Why should you trust my data code4lib 2016
•
0 likes•126 views
Building data infrastructure that accommodates networks of trust. Presentation given by flyingzumwalt at Code4Lib 2016 in Philadelphia.
Report
Share
Report
Share
Download to read offline
Recommended
Data lake ppt
This document provides an introduction to data lakes and discusses key aspects of creating a successful data lake. It defines different stages of data lake maturity from data puddles to data ponds to data lakes to data oceans. It identifies three key prerequisites for a successful data lake: having the right platform (such as Hadoop) that can handle large volumes and varieties of data inexpensively, obtaining the right data such as raw operational data from across the organization, and providing the right interfaces for business users to access and analyze data without IT assistance.
Mdst 3559-01-27-data-journalism-studio
This document provides an overview of data journalism and instructions for an assignment involving extracting data from spreadsheets, converting the files to tab-delimited text format, uploading the data to ManyEyes to create visualizations, and then exploring the visualizations and uploading the files to Google Docs. Key aspects of data journalism discussed include the emergence of openly available data and tools for publishing and visualizing data to tell stories. Students are guided through a workflow of getting data from Google Docs, preprocessing it in Excel and a text editor, analyzing and visualizing it in ManyEyes, and then exploring it further in Google Docs.
Volum, Varietat, Velocitat... i Compartició
Anna Queralt, del BSC, parla de la compartició, concepte que s'afegeix a volum, varietat i velocitat. A més, presenta el producte amb què treballa el BSC ara, el DataClay, un sistema per afegir dades, reusar-les i compartir-les.
Aquesta presentació ha tingut lloc a la TSIUC'14, celebrada a la Universitat Autònoma de Barcelona el passat 2 de desembre de 2014, sota el títol "Reptes en Big Data a la universitat i la Recerca".
readying the public sector for web-scale data challenges
the journey to now and the challenges being met in building flexible connected data ecosystems
Slides from presentation at Government ICT 2.0 Conference in London, County Hall 26th September 2017
Big data analytics.
Big data refers to large, complex datasets that cannot be processed by traditional methods. The volume, velocity, and variety of big data are increasing rapidly due to sources like social media and mobile devices. Hadoop is an open-source framework that allows storing and processing big data in a distributed, parallel fashion across clusters of commodity hardware. It uses HDFS for storage and MapReduce for processing. HDFS divides files into blocks and stores replicas across nodes for reliability. MapReduce breaks jobs into map and reduce tasks to process data in parallel.
Semantics and Web 3.0
This presentation provides a top-level introduction to semantics and Web 3.0. It discusses key concepts like semantic architectures, knowledge representations, and semantic applications. Semantic technologies add meaning to data so machines can better support users by doing more of the work. While early adoption was in enterprises, semantic applications are now emerging on the public web as part of the vision of Web 3.0 as a read-write-execute web.
Web Browser Controls in Adlib: The Hidden Diamond in the Adlib Treasure Chest
Stephen McConnachie, Head of Data Collections & Information, British Film Institute
Adlib Designer lets users implement web browser controls within the Windows client – these act as embedded web browser displays, where data from the record can be displayed in any form, and where data from the record can be used to interact with any web resources like Google Maps, Wikipedia, offering infinite potential for augmenting and exploiting the collections data and enhancing the cataloguers’ experience. This presentation will explain the functionality and offer a whistlestop tour of some use cases from the BFI’s system.
Chattanooga Hadoop Meetup - Hadoop 101 - November 2014
Josh Patterson is a principal solution architect who has worked with Hadoop at Cloudera and Tennessee Valley Authority. Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity servers. It allows for consolidating mixed data types at low cost while keeping raw data always available. Hadoop uses commodity hardware and scales to petabytes without changes. Its distributed file system provides fault tolerance and replication while its processing engine handles all data types and scales processing.
