The Rise of Data in Motion in the Healthcare Industry - Use Cases, Architectures and Examples powered by Apache Kafka.
Use Cases for Data in Motion in the Healthcare Industry:
- Know Your Patient (= “Customer 360”)
- Operations (Healthcare 4.0 including Drug R&D, Patient Care, etc.)
- IT Perspective (Cybersecurity, Mainframe Offload, Hybrid Cloud, Streaming ETL, etc)
Real-world examples include Covid-19 Electronic Lab Reporting, Cerner, Optum, Centene, Humana, Invitae, Bayer, Celmatix, Care.com.
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.
Ameerpet Online Training gives you an effective and proven online learning option with an extensive learning catalog and the freedom to attend from virtually anywhere. We have trained nearly 1500+ Students on all technologies.
We are offering 10% off on Oracle Training and we will arrange a free demo at your flexible timings
Data Modeling & Metadata for Graph DatabasesDATAVERSITY
Graph databases are seeing a spike in popularity as their value in leveraging large data sets for key areas such as fraud detection, marketing, and network optimization become increasingly apparent. With graph databases, it’s been said that ‘the data model and the metadata are the database’. What does this mean in a practical application, and how can this technology be optimized for maximum business value?
The Rise of Data in Motion in the Healthcare Industry - Use Cases, Architectures and Examples powered by Apache Kafka.
Use Cases for Data in Motion in the Healthcare Industry:
- Know Your Patient (= “Customer 360”)
- Operations (Healthcare 4.0 including Drug R&D, Patient Care, etc.)
- IT Perspective (Cybersecurity, Mainframe Offload, Hybrid Cloud, Streaming ETL, etc)
Real-world examples include Covid-19 Electronic Lab Reporting, Cerner, Optum, Centene, Humana, Invitae, Bayer, Celmatix, Care.com.
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.
Ameerpet Online Training gives you an effective and proven online learning option with an extensive learning catalog and the freedom to attend from virtually anywhere. We have trained nearly 1500+ Students on all technologies.
We are offering 10% off on Oracle Training and we will arrange a free demo at your flexible timings
Data Modeling & Metadata for Graph DatabasesDATAVERSITY
Graph databases are seeing a spike in popularity as their value in leveraging large data sets for key areas such as fraud detection, marketing, and network optimization become increasingly apparent. With graph databases, it’s been said that ‘the data model and the metadata are the database’. What does this mean in a practical application, and how can this technology be optimized for maximum business value?
Data Lakehouse Symposium | Day 1 | Part 2Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Scaling and Modernizing Data Platform with DatabricksDatabricks
Today a Data Platform is expected to process and analyze a multitude of sources spanning batch files, streaming sources, backend databases, REST APIs, and more. There is clearly a need for standardizing the platform that scales and be flexible letting data engineers and data scientists focus on the business problems rather than managing the infrastructure and backend services. Another key aspect of the platform is multi-tenancy to isolate the workloads and able to track cost usage per tenant.
In this talk, Richa Singhal and Esha Shah will cover how to build a scalable Data Platform using Databricks and deploy your data pipelines effectively while managing the costs. The following topics will be covered:
Key tenets of a Data Platform
Setup multistage environment on Databricks
Build data pipelines locally and test on Databricks cluster
CI/CD for data pipelines with Databricks
Orchestrating pipelines using Apache Airflow – Change Data Capture using Databricks Delta
Leveraging Databricks Notebooks for Analytics and Data Science teams
Automating Your Clone in E-Business Suite R12.2Michael Brown
It is possible to automate the cloning process in Oracle E-Business Suite 12.2. This presentation discusses how to accomplish that and gives some warnings about when it is not possible to run a clone.
For OAUG members, the slides and a recording of the presentation are available on www.oaug.org.
Master the Multi-Clustered Data Warehouse - SnowflakeMatillion
Snowflake is one of the most powerful, efficient data warehouses on the market today—and we joined forces with the Snowflake team to show you how it works!
In this webinar:
- Learn how to optimize Snowflake
- Hear insider tips and tricks on how to improve performance
- Get expert insights from Craig Collier, Technical Architect from Snowflake, and Kalyan Arangam, Solution Architect from Matillion
- Find out how leading brands like Converse, Duo Security, and Pets at Home use Snowflake and Matillion ETL to make data-driven decisions
- Discover how Matillion ETL and Snowflake work together to modernize your data world
- Learn how to utilize the impressive scalability of Snowflake and Matillion
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.
A short introduction to WinAPI and DirectX 11 that I created for Game Designers' Students Club on Poznań University of Technology. It isn't finished, but still I hope I can help someone with it :)
//edit:
I've just found out that some text in the source code is cropped, here you can find the online version of the presentation (in which you can scroll the code): http://jotunheim.kmarciniak.com/prezentacje/directx/online/#/
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.
Introduction to Oracle Cloud Infrastructure ServicesKnoldus Inc.
Oracle Cloud Infrastructure is a set of complementary cloud services that enable you to build and run a wide range of applications and services in a highly available hosted environment. Oracle Cloud Infrastructure (OCI) offers high-performance compute capabilities (as physical hardware instances) and storage capacity in a flexible overlay virtual network that is securely accessible from your on-premises network.
Time series Analytics - a deep dive into ADX Azure Data Explorer @Data Saturd...Riccardo Zamana
Time series Analytics - a deep dive into ADX Azure Data Explorer. Let’s discover with a step-by-step approach the entire ecosystem of features driven by Azure Data eXplorer.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
DockerCon SF 2015: The Distributed System ToolkitDocker, Inc.
