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.
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.
A Work of Zhamak Dehghani
Principal consultant
ThoughtWorks
https://martinfowler.com/articles/data-monolith-to-mesh.html
https://fast.wistia.net/embed/iframe/vys2juvzc3?videoFoam
How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh
Many enterprises are investing in their next generation data lake, with the hope of democratizing data at scale to provide business insights and ultimately make automated intelligent decisions. Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale. To address these failure modes we need to shift from the centralized paradigm of a lake, or its predecessor data warehouse. We need to shift to a paradigm that draws from modern distributed architecture: considering domains as the first class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product.
this is part 3 of the series on Data Mesh ... looking at the intersection of microservices architecture concepts, data integration / replication technologies and log-based stream integration techniques. This webinar was mostly a demonstration, but several slides used to setup the demo are included here as a PDF for viewers.
Wonder what this data mesh stuff is all about? What are the principles of data mesh? Can you or should you consider data mesh as the approach for your analytics platform? And most important - how can Snowflake help?
Given in Montreal on 14-Dec-2021
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
Enabling a Data Mesh Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
Data Mesh in Practice - How Europe's Leading Online Platform for Fashion Goes...Dr. Arif Wider
A talk presented by Max Schultze from Zalando and Arif Wider from ThoughtWorks at NDC Oslo 2020.
Abstract:
The Data Lake paradigm is often considered the scalable successor of the more curated Data Warehouse approach when it comes to democratization of data. However, many who went out to build a centralized Data Lake came out with a data swamp of unclear responsibilities, a lack of data ownership, and sub-par data availability.
At Zalando - europe’s biggest online fashion retailer - we realised that accessibility and availability at scale can only be guaranteed when moving more responsibilities to those who pick up the data and have the respective domain knowledge - the data owners - while keeping only data governance and metadata information central. Such a decentralized and domain focused approach has recently been coined a Data Mesh.
The Data Mesh paradigm promotes the concept of Data Products which go beyond sharing of files and towards guarantees of quality and acknowledgement of data ownership.
This talk will take you on a journey of how we went from a centralized Data Lake to embrace a distributed Data Mesh architecture and will outline the ongoing efforts to make creation of data products as simple as applying a template.
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.
A Work of Zhamak Dehghani
Principal consultant
ThoughtWorks
https://martinfowler.com/articles/data-monolith-to-mesh.html
https://fast.wistia.net/embed/iframe/vys2juvzc3?videoFoam
How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh
Many enterprises are investing in their next generation data lake, with the hope of democratizing data at scale to provide business insights and ultimately make automated intelligent decisions. Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale. To address these failure modes we need to shift from the centralized paradigm of a lake, or its predecessor data warehouse. We need to shift to a paradigm that draws from modern distributed architecture: considering domains as the first class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product.
this is part 3 of the series on Data Mesh ... looking at the intersection of microservices architecture concepts, data integration / replication technologies and log-based stream integration techniques. This webinar was mostly a demonstration, but several slides used to setup the demo are included here as a PDF for viewers.
Wonder what this data mesh stuff is all about? What are the principles of data mesh? Can you or should you consider data mesh as the approach for your analytics platform? And most important - how can Snowflake help?
Given in Montreal on 14-Dec-2021
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
Enabling a Data Mesh Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
Data Mesh in Practice - How Europe's Leading Online Platform for Fashion Goes...Dr. Arif Wider
A talk presented by Max Schultze from Zalando and Arif Wider from ThoughtWorks at NDC Oslo 2020.
Abstract:
The Data Lake paradigm is often considered the scalable successor of the more curated Data Warehouse approach when it comes to democratization of data. However, many who went out to build a centralized Data Lake came out with a data swamp of unclear responsibilities, a lack of data ownership, and sub-par data availability.
At Zalando - europe’s biggest online fashion retailer - we realised that accessibility and availability at scale can only be guaranteed when moving more responsibilities to those who pick up the data and have the respective domain knowledge - the data owners - while keeping only data governance and metadata information central. Such a decentralized and domain focused approach has recently been coined a Data Mesh.
The Data Mesh paradigm promotes the concept of Data Products which go beyond sharing of files and towards guarantees of quality and acknowledgement of data ownership.