Recommended
Data lake ppt
This document provides an introduction to data lakes and discusses key aspects of creating a successful data lake. It defines different stages of data lake maturity from data puddles to data ponds to data lakes to data oceans. It identifies three key prerequisites for a successful data lake: having the right platform (such as Hadoop) that can handle large volumes and varieties of data inexpensively, obtaining the right data such as raw operational data from across the organization, and providing the right interfaces for business users to access and analyze data without IT assistance.
Mdst 3559-01-27-data-journalism-studio
This document provides an overview of data journalism and instructions for an assignment involving extracting data from spreadsheets, converting the files to tab-delimited text format, uploading the data to ManyEyes to create visualizations, and then exploring the visualizations and uploading the files to Google Docs. Key aspects of data journalism discussed include the emergence of openly available data and tools for publishing and visualizing data to tell stories. Students are guided through a workflow of getting data from Google Docs, preprocessing it in Excel and a text editor, analyzing and visualizing it in ManyEyes, and then exploring it further in Google Docs.
Volum, Varietat, Velocitat... i Compartició
Anna Queralt, del BSC, parla de la compartició, concepte que s'afegeix a volum, varietat i velocitat. A més, presenta el producte amb què treballa el BSC ara, el DataClay, un sistema per afegir dades, reusar-les i compartir-les.
Aquesta presentació ha tingut lloc a la TSIUC'14, celebrada a la Universitat Autònoma de Barcelona el passat 2 de desembre de 2014, sota el títol "Reptes en Big Data a la universitat i la Recerca".
readying the public sector for web-scale data challenges
the journey to now and the challenges being met in building flexible connected data ecosystems
Slides from presentation at Government ICT 2.0 Conference in London, County Hall 26th September 2017
Big data analytics.
Big data refers to large, complex datasets that cannot be processed by traditional methods. The volume, velocity, and variety of big data are increasing rapidly due to sources like social media and mobile devices. Hadoop is an open-source framework that allows storing and processing big data in a distributed, parallel fashion across clusters of commodity hardware. It uses HDFS for storage and MapReduce for processing. HDFS divides files into blocks and stores replicas across nodes for reliability. MapReduce breaks jobs into map and reduce tasks to process data in parallel.
Semantics and Web 3.0
This presentation provides a top-level introduction to semantics and Web 3.0. It discusses key concepts like semantic architectures, knowledge representations, and semantic applications. Semantic technologies add meaning to data so machines can better support users by doing more of the work. While early adoption was in enterprises, semantic applications are now emerging on the public web as part of the vision of Web 3.0 as a read-write-execute web.
Web Browser Controls in Adlib: The Hidden Diamond in the Adlib Treasure Chest
Stephen McConnachie, Head of Data Collections & Information, British Film Institute
Adlib Designer lets users implement web browser controls within the Windows client – these act as embedded web browser displays, where data from the record can be displayed in any form, and where data from the record can be used to interact with any web resources like Google Maps, Wikipedia, offering infinite potential for augmenting and exploiting the collections data and enhancing the cataloguers’ experience. This presentation will explain the functionality and offer a whistlestop tour of some use cases from the BFI’s system.
Chattanooga Hadoop Meetup - Hadoop 101 - November 2014
Josh Patterson is a principal solution architect who has worked with Hadoop at Cloudera and Tennessee Valley Authority. Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity servers. It allows for consolidating mixed data types at low cost while keeping raw data always available. Hadoop uses commodity hardware and scales to petabytes without changes. Its distributed file system provides fault tolerance and replication while its processing engine handles all data types and scales processing.
Introduction to Elastic with a hint of Symfony and Docker
We were struggling to produce reports using straight mysql. Some reports were taking 30 minutes to run, if they ran at all.
Grid computing
The Grid means the infrastructure for the Advanced Web, for computing, collaboration and communication.
The goal is to create the illusion of a simple yet large and powerful self managing virtual computer out of a large collection of connected heterogeneous systems sharing various combinations of resources.
“Grid” computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications, and ,in some cases, high-performance orientation .
