Azure Data Factory Data Flow Limited Preview for January 2019

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#Microsoft #Azure #DataFactory limited preview overview for January 2019

Azure Data Factory
Visual Data Flow
Limited Preview January 2019

ADF Data Flow Workstream
Data Sources Staging Transformations Destination
Sort, Merge, Join,
Lookup …
• Explicit user action
• User places data
source(s) on design
surface, from toolbox
• Select explicit sources
• Implicit/Explicit
• Data Lake staging area
as default
• User does not need to
configure this manually
• Advanced feature to set
staging area options
• File Formats / Types
(Parquet, JSON, txt, CSV
…)
• Explicit user action
• User places
transformations on
design surface, from
toolbox
• User must set properties
for transformation steps
and step connectors
• Explicit user action
• User chooses destination
connector(s)
• User sets connector
property options

ADF Data Flow Workstream
Stage Data in Azure
(ADLS, Blob, SQL
DB/DW)
Transform Data in
Visual Data Flow
Land Data in Azure
Staging Area (ADLS,
Blob, SQL DB/DW)

adfdataflowext@microsoft.com
http://aka.ms/dataflowpreview

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

Azure Data Factory Data Flow Limited Preview for January 2019

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Principled Technologies

This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.

Powerful Google developer tools for immediate impact! (2023-24 C)

Powerful Google developer tools for immediate impact! (2023-24 C)

Powerful Google developer tools for immediate impact! (2023-24 C)

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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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Artificial Intelligence: Facts and Myths

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The 7 Things I Know About Cyber Security After 25 Years | April 2024

The 7 Things I Know About Cyber Security After 25 Years | April 2024

The 7 Things I Know About Cyber Security After 25 Years | April 2024

Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

AWS Community Day CPH - Three problems of Terraform

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AWS Community Day CPH - Three problems of Terraform

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Data Cloud, More than a CDP by Matt Robison

Data Cloud, More than a CDP by Matt Robison

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Scaling API-first – The story of a global engineering organization

Scaling API-first – The story of a global engineering organization

Scaling API-first – The story of a global engineering organization

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Boost PC performance: How more available memory can improve productivity

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Powerful Google developer tools for immediate impact! (2023-24 C)

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Azure Data Factory Data Flow Limited Preview for January 2019

1. Azure Data Factory Visual Data Flow Limited Preview January 2019

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5. ADF Data Flow Workstream Data Sources Staging Transformations Destination Sort, Merge, Join, Lookup … • Explicit user action • User places data source(s) on design surface, from toolbox • Select explicit sources • Implicit/Explicit • Data Lake staging area as default • User does not need to configure this manually • Advanced feature to set staging area options • File Formats / Types (Parquet, JSON, txt, CSV …) • Explicit user action • User places transformations on design surface, from toolbox • User must set properties for transformation steps and step connectors • Explicit user action • User chooses destination connector(s) • User sets connector property options

6. ADF Data Flow Workstream Stage Data in Azure (ADLS, Blob, SQL DB/DW) Transform Data in Visual Data Flow Land Data in Azure Staging Area (ADLS, Blob, SQL DB/DW)

7. adfdataflowext@microsoft.com http://aka.ms/dataflowpreview