applications, applying these concepts to Big Data Analytical Pipelines is a relatively new and emerging area. In this demo heavy session, we will see how to apply DevOps principles to an end-to-end Data Pipeline built on the Microsoft Azure Data Platform with technologies such as Data Factory, Databricks, Data Lake Gen2, Azure Synapse, and AzureDevOps.
Resources: https://aka.ms/mdw-dataops
As part of this session, I will be giving an introduction to Data Engineering and Big Data. It covers up to date trends.
* Introduction to Data Engineering
* Role of Big Data in Data Engineering
* Key Skills related to Data Engineering
* Role of Big Data in Data Engineering
* Overview of Data Engineering Certifications
* Free Content and ITVersity Paid Resources
Don't worry if you miss the video - you can click on the below link to go through the video after the schedule.
https://youtu.be/dj565kgP1Ss
* Upcoming Live Session - Overview of Big Data Certifications (Spark Based) - https://www.meetup.com/itversityin/events/271739702/
Relevant Playlists:
* Apache Spark using Python for Certifications - https://www.youtube.com/playlist?list=PLf0swTFhTI8rMmW7GZv1-z4iu_-TAv3bi
* Free Data Engineering Bootcamp - https://www.youtube.com/playlist?list=PLf0swTFhTI8pBe2Vr2neQV7shh9Rus8rl
* Join our Meetup group - https://www.meetup.com/itversityin/
* Enroll for our labs - https://labs.itversity.com/plans
* Subscribe to our YouTube Channel for Videos - http://youtube.com/itversityin/?sub_confirmation=1
* Access Content via our GitHub - https://github.com/dgadiraju/itversity-books
* Lab and Content Support using Slack
Snowflake + Power BI: Cloud Analytics for EveryoneAngel Abundez
This document discusses architectures for using Snowflake and Power BI together. It begins by describing the benefits of each technology. It then outlines several architectural scenarios for connecting Snowflake to Power BI, including using a Power BI gateway, without a gateway, and connecting to Analysis Services. The document also provides examples of usage scenarios and developer best practices. It concludes with a section on data governance considerations for architectures with and without a Power BI gateway.
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.
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
Power BI is a collection of services from Microsoft used for modeling, analyzing, and visualizing data. It involves data modeling by organizing and preparing data, data visualization through interactive reports and visuals to develop business insights, and a workflow that includes connecting data sources, loading data into a data model, and building visualizations. The Power BI desktop application is used to create data models and reports which can then be saved locally.
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.
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 Factory ETL Patterns in the CloudMark Kromer
This document discusses ETL patterns in the cloud using Azure Data Factory. It covers topics like ETL vs ELT, the importance of scale and flexible schemas in cloud ETL, and how Azure Data Factory supports workflows, templates, and integration with on-premises and cloud data. It also provides examples of nightly ETL data flows, handling schema drift, loading dimensional models, and data science scenarios using Azure data services.
Data platform modernization with Databricks.pptxCalvinSim10
The document discusses modernizing a healthcare organization's data platform from version 1.0 to 2.0 using Azure Databricks. Version 1.0 used Azure HDInsight (HDI) which was challenging to scale and maintain. It presented performance issues and lacked integrations. Version 2.0 with Databricks will provide improved scalability, cost optimization, governance, and ease of use through features like Delta Lake, Unity Catalog, and collaborative notebooks. This will help address challenges faced by consumers, data engineers, and the client.
Azure Days 2019: Business Intelligence auf Azure (Marco Amhof & Yves Mauron)Trivadis
In dieser Session stellen wir ein Projekt vor, in welchem wir ein umfassendes BI-System mit Hilfe von Azure Blob Storage, Azure SQL, Azure Logic Apps und Azure Analysis Services für und in der Azure Cloud aufgebaut haben. Wir berichten über die Herausforderungen, wie wir diese gelöst haben und welche Learnings und Best Practices wir mitgenommen haben.
Azure SQL Database Managed Instance is a new flavor of Azure SQL Database that is a game changer. It offers near-complete SQL Server compatibility and network isolation to easily lift and shift databases to Azure (you can literally backup an on-premise database and restore it into a Azure SQL Database Managed Instance). Think of it as an enhancement to Azure SQL Database that is built on the same PaaS infrastructure and maintains all it's features (i.e. active geo-replication, high availability, automatic backups, database advisor, threat detection, intelligent insights, vulnerability assessment, etc) but adds support for databases up to 35TB, VNET, SQL Agent, cross-database querying, replication, etc. So, you can migrate your databases from on-prem to Azure with very little migration effort which is a big improvement from the current Singleton or Elastic Pool flavors which can require substantial changes.
Cloud and Analytics - From Platforms to an EcosystemDatabricks
Zurich North America is one of the largest providers of insurance solutions and services in the world with customers representing a wide range of industries from agriculture to construction and more than 90 percent of the Fortune 500.
This document discusses Zurich Insurance Group's use of cloud analytics platforms and technologies. It outlines how Zurich leverages multiple data sources and tools for data exploration, integration, modeling and deployment. Key elements of their ecosystem include a data lake on Azure, various analytics tools, containerization, and DevOps processes to automate deployments and upgrades. The goal is to accelerate insights, improve agility and reduce costs through this cloud-based analytics environment.