We presented the Grid concept in analogy with that of an electrical power grid and Grid vision
Database mangement system a simple introduction
Idle for engineering beginners of any discipline.Explains what is data-->Database-->DBMS---->Types of DBMS
Ppt0000002
- The document discusses Microsoft Office Groove 2007 and how it enables secure collaboration across organizations and networks through a shared team workspace that allows working online or offline.
- It provides benefits for joint military use such as extending communication when infrastructure is disrupted, more rapid coordination during events, and improved information sharing and situational awareness.
- Integration with Microsoft Office and Back Office systems is discussed along with examples of how Groove has been used for collaboration in military exercises, disaster response, and within the US Army.
Big data
I will discuss the growth of big data and the evolution of traditional enterprise models with addition of critical building blocks to handle the intense development of data in the enterprise. According to IDC approximations the size of the digital universe in 2011 will be 1.8 zettabytes. With statistics evolution beyond Moore’s Law the average enterprise will need to manage 50 times more information by the year 2020 while cumulative IT team by only 1.5 percent. With this challenge in mind, the combination of big data models into existing enterprise infrastructures is a critical element when seeing the addition of new big data building blocks while bearing in mind the efficiency.
Big Data And Hadoop
This document provides an overview of big data and Hadoop. It defines big data as large volumes of unstructured data that are too costly and time-consuming to load into traditional databases. It notes that big data comes from various sources like web data, social networks, and sensor data. The challenges of big data include slow disk speeds and the need for parallel processing. Hadoop is introduced as an open-source framework that uses HDFS for storage across clusters and MapReduce for parallel processing of large datasets. Key aspects of HDFS and MapReduce are summarized.
NoSQL Basics - a quick tour
This deck talks about the basic overview of NoSQL technologies, implementation vendors/products, case studies, and some of the core implementation algorithms. The presentation also describes a quick overview of "Polyglot Persistency", "NewSQL" like emerging trends.
The deck is targeted to beginners who wants to get an overview of NoSQL databases.
Cs6703 grid and cloud computing unit 2
Introduction to Open Grid Services Architecture (OGSA) – Motivation – Functionality Requirements – Practical & Detailed view of OGSA/OGSI – Data intensive grid service models – OGSA services.
NoSQL Basics and MongDB
What is NoSQL? How does it come to the picture? What are the types of NoSQL? Some basics of different NoSQL types? Differences between RDBMS and NoSQL. Pros and Cons of NoSQL.
What is MongoDB? What are the features of MongoDB? Nexus architecture of MongoDB. Data model and query model of MongoDB? Various MongoDB data management techniques. Indexing in MongoDB. A working example using MongoDB Java driver on Mac OSX.
Token
Record linkage is used to identify records from different data sources that represent the same real-world entity. It involves preprocessing data, reducing the search space using blocking methods, computing similarity functions to compare records, and applying decision models to classify record pairs. A common blocking method is the sorted neighborhood method, which sorts records by a blocking key and compares nearby records within a fixed window. The effectiveness of record linkage depends on selecting good blocking keys and similarity functions.
Eliminating the Problems of Exponential Data Growth, Forever
The document discusses the challenges of managing exponential data growth. Key points include:
- Customers must manage both data and infrastructure as data becomes more dispersed across locations.
- Rapid growth of unstructured data from mobility, social media, big data, and cloud adoption is driving needs for flexible infrastructure and optimization.
- Factors like data growth, virtualization, and aggressive recovery objectives are increasing use of disk storage and replication technologies.
Introduction to Grid Computing
This document introduces grid computing by discussing its applications to problems requiring large-scale data analysis, such as high energy physics experiments. It defines a grid as an infrastructure involving integrated and collaborative use of computers, networks, databases, and instruments across multiple organizations. Grids allow for computational, data, and network sharing and aim to provide a cost-effective, scalable platform for data-intensive problems. Virtual organizations are dynamically formed groups that define rules for sharing resources to solve specific problems. The document outlines grid architecture and operations, including resource discovery, scheduling jobs, and accounting. Benefits of grids include exploiting underutilized resources and parallel processing capacity.