This talk will take you on a journey of how we went from a centralized Data Lake to embrace a distributed Data Mesh architecture and will outline the ongoing efforts to make creation of data products as simple as applying a template.
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.
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.
Data Mesh is a new socio-technical approach to data architecture, first described by Zhamak Dehghani and popularised through a guest blog post on Martin Fowler's site.
Since then, community interest has grown, due to Data Mesh's ability to explain and address the frustrations that many organisations are experiencing as they try to get value from their data. The 2022 publication of Zhamak's book on Data Mesh further provoked conversation, as have the growing number of experience reports from companies that have put Data Mesh into practice.
So what's all the fuss about?
On one hand, Data Mesh is a new approach in the field of big data. On the other hand, Data Mesh is application of the lessons we have learned from domain-driven design and microservices to a data context.
In this talk, Chris and Pablo will explain how Data Mesh relates to current thinking in software architecture and the historical development of data architecture philosophies. They will outline what benefits Data Mesh brings, what trade-offs it comes with and when organisations should and should not consider adopting it.
[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.
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Intuit's Data Mesh - Data Mesh Leaning Community meetup 5.13.2021Tristan Baker
Past, present and future of data mesh at Intuit. This deck describes a vision and strategy for improving data worker productivity through a Data Mesh approach to organizing data and holding data producers accountable. Delivered at the inaugural Data Mesh Leaning meetup on 5/13/2021.
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.
Data Catalog for Better Data Discovery and GovernanceDenodo
Watch full webinar here: https://buff.ly/2Vq9FR0
Data catalogs are en vogue answering critical data governance questions like “Where all does my data reside?” “What other entities are associated with my data?” “What are the definitions of the data fields?” and “Who accesses the data?” Data catalogs maintain the necessary business metadata to answer these questions and many more. But that’s not enough. For it to be useful, data catalogs need to deliver these answers to the business users right within the applications they use.
In this session, you will learn:
*How data catalogs enable enterprise-wide data governance regimes
*What key capability requirements should you expect in data catalogs
*How data virtualization combines dynamic data catalogs with delivery
Build Real-Time Applications with Databricks StreamingDatabricks
In this presentation, we will study a recent use case we implemented recently. In this use case we are working with a large, metropolitan fire department. Our company has already created a complete analytics architecture for the department based upon Azure Data Factory, Databricks, Delta Lake, Azure SQL and Azure SQL Server Analytics Services (SSAS). While this architecture works very well for the department, they would like to add a real-time channel to their reporting infrastructure.
This channel should serve up the following information: •The most up-to-date locations and status of equipment (fire trucks, ambulances, ladders etc.)
• The current locations and status of firefighters, EMT personnel and other relevant fire department employees
• The current list of active incidents within the city The above information should be visualized through an automatically updating dashboard. The central component of the dashboard will be map which automatically updates with the locations and incidents. This view should be as real-time as possible and will be used by the fire chiefs to assist with real-time decision-making on resource and equipment deployments.
In this presentation, we will leverage Databricks, Spark Structured Streaming, Delta Lake and the Azure platform to create this real-time delivery channel.
Building Data Quality pipelines with Apache Spark and Delta LakeDatabricks
Technical Leads and Databricks Champions Darren Fuller & Sandy May will give a fast paced view of how they have productionised Data Quality Pipelines across multiple enterprise customers. Their vision to empower business decisions on data remediation actions and self healing of Data Pipelines led them to build a library of Data Quality rule templates and accompanying reporting Data Model and PowerBI reports.
With the drive for more and more intelligence driven from the Lake and less from the Warehouse, also known as the Lakehouse pattern, Data Quality at the Lake layer becomes pivotal. Tools like Delta Lake become building blocks for Data Quality with Schema protection and simple column checking, however, for larger customers they often do not go far enough. Notebooks will be shown in quick fire demos how Spark can be leverage at point of Staging or Curation to apply rules over data.
Expect to see simple rules such as Net sales = Gross sales + Tax, or values existing with in a list. As well as complex rules such as validation of statistical distributions and complex pattern matching. Ending with a quick view into future work in the realm of Data Compliance for PII data with generations of rules using regex patterns and Machine Learning rules based on transfer learning.