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.
This document discusses designing a modern data warehouse in Azure. It provides an overview of traditional vs. self-service data warehouses and their limitations. It also outlines challenges with current data warehouses around timeliness, flexibility, quality and findability. The document then discusses why organizations need a modern data warehouse based on criteria like customer experience, quality assurance and operational efficiency. It covers various approaches to ingesting, storing, preparing and modeling data in Azure. Finally, it discusses architectures like the lambda architecture and common data models.
This document discusses designing a modern data warehouse in Azure. It provides an overview of traditional vs. self-service data warehouses and their limitations. It also outlines challenges with current data warehouses around timeliness, flexibility, quality and findability. The document then discusses why organizations need a modern data warehouse based on criteria like customer experience, quality assurance and operational efficiency. It covers various approaches to ingesting, storing, preparing, modeling and serving data on Azure. Finally, it discusses architectures like the lambda architecture and common data models.
Migrating Enterprise Applications to AWSTom Laszewski
The document provides an overview of best practices for migrating enterprise applications to AWS. It discusses calculating total cost of ownership, licensing models, mapping on-premises infrastructure to equivalent AWS services, migration approaches and best practices. Specific sections cover identifying good candidate applications, conducting proof of concepts, migrating data, tools and services to assist with migrations. It also shares lessons learned from a customer project that migrated their Oracle EBS and OBIEE environments to AWS, achieving significant cost savings and other benefits.
Understanding System Design and Architecture Blueprints of EfficiencyKnoldus Inc.
This exploration delves into the intricate world of system design and architecture, dissecting the fundamental principles and methodologies that underpin the creation of robust and scalable systems. From the conceptualization of software structures to the deployment of hardware components, this comprehensive study navigates through the critical decisions and considerations that engineers face when crafting efficient and reliable systems. Gain insights into best practices, design patterns, and emerging trends that shape the backbone of modern technology, empowering you to engineer solutions that stand the test of time. Whether you're a seasoned architect or an aspiring designer, embark on a journey to master the art and science of system design and architecture.
Azure SQL Database now has a Managed Instance, for near 100% compatibility for lifting-and-shifting applications running on Microsoft SQL Server to Azure. Contact me for more information.
The breath and depth of Azure products that fall under the AI and ML umbrella can be difficult to follow. In this presentation I’ll first define exactly what AI, ML, and deep learning is, and then go over the various Microsoft AI and ML products and their use cases.
SQL Saturday Redmond 2019 ETL Patterns in the CloudMark Kromer
This document discusses ETL patterns in the cloud using Azure Data Factory. It covers topics like ETL vs ELT, scaling ETL in the cloud, handling flexible schemas, and using ADF for orchestration. Key points include staging data in low-cost storage before processing, using ADF's integration runtime to process data both on-premises and in the cloud, and building resilient data flows that can handle schema drift.
Modern Analytics Academy - Data Modeling (1).pptxssuser290967
This document provides an overview of Modern Analytics Academy and Azure Synapse Analytics. It introduces the Modern Analytics Academy team and their agenda to discuss modeling, data lakes, Synapse, and a demo. It then covers key concepts like the data lake, logical data warehouse, and data warehouse. It describes the role of data in modern analytics between data lakes and data warehouses. Finally, it introduces Azure Synapse Analytics and its capabilities for dedicated SQL pools, serverless SQL pools, and Apache Spark pools for unified analytics.
Modern DW Architecture
- The document discusses modern data warehouse architectures using Azure cloud services like Azure Data Lake, Azure Databricks, and Azure Synapse. It covers storage options like ADLS Gen 1 and Gen 2 and data processing tools like Databricks and Synapse. It highlights how to optimize architectures for cost and performance using features like auto-scaling, shutdown, and lifecycle management policies. Finally, it provides a demo of a sample end-to-end data pipeline.
Just-in-time: Repetitive production system in which processing and movement of materials and goods occur just as they are needed, usually in small batches
JIT is characteristic of lean production systems
JIT operates with very little “fat”
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!
computer organization and assembly language : its about types of programming language along with variable and array description..https://www.nfciet.edu.pk/
GenAI for Quant Analytics: survey-analytics.aiInspirient
Pitched at the Greenbook Insight Innovation Competition as apart of IIEX North America 2025 on 30 April 2025 in Washington, D.C.
Join us at survey-analytics.ai!
2. Introduction
• Education
• B.S. in Computer Science from UW-Milwaukee
• M.S. in Science of Management from Cardinal
Stritch
• Professional
• Fiserv, DentaQuest, BMO Harris
• Focus on SQL, SSIS, and DevOps strategies
• Talavant
• Focus on larger BI strategies
Turner Kunkel
[email protected]
Senior Consultant @ Talavant
• Hobbies
• Home brewing
• Bass playing (poorly)
• Sailing (light skipper license)
3. There is a better way to make data work for companies. Better resources, strategy, sustainability, inclusion of the
organization as a whole, understanding of client needs, tools, outcomes, better ROI.