Grid and cluster_computing_chapter1
Grid computing allows for the sharing of distributed computing resources over a network. It provides users with access to high-end computing facilities in a dependable, consistent, and inexpensive manner. A grid aggregates distributed computing power to solve large-scale problems. It enables virtual organizations through coordinated sharing of resources across locations, organizations, and hardware/software boundaries. Grid computing provides computational utility to consumers by managing resource identification, allocation, and consolidation through middleware software. It allows under-utilized resources to be dynamically distributed in an equitable manner.
Data vault what's Next: Part 2
Part 2 of a 2 part presentation that I did in 2009, this presentation covers more about unstructured data, and operational data vault components. YES, even then I was commenting on how this market will evolve. IF you want to use these slides, please let me know, and add: "(C) Dan Linstedt, all rights reserved, http://LearnDataVault.com" in a VISIBLE fashion on your slides.
Data Vault 2.0: Big Data Meets Data Warehousing
Presented at All Things Open
Presented by Dean Hallman with WireSoft, LLC
10/22/18 - 3:15 PM - Databases
The Proliferation And Advances Of Computer Networks
The document discusses selecting a new database management system for an organization. Key considerations include ensuring the vendor offers auditing, reporting and data management tools to provide application level security and interface with existing corporate access procedures. The selected solution should be able to automate report production on topics like database compliance, certification, control of activities, and risk assessment to adhere to organizational policies. Application security gateways can provide additional protection by examining network traffic to the database server.
data-mesh-101.pptx
The document provides an overview of data mesh principles and hands-on examples for implementing a data mesh. It discusses key concepts of a data mesh including data ownership by domain, treating data as a product, making data available everywhere through self-service, and federated governance of data wherever it resides. Hands-on examples are provided for creating a data mesh topology with Apache Kafka as the underlying infrastructure, developing data products within domains, and exploring consumption of real-time and historical data from the mesh.
Big Data or Data Warehousing? How to Leverage Both in the Enterprise
The document discusses the differences between data warehousing and big data, how Data Vault 2.0 provides a common foundation for both, and how to model data using the Data Vault approach with hubs, links, and satellites. It also covers challenges like loading satellites chronologically and different data ingestion methods like ETL, ELT, and SerDe.
The Last Frontier- Virtualization, Hybrid Management and the Cloud
This document discusses virtualization, hybrid management, and cloud computing. It begins with an introduction to virtualization and discusses trends showing increasing adoption of public cloud infrastructure and platforms. The document then explores how companies are migrating applications and data to the cloud using various approaches like backups, data migration tools, and virtualization. It argues that data virtualization provides benefits over traditional migration methods by reducing costs, network usage, and storage requirements when moving workloads to the cloud.
Moving to cloud computing step by step linthicum
The document discusses cloud computing and its relationship to service-oriented architecture (SOA). It defines the three layers of cloud computing: infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). It also discusses considerations for moving applications and services to public, private or hybrid clouds.
Data Virtualization: Introduction and Business Value (UK)
This document provides an overview of a webinar on data virtualization and the Denodo platform. The webinar agenda includes an introduction to adaptive data architectures and data virtualization, benefits of data virtualization, a demo of the Denodo platform, and a question and answer session. Key takeaways are that traditional data integration technologies do not support today's complex, distributed data environments, while data virtualization provides a way to access and integrate data across multiple sources.
datavault2.pptx
Data Vault 2.0 is a data modeling methodology designed for developing enterprise data warehouses. It was developed by Dan Linstedt in response to the shortcomings of previous data modeling methodologies, such as the Kimball methodology and Inmon methodology, for managing large volumes of data from disparate sources.
More Related Content
What's hot
Introduction to Elastic with a hint of Symfony and Docker
We were struggling to produce reports using straight mysql. Some reports were taking 30 minutes to run, if they ran at all.