Data Lakehouse, Data Mesh, and Data Fabric (r2)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 modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
Snowflake: The most cost-effective agile and scalable data warehouse ever!Visual_BI
In this webinar, the presenter will take you through the most revolutionary data warehouse, Snowflake with a live demo and technical and functional discussions with a customer. Ryan Goltz from Chesapeake Energy and Tristan Handy, creator of DBT Cloud and owner of Fishtown Analytics will also be joining the webinar.
Making Data Timelier and More Reliable with Lakehouse TechnologyMatei Zaharia
Enterprise data architectures usually contain many systems—data lakes, message queues, and data warehouses—that data must pass through before it can be analyzed. Each transfer step between systems adds a delay and a potential source of errors. What if we could remove all these steps? In recent years, cloud storage and new open source systems have enabled a radically new architecture: the lakehouse, an ACID transactional layer over cloud storage that can provide streaming, management features, indexing, and high-performance access similar to a data warehouse. Thousands of organizations including the largest Internet companies are now using lakehouses to replace separate data lake, warehouse and streaming systems and deliver high-quality data faster internally. I’ll discuss the key trends and recent advances in this area based on Delta Lake, the most widely used open source lakehouse platform, which was developed at Databricks.
Webinar future dataintegration-datamesh-and-goldengatekafkaJeffrey T. Pollock
The Future of Data Integration: Data Mesh, and a Special Deep Dive into Stream Processing with GoldenGate, Apache Kafka and Apache Spark. This video is a replay of a Live Webinar hosted on 03/19/2020.
Join us for a timely 45min webinar to see our take on the future of Data Integration. As the global industry shift towards the “Fourth Industrial Revolution” continues, outmoded styles of centralized batch processing and ETL tooling continue to be replaced by realtime, streaming, microservices and distributed data architecture patterns.
This webinar will start with a brief look at the macro-trends happening around distributed data management and how that affects Data Integration. Next, we’ll discuss the event-driven integrations provided by GoldenGate Big Data, and continue with a deep-dive into some essential patterns we see when replicating Database change events into Apache Kafka. In this deep-dive we will explain how to effectively deal with issues like Transaction Consistency, Table/Topic Mappings, managing the DB Change Stream, and various Deployment Topologies to consider. Finally, we’ll wrap up with a brief look into how Stream Processing will help to empower modern Data Integration by supplying realtime data transformations, time-series analytics, and embedded Machine Learning from within data pipelines.
GoldenGate: https://www.oracle.com/middleware/tec...
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Data Engineer, Patterns & Architecture The future: Deep-dive into Microservic...Igor De Souza
With Industry 4.0, several technologies are used to have data analysis in real-time, maintaining, organizing, and building this on the other hand is a complex and complicated job. Over the past 30 years, we saw several ideas to centralize the database in a single place as the united and true source of data has been implemented in companies, such as Data wareHouse, NoSQL, Data Lake, Lambda & Kappa Architecture.
On the other hand, Software Engineering has been applying ideas to separate applications to facilitate and improve application performance, such as microservices.
The idea is to use the MicroService patterns on the date and divide the model into several smaller ones. And a good way to split it up is to use the model using the DDD principles. And that's how I try to explain and define DataMesh & Data Fabric.
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.
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.
Data Mesh is a new socio-technical approach to data architecture, first described by Zhamak Dehghani and popularised through a guest blog post on Martin Fowler's site.
Since then, community interest has grown, due to Data Mesh's ability to explain and address the frustrations that many organisations are experiencing as they try to get value from their data. The 2022 publication of Zhamak's book on Data Mesh further provoked conversation, as have the growing number of experience reports from companies that have put Data Mesh into practice.
So what's all the fuss about?
On one hand, Data Mesh is a new approach in the field of big data. On the other hand, Data Mesh is application of the lessons we have learned from domain-driven design and microservices to a data context.
In this talk, Chris and Pablo will explain how Data Mesh relates to current thinking in software architecture and the historical development of data architecture philosophies. They will outline what benefits Data Mesh brings, what trade-offs it comes with and when organisations should and should not consider adopting it.
[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.