• Accelerated planning, implementation and results
• Sustainable solutions
• Increased company-wide buy-in & usage
VALUE WE PROVIDE
By providing a holistic approach inclusive of a
client’s people, processes and technologies -
built on investment in our own employees and
company growth.
HOW WE DO IT
STRATEGY
ARCHITECTURE
IMPLEMENTATION
4. OVERVIEW
On-Premises SSAS
History of AS • ROI considerations
• Future research
• Questions/Comments
• Sources & References
Azure AS
Developing w/ AAS
Automation on AAS
5. END PRODUCT
Robust Power BI sample from Microsoft
2.04 billion NY Taxi trips
Refresh using 20 Azure cores in ~4 seconds
7. OLAP
Database theory
(X,Y,Z) → W (X,Y,Z are axis in a cube, W is value of
cell)
Need arose from tab report structure
from 1980’s database management
systems
Slice, Dice, Drill Down, Roll Up
11. BUSINESS USE
• Explore data from outside the organization
• Historical insight
• Trends and predictive analysis
• Self-service opportunities for business users
• Space saving
• Time saving
Reporting Tools
SSRS
SharePoint
Power Pivot
Power BI
Tableau (and other third parties)
19. MODEL DEVELOPMENT
Web interface (public preview)
Edit relationships, measures, hierarchies
Drag-and-drop query editor for data
Translates to DAX if needed
Interface to open model in several tools
SQL Server Data Tools/Visual Studio
(>=2016)
SQL Server Management Studio
(>=2016)
20. MIGRATION
Best practice
Incremental port of solution
Lean, Phased, Ramp up
Types
Full
Hybrid/piece-wise
Tools
On-premises Gateway
Visual Studio, SQL Management Studio
21. GATEWAY
Install
Download, install, register on-premises
Use
Connect gateway to AAS instance
Configure
Add gateway resource to Azure
*Performs slowly on wireless
*Communication is encrypted
22. DATA ACCESS
DirectQuery benefits
Up-to-date data
Stronger security
Optimized query plan
In-Memory Cache
Stronger query performance
Data refresh required
DirectQuery limitations
Sources
SQL server, Oracle, Teradata
No stored procedures
No calculated tables
Query language translations
23. “HIGH AVAILABILITY”
Backups
Azure allows backup storage
Configurable in the AAS instance
Can backup and restore to separate instances
Hint: Use deployment
scripts
Redundancy
Rarely, Azure servers go down
Ensure availability by deploying to another instance
in a different region
Process each instance in parallel
25. AUTOMATION
Automation Uses
• Model processing on schedule
• Full or incremental
• Start/Stop AAS on schedule
• Scale AAS automatically
• Backup AAS instance on schedule
How to Automate
• Azure Function Apps
• Azure RunBooks
• Classic SSMS/SSIS
• Custom .NET application
• Custom PowerShell
26. FUNCTION APPS
• Separate module in Azure
• Premade Functions
• Webhook/API, Timer (CRON), Data Processing
• Templates (C#, F#, JavaScript)
• Custom functions
• PowerShell, Python, and Windows Batch
• Analysis Server libraries, providers, and
connection string references needed
• Web application sitting in Azure
• Separate pricing model
• Can use Source Control/TFS
27. RUNBOOKS
• Requires Azure Automation account
• Uses ‘Run As’ account to connect to AAS
• RunBooks support PowerShell and Python scripts
• Can be run on a recurring schedule
29. ROI & CONSIDERATIONS
Advantages to movement to AAS
• No physical space needed for servers
• One cloud platform for development
• Azure is Microsoft’s future – AAS is beneficial to learn
• Scaling and automating use is quick
• Integration to already used tools
• Processing power is immense for large data sets
• Access administration is streamlined
• Redundancy built-in
• Simple and forecasted pricing model
Disadvantages to movement to AAS
• Learning curve
• (Possible!) replacement of SSAS resume skill
30. OTHER FEATURES
Query Replicas
Queries distributed among multiple replicas in
a pool
Up to 7 additional query streams (8 total)
Good for high-QPU usage times
Can be configured based on instance usage
Tags
Azure general feature – Name/Value pair for
resources
For example: Environment/Development
Locks
Azure general feature – Locks resource to
prevent actions on the resource
Diagnostics
Performance logs
Event logs
Error logs
Azure infrastructure logs
31. FUTURE FEATURES
AAS support in Power BI Embedded Azure Data Lake storage as data source
Multidimensional cube support
Schema compare
Excel Online connections
AAS data source in SSRS
32. FUTURE RESEARCH
Automation best practices
Deterministic scaling development
Best uses of RunBooks, Functions, Replicas, or
other techniques?
Migration strategy
Full approach to migration plan
Testing techniques while migrating leanly
True Implementation Cost
Production down time while migrating
Opportunity cost and monetary cost during
learning curve
Buy-in from business
On-boarding of business users, if necessary
Production monitoring
Best practices for maintenance
How many resources required
Improvements to implemented system and
development life cycle
33. RECAP
On-Premises SSAS
History of AS • ROI considerations
• Future research
• Questions/Comments
• Sources & References
Azure AS
Developing w/ AAS
Automation on AAS