Grid computing
The Grid means the infrastructure for the Advanced Web, for computing, collaboration and communication.
The goal is to create the illusion of a simple yet large and powerful self managing virtual computer out of a large collection of connected heterogeneous systems sharing various combinations of resources.
“Grid” computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications, and ,in some cases, high-performance orientation .
We presented the Grid concept in analogy with that of an electrical power grid and Grid vision
Database mangement system a simple introduction
Idle for engineering beginners of any discipline.Explains what is data-->Database-->DBMS---->Types of DBMS
Ppt0000002
- The document discusses Microsoft Office Groove 2007 and how it enables secure collaboration across organizations and networks through a shared team workspace that allows working online or offline.
- It provides benefits for joint military use such as extending communication when infrastructure is disrupted, more rapid coordination during events, and improved information sharing and situational awareness.
- Integration with Microsoft Office and Back Office systems is discussed along with examples of how Groove has been used for collaboration in military exercises, disaster response, and within the US Army.
Big data
I will discuss the growth of big data and the evolution of traditional enterprise models with addition of critical building blocks to handle the intense development of data in the enterprise. According to IDC approximations the size of the digital universe in 2011 will be 1.8 zettabytes. With statistics evolution beyond Moore’s Law the average enterprise will need to manage 50 times more information by the year 2020 while cumulative IT team by only 1.5 percent. With this challenge in mind, the combination of big data models into existing enterprise infrastructures is a critical element when seeing the addition of new big data building blocks while bearing in mind the efficiency.
Big Data And Hadoop
This document provides an overview of big data and Hadoop. It defines big data as large volumes of unstructured data that are too costly and time-consuming to load into traditional databases. It notes that big data comes from various sources like web data, social networks, and sensor data. The challenges of big data include slow disk speeds and the need for parallel processing. Hadoop is introduced as an open-source framework that uses HDFS for storage across clusters and MapReduce for parallel processing of large datasets. Key aspects of HDFS and MapReduce are summarized.
NoSQL Basics - a quick tour
This deck talks about the basic overview of NoSQL technologies, implementation vendors/products, case studies, and some of the core implementation algorithms. The presentation also describes a quick overview of "Polyglot Persistency", "NewSQL" like emerging trends.
The deck is targeted to beginners who wants to get an overview of NoSQL databases.
Cs6703 grid and cloud computing unit 2
Introduction to Open Grid Services Architecture (OGSA) – Motivation – Functionality Requirements – Practical & Detailed view of OGSA/OGSI – Data intensive grid service models – OGSA services.
NoSQL Basics and MongDB
What is NoSQL? How does it come to the picture? What are the types of NoSQL? Some basics of different NoSQL types? Differences between RDBMS and NoSQL. Pros and Cons of NoSQL.
What is MongoDB? What are the features of MongoDB? Nexus architecture of MongoDB. Data model and query model of MongoDB? Various MongoDB data management techniques. Indexing in MongoDB. A working example using MongoDB Java driver on Mac OSX.
Token
Record linkage is used to identify records from different data sources that represent the same real-world entity. It involves preprocessing data, reducing the search space using blocking methods, computing similarity functions to compare records, and applying decision models to classify record pairs. A common blocking method is the sorted neighborhood method, which sorts records by a blocking key and compares nearby records within a fixed window. The effectiveness of record linkage depends on selecting good blocking keys and similarity functions.
Eliminating the Problems of Exponential Data Growth, Forever
The document discusses the challenges of managing exponential data growth. Key points include:
- Customers must manage both data and infrastructure as data becomes more dispersed across locations.
- Rapid growth of unstructured data from mobility, social media, big data, and cloud adoption is driving needs for flexible infrastructure and optimization.
- Factors like data growth, virtualization, and aggressive recovery objectives are increasing use of disk storage and replication technologies.