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Intuit's Data Mesh - Data Mesh Leaning Community meetup 5.13.2021Tristan Baker
Past, present and future of data mesh at Intuit. This deck describes a vision and strategy for improving data worker productivity through a Data Mesh approach to organizing data and holding data producers accountable. Delivered at the inaugural Data Mesh Leaning meetup on 5/13/2021.
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.
Data Catalog for Better Data Discovery and GovernanceDenodo
Watch full webinar here: https://buff.ly/2Vq9FR0
Data catalogs are en vogue answering critical data governance questions like “Where all does my data reside?” “What other entities are associated with my data?” “What are the definitions of the data fields?” and “Who accesses the data?” Data catalogs maintain the necessary business metadata to answer these questions and many more. But that’s not enough. For it to be useful, data catalogs need to deliver these answers to the business users right within the applications they use.
In this session, you will learn:
*How data catalogs enable enterprise-wide data governance regimes
*What key capability requirements should you expect in data catalogs
*How data virtualization combines dynamic data catalogs with delivery
Build Real-Time Applications with Databricks StreamingDatabricks
In this presentation, we will study a recent use case we implemented recently. In this use case we are working with a large, metropolitan fire department. Our company has already created a complete analytics architecture for the department based upon Azure Data Factory, Databricks, Delta Lake, Azure SQL and Azure SQL Server Analytics Services (SSAS). While this architecture works very well for the department, they would like to add a real-time channel to their reporting infrastructure.
This channel should serve up the following information: •The most up-to-date locations and status of equipment (fire trucks, ambulances, ladders etc.)
• The current locations and status of firefighters, EMT personnel and other relevant fire department employees
• The current list of active incidents within the city The above information should be visualized through an automatically updating dashboard. The central component of the dashboard will be map which automatically updates with the locations and incidents. This view should be as real-time as possible and will be used by the fire chiefs to assist with real-time decision-making on resource and equipment deployments.
In this presentation, we will leverage Databricks, Spark Structured Streaming, Delta Lake and the Azure platform to create this real-time delivery channel.
Building Data Quality pipelines with Apache Spark and Delta LakeDatabricks
Technical Leads and Databricks Champions Darren Fuller & Sandy May will give a fast paced view of how they have productionised Data Quality Pipelines across multiple enterprise customers. Their vision to empower business decisions on data remediation actions and self healing of Data Pipelines led them to build a library of Data Quality rule templates and accompanying reporting Data Model and PowerBI reports.
With the drive for more and more intelligence driven from the Lake and less from the Warehouse, also known as the Lakehouse pattern, Data Quality at the Lake layer becomes pivotal. Tools like Delta Lake become building blocks for Data Quality with Schema protection and simple column checking, however, for larger customers they often do not go far enough. Notebooks will be shown in quick fire demos how Spark can be leverage at point of Staging or Curation to apply rules over data.
Expect to see simple rules such as Net sales = Gross sales + Tax, or values existing with in a list. As well as complex rules such as validation of statistical distributions and complex pattern matching. Ending with a quick view into future work in the realm of Data Compliance for PII data with generations of rules using regex patterns and Machine Learning rules based on transfer learning.
Data Lakehouse, Data Mesh, and Data Fabric (r2)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 modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
Snowflake: The most cost-effective agile and scalable data warehouse ever!Visual_BI
In this webinar, the presenter will take you through the most revolutionary data warehouse, Snowflake with a live demo and technical and functional discussions with a customer. Ryan Goltz from Chesapeake Energy and Tristan Handy, creator of DBT Cloud and owner of Fishtown Analytics will also be joining the webinar.
Making Data Timelier and More Reliable with Lakehouse TechnologyMatei Zaharia
Enterprise data architectures usually contain many systems—data lakes, message queues, and data warehouses—that data must pass through before it can be analyzed. Each transfer step between systems adds a delay and a potential source of errors. What if we could remove all these steps? In recent years, cloud storage and new open source systems have enabled a radically new architecture: the lakehouse, an ACID transactional layer over cloud storage that can provide streaming, management features, indexing, and high-performance access similar to a data warehouse. Thousands of organizations including the largest Internet companies are now using lakehouses to replace separate data lake, warehouse and streaming systems and deliver high-quality data faster internally. I’ll discuss the key trends and recent advances in this area based on Delta Lake, the most widely used open source lakehouse platform, which was developed at Databricks.