Slides from Brendan Burns Advanced Tech Talk at DockerCon SF 2015:
Talk Description:
People often adopt containers for the dramatic improvements in application packaging and deployment that they provide. However, possibly more important, is the abstraction layer that containers provide. By encouraging users to build their distributed applications through containerized modules, rather than monolithic systems, developers are building composable, reusable distributed applications. In this talk we will explore both the development of abstract application patterns for distributed systems, as well as introduce a set of reusable, composable containers that radically simplify the process of distributed application design and construction.
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
Data Lakehouse Symposium | Day 1 | Part 2Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Scaling and Modernizing Data Platform with DatabricksDatabricks
Today a Data Platform is expected to process and analyze a multitude of sources spanning batch files, streaming sources, backend databases, REST APIs, and more. There is clearly a need for standardizing the platform that scales and be flexible letting data engineers and data scientists focus on the business problems rather than managing the infrastructure and backend services. Another key aspect of the platform is multi-tenancy to isolate the workloads and able to track cost usage per tenant.
In this talk, Richa Singhal and Esha Shah will cover how to build a scalable Data Platform using Databricks and deploy your data pipelines effectively while managing the costs. The following topics will be covered:
Key tenets of a Data Platform
Setup multistage environment on Databricks
Build data pipelines locally and test on Databricks cluster
CI/CD for data pipelines with Databricks
Orchestrating pipelines using Apache Airflow – Change Data Capture using Databricks Delta
Leveraging Databricks Notebooks for Analytics and Data Science teams
Automating Your Clone in E-Business Suite R12.2Michael Brown
It is possible to automate the cloning process in Oracle E-Business Suite 12.2. This presentation discusses how to accomplish that and gives some warnings about when it is not possible to run a clone.
For OAUG members, the slides and a recording of the presentation are available on www.oaug.org.
Master the Multi-Clustered Data Warehouse - SnowflakeMatillion
Snowflake is one of the most powerful, efficient data warehouses on the market today—and we joined forces with the Snowflake team to show you how it works!
In this webinar:
- Learn how to optimize Snowflake
- Hear insider tips and tricks on how to improve performance
- Get expert insights from Craig Collier, Technical Architect from Snowflake, and Kalyan Arangam, Solution Architect from Matillion
- Find out how leading brands like Converse, Duo Security, and Pets at Home use Snowflake and Matillion ETL to make data-driven decisions
- Discover how Matillion ETL and Snowflake work together to modernize your data world
- Learn how to utilize the impressive scalability of Snowflake and Matillion
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.
A short introduction to WinAPI and DirectX 11 that I created for Game Designers' Students Club on Poznań University of Technology. It isn't finished, but still I hope I can help someone with it :)
//edit:
I've just found out that some text in the source code is cropped, here you can find the online version of the presentation (in which you can scroll the code): http://jotunheim.kmarciniak.com/prezentacje/directx/online/#/
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.
Introduction to Oracle Cloud Infrastructure ServicesKnoldus Inc.
Oracle Cloud Infrastructure is a set of complementary cloud services that enable you to build and run a wide range of applications and services in a highly available hosted environment. Oracle Cloud Infrastructure (OCI) offers high-performance compute capabilities (as physical hardware instances) and storage capacity in a flexible overlay virtual network that is securely accessible from your on-premises network.
Time series Analytics - a deep dive into ADX Azure Data Explorer @Data Saturd...Riccardo Zamana
Time series Analytics - a deep dive into ADX Azure Data Explorer. Let’s discover with a step-by-step approach the entire ecosystem of features driven by Azure Data eXplorer.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
DockerCon SF 2015: The Distributed System ToolkitDocker, Inc.
Slides from Brendan Burns Advanced Tech Talk at DockerCon SF 2015:
Talk Description:
People often adopt containers for the dramatic improvements in application packaging and deployment that they provide. However, possibly more important, is the abstraction layer that containers provide. By encouraging users to build their distributed applications through containerized modules, rather than monolithic systems, developers are building composable, reusable distributed applications. In this talk we will explore both the development of abstract application patterns for distributed systems, as well as introduce a set of reusable, composable containers that radically simplify the process of distributed application design and construction.
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
Data blending is important because it allows data analysts
to access data from all the relevant data sources: Big Data,
the cloud, social media, third-party data providers, in-house
databases, department data stores, and more. Historically,
the challenge of data analysts has been accessing this data
and then cleansing and preparing the data for analysis. These
stages of access, cleansing, and preparing data are complex
and time intensive. Easy-to-use software tools that reduce the
burden of this data preparation and turn data blending into an
asset greatly empower the data analyst to become more effective and open new opportunities to the business.
The focus of this book is how data blending is used and what
it can provide the data analyst working to support business
decision makers. I identify what features to look for in data
blending tools and how to successfully deploy these tools and
data blending within your business.
Big Data Analytics é uma ferramenta importante para ganhar vantagem competitiva e para atrair e reter clientes. A simples implantação de um pacote de software não é suficiente. Entenda como uma infraestrutura performática, eficiente e ágil pode ser impactante para o sucesso da sua empresa.
Technology trends, such as BYOD, cloud computing, and the digital workplace, are driving a culture of shadow IT in today's fast-paced organizations. To address these trends and remain relevant, IT organizations must change their operation models fro service support to service brokering. This book explains how.
Dear Reader, this book is designed to provide you the necessary insights to move beyond simply storing data and into creating a framework under which data can be intelligently governed for the length of time it has value to your business or regulations require it to be kept (whichever is greater).
We hope you find this book insightful — allowing you and your
organization to accelerate your ability to enact intelligent data
governance.
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