Webinar future dataintegration-datamesh-and-goldengatekafkaJeffrey T. Pollock
The Future of Data Integration: Data Mesh, and a Special Deep Dive into Stream Processing with GoldenGate, Apache Kafka and Apache Spark. This video is a replay of a Live Webinar hosted on 03/19/2020.
Join us for a timely 45min webinar to see our take on the future of Data Integration. As the global industry shift towards the “Fourth Industrial Revolution” continues, outmoded styles of centralized batch processing and ETL tooling continue to be replaced by realtime, streaming, microservices and distributed data architecture patterns.
This webinar will start with a brief look at the macro-trends happening around distributed data management and how that affects Data Integration. Next, we’ll discuss the event-driven integrations provided by GoldenGate Big Data, and continue with a deep-dive into some essential patterns we see when replicating Database change events into Apache Kafka. In this deep-dive we will explain how to effectively deal with issues like Transaction Consistency, Table/Topic Mappings, managing the DB Change Stream, and various Deployment Topologies to consider. Finally, we’ll wrap up with a brief look into how Stream Processing will help to empower modern Data Integration by supplying realtime data transformations, time-series analytics, and embedded Machine Learning from within data pipelines.
GoldenGate: https://www.oracle.com/middleware/tec...
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Data Engineer, Patterns & Architecture The future: Deep-dive into Microservic...Igor De Souza
With Industry 4.0, several technologies are used to have data analysis in real-time, maintaining, organizing, and building this on the other hand is a complex and complicated job. Over the past 30 years, we saw several ideas to centralize the database in a single place as the united and true source of data has been implemented in companies, such as Data wareHouse, NoSQL, Data Lake, Lambda & Kappa Architecture.
On the other hand, Software Engineering has been applying ideas to separate applications to facilitate and improve application performance, such as microservices.
The idea is to use the MicroService patterns on the date and divide the model into several smaller ones. And a good way to split it up is to use the model using the DDD principles. And that's how I try to explain and define DataMesh & Data Fabric.
Deep-dive into Microservices Patterns with Replication and Stream Analytics
Target Audience: Microservices and Data Architects
This is an informational presentation about microservices event patterns, GoldenGate event replication, and event stream processing with Oracle Stream Analytics. This session will discuss some of the challenges of working with data in a microservices architecture (MA), and how the emerging concept of a “Data Mesh” can go hand-in-hand to improve microservices-based data management patterns. You may have already heard about common microservices patterns like CQRS, Saga, Event Sourcing and Transaction Outbox; we’ll share how GoldenGate can simplify these patterns while also bringing stronger data consistency to your microservice integrations. We will also discuss how complex event processing (CEP) and stream processing can be used with event-driven MA for operational and analytical use cases.
Business pressures for modernization and digital transformation drive demand for rapid, flexible DevOps, which microservices address, but also for data-driven Analytics, Machine Learning and Data Lakes which is where data management tech really shines. Join us for this presentation where we take a deep look at the intersection of microservice design patterns and modern data integration tech.
Oracle OpenWorld London - session for Stream Analysis, time series analytics, streaming ETL, streaming pipelines, big data, kafka, apache spark, complex event processing
Cloud Modernization and Data as a Service OptionDenodo
Watch here: https://bit.ly/36tEThx
The current data landscape is fragmented, not just in location but also in terms of shape and processing paradigms. Cloud has become a key component of modern architecture design. Data lakes, IoT, NoSQL, SaaS, etc. coexist with relational databases to fuel the needs of modern analytics, ML and AI. Exploring and understanding the data available within your organization is a time-consuming task. Dealing with bureaucracy, different languages and protocols, and the definition of ingestion pipelines to load that data into your data lake can be complex. And all of this without even knowing if that data will be useful at all.
Attend this session to learn:
- How dynamic data challenges and the speed of change requires a new approach to data architecture – one that is real-time, agile and doesn’t rely on physical data movement.
- Learn how logical data architecture can enable organizations to transition data faster to the cloud with zero downtime and ultimately deliver faster time to insight.
- Explore how data as a service and other API management capabilities is a must in a hybrid cloud environment.
The Enterprise Guide to Building a Data Mesh - Introducing SpecMeshIanFurlong4
For organisations to successfully adopt data mesh, setting up and maintaining infrastructure needs to be easy.
We believe the best way to achieve this is to leverage the learnings from building a ‘central nervous system‘, commonly used in modern data-streaming ecosystems. This approach formalises and automates of the manual parts of building a data mesh.
This presentation introduces SpecMesh; a methodology and supporting developer toolkit to enable business to build the foundations of their data mesh.
[OpenStack Day in Korea 2015] Keynote 2 - Leveraging OpenStack to Realize the...OpenStack Korea Community
OpenStack Day in Korea 2015 - Keynote 2
Leveraging OpenStack to Realize the SKT Software-Defined Data Center
Jinsung Choi, Ph.D - CTO, Corporate R&D Center, SK Telecom
Modern Data Management for Federal ModernizationDenodo
Watch full webinar here: https://bit.ly/2QaVfE7
Faster, more agile data management is at the heart of government modernization. However, Traditional data delivery systems are limited in realizing a modernized and future-proof data architecture.
This webinar will address how data virtualization can modernize existing systems and enable new data strategies. Join this session to learn how government agencies can use data virtualization to:
- Enable governed, inter-agency data sharing
- Simplify data acquisition, search and tagging
- Streamline data delivery for transition to cloud, data science initiatives, and more
Feature Store as a Data Foundation for Machine LearningProvectus
Looking to design and build a centralized, scalable Feature Store for your Data Science & Machine Learning teams to take advantage of? Come and learn from experts of Provectus and Amazon Web Services (AWS) how to!
Feature Store is a key component of the ML stack and data infrastructure, which enables feature engineering and management. By having a Feature Store, organizations can save massive amounts of resources, innovate faster, and drive ML processes at scale. In this webinar, you will learn how to build a Feature Store with a data mesh pattern and see how to achieve consistency between real-time and training features, to improve reproducibility with time-traveling for data.
Agenda
- Modern Data Lakes & Modern ML Infrastructure
- Existing and Emerging Architectural Shifts
- Feature Store: Overview and Reference Architecture
- AWS Perspective on Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data architects & analysts, data engineers & data scientists, ML practitioners & ML engineers, and developers
Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Gandhi Raketla, Senior Solutions Architect, AWS
- German Osin, Senior Solutions Architect, Provectus
Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions!
REQUEST WEBINAR: https://provectus.com/webinar-feature-store-as-data-foundation-for-ml-nov-2020/
Data Virtualization: Introduction and Business Value (UK)Denodo
Watch full webinar here: https://bit.ly/30mHuYH
What started to evolve as the most agile and real-time enterprise data fabric, data virtualization is proving to go beyond its initial promise and is becoming one of the most important enterprise big data fabrics. Denodo’s vision is to provide a unified data delivery layer as a logical data fabric, to bridge the gap between the IT and the business, hiding the underlying complexity and creating a semantic layer to expose data in a business friendly manner.
Attend this webinar to learn:
- What data virtualization really is
- How it differs from other enterprise data integration technologies
- Why data virtualization is finding enterprise-wide deployment inside some of the largest organizations
- Business Value of data virtualization and customer use cases
- Highlights of the newly launched Denodo Platform 8.0
Analytics and Lakehouse Integration Options for Oracle ApplicationsRay Février
This Red Hot session is designed for customers who are currently using Oracle Cloud applications such as Fusion and EPM, and are interested in gaining a better understanding of the integration options that are available to them.
Here is a high level agenda:
- We will start by discussing the modern data platform on OCI, the Lakehouse architecture and the OCI related services that supports it.
- We will then discuss the data extraction methods available on OCI for Fusion and EPM.
- Last but not least, we will end with a few best practices and possible use cases.
In the interest of time, we will mainly focus on integration patterns that are recommended for Fusion and EPM, but don’t hesitate to reach out if you would to talk to us about other Oracle applications.
Enjoy!
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
Python Ireland Dec Talks - Windows Azure -- The Nuts and BoltsPython Ireland
Speaker: Stephen Fitzmaurice
Abstract:
With the Windows Azure platform, we get a highly scalable environment, pay only for the resources we need, and relieve subscribers of the responsibilities required for supporting dynamic software and hardware infrastructure.
This is an introductory talk to…
- Explain the foundation constituents of the Windows Azure Platform and the underlying infrastructure.
- Demonstrate the Cloud developer experience in Windows Azure.
- Showcase one of the early Windows Azure adopters revealing the reasons behind their choice, the flexibility that they are now empowered with and the success it has brought to their business.
Questions? You can contact Stephen at stephen.fitzmaurice@microsoft.com
Enterprise guide to building a Data MeshSion Smith
Making Data Mesh simple, Open Source and available to all; without vendor lock-in, without complex tooling and to use an approach centered around ‘specifications’, existing tools and baking in a ‘domain’ model.
Myth Busters II: BI Tools and Data Virtualization are InterchangeableDenodo
Watch Here: https://bit.ly/2NcqU6F
We take on the 2nd myth about data virtualization and it’s one that suggests a BI tool can substitute a data virtualization software.
You might be thinking: If I can have multi-source queries and define a logical model in my reporting tool, why would I need a data virtualization software?
Reporting tools, no doubt important and necessary, focus on the visualization of data and it’s presentation to the business user. Data virtualization is a governed data access layer designed to connect to and provide transparency of all enterprise data.
Yet the myth suggests that these technologies are interchangeable. So we’re going to take it on!
Watch this webinar as we compare and contrast BI tools and data virtualization to draw a final conclusion.
Data Integration for Big Data (OOW 2016, Co-Presented With Oracle)Rittman Analytics
Set of product roadmap + capabilities slides from Oracle Data Integration Product Management, and thoughts on data integration on big data implementations by Mark Rittman (Independent Analyst)
Modern data management using Kappa and streaming architectures, including discussion by EBay's Connie Yang about the Rheos platform and the use of Oracle GoldenGate, Kafka, Flink, etc.
Brief training targeted to middle school aged students who are participating in First Lego League robotics and planning to use a version control tool such as EV3Hub
This is a brief technology introduction to Oracle Stream Analytics, and how to use the platform to develop streaming data pipelines that support a wide variety of industry use cases
GoldenGate and Stream Processing with Special Guest RakutenJeffrey T. Pollock
Oracle OpenWorld roadmap presentation for GoldenGate, stream processing, analytics and big data use cases with special guest presenters from Rakuten Travel.
A modern approach to streaming data integration, event processing with a big data (kappa style) data architecture. Key patterns are discussed with pros/cons of newer approaches and open source technologies. Focus on Oracle and GoldenGate technology. OpenWorld 2018 presentation.
Presentation to discuss major shift in enterprise data management. Describes movement away from older hub and spoke data architecture and towards newer, more modern Kappa data architecture
CON6619 - OpenWorld Presentation. Oracle data integration, big data, data governance, and cloud integration. Replication, ETL, Data Quality, Streaming Big Data, and Data Preparation
Oracle Data Integration overview, vision and roadmap. Covers GoldenGate, Data Integrator (ODI), Data Quality (EDQ), Metadata Management (MM) and Big Data Preparation (BDP)
Strata 2015 presentation from Oracle for Big Data - we are announcing several new big data products including GoldenGate for Big Data, Big Data Discovery, Oracle Big Data SQL and Oracle NoSQL
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Strategies for Successful Data Migration Tools.pptxvarshanayak241
Data migration is a complex but essential task for organizations aiming to modernize their IT infrastructure and leverage new technologies. By understanding common challenges and implementing these strategies, businesses can achieve a successful migration with minimal disruption. Data Migration Tool like Ask On Data play a pivotal role in this journey, offering features that streamline the process, ensure data integrity, and maintain security. With the right approach and tools, organizations can turn the challenge of data migration into an opportunity for growth and innovation.
Modern design is crucial in today's digital environment, and this is especially true for SharePoint intranets. The design of these digital hubs is critical to user engagement and productivity enhancement. They are the cornerstone of internal collaboration and interaction within enterprises.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.