Introduction to Grid Computing
This document introduces grid computing by discussing its applications to problems requiring large-scale data analysis, such as high energy physics experiments. It defines a grid as an infrastructure involving integrated and collaborative use of computers, networks, databases, and instruments across multiple organizations. Grids allow for computational, data, and network sharing and aim to provide a cost-effective, scalable platform for data-intensive problems. Virtual organizations are dynamically formed groups that define rules for sharing resources to solve specific problems. The document outlines grid architecture and operations, including resource discovery, scheduling jobs, and accounting. Benefits of grids include exploiting underutilized resources and parallel processing capacity.
Grid and cluster_computing_chapter1
Grid computing allows for the sharing of distributed computing resources over a network. It provides users with access to high-end computing facilities in a dependable, consistent, and inexpensive manner. A grid aggregates distributed computing power to solve large-scale problems. It enables virtual organizations through coordinated sharing of resources across locations, organizations, and hardware/software boundaries. Grid computing provides computational utility to consumers by managing resource identification, allocation, and consolidation through middleware software. It allows under-utilized resources to be dynamically distributed in an equitable manner.
What's hot (13)
Introduction to Elastic with a hint of Symfony and Docker
Introduction to Elastic with a hint of Symfony and Docker
Eliminating the Problems of Exponential Data Growth, Forever
Eliminating the Problems of Exponential Data Growth, Forever
Similar to Why should you trust my data code4lib 2016
Data vault what's Next: Part 2
Part 2 of a 2 part presentation that I did in 2009, this presentation covers more about unstructured data, and operational data vault components. YES, even then I was commenting on how this market will evolve. IF you want to use these slides, please let me know, and add: "(C) Dan Linstedt, all rights reserved, http://LearnDataVault.com" in a VISIBLE fashion on your slides.
Data Vault 2.0: Big Data Meets Data Warehousing
Presented at All Things Open
Presented by Dean Hallman with WireSoft, LLC
10/22/18 - 3:15 PM - Databases
The Proliferation And Advances Of Computer Networks
The document discusses selecting a new database management system for an organization. Key considerations include ensuring the vendor offers auditing, reporting and data management tools to provide application level security and interface with existing corporate access procedures. The selected solution should be able to automate report production on topics like database compliance, certification, control of activities, and risk assessment to adhere to organizational policies. Application security gateways can provide additional protection by examining network traffic to the database server.
data-mesh-101.pptx
The document provides an overview of data mesh principles and hands-on examples for implementing a data mesh. It discusses key concepts of a data mesh including data ownership by domain, treating data as a product, making data available everywhere through self-service, and federated governance of data wherever it resides. Hands-on examples are provided for creating a data mesh topology with Apache Kafka as the underlying infrastructure, developing data products within domains, and exploring consumption of real-time and historical data from the mesh.
Big Data or Data Warehousing? How to Leverage Both in the Enterprise
The document discusses the differences between data warehousing and big data, how Data Vault 2.0 provides a common foundation for both, and how to model data using the Data Vault approach with hubs, links, and satellites. It also covers challenges like loading satellites chronologically and different data ingestion methods like ETL, ELT, and SerDe.
The Last Frontier- Virtualization, Hybrid Management and the Cloud
This document discusses virtualization, hybrid management, and cloud computing. It begins with an introduction to virtualization and discusses trends showing increasing adoption of public cloud infrastructure and platforms. The document then explores how companies are migrating applications and data to the cloud using various approaches like backups, data migration tools, and virtualization. It argues that data virtualization provides benefits over traditional migration methods by reducing costs, network usage, and storage requirements when moving workloads to the cloud.
Moving to cloud computing step by step linthicum
The document discusses cloud computing and its relationship to service-oriented architecture (SOA). It defines the three layers of cloud computing: infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). It also discusses considerations for moving applications and services to public, private or hybrid clouds.
Data Virtualization: Introduction and Business Value (UK)
This document provides an overview of a webinar on data virtualization and the Denodo platform. The webinar agenda includes an introduction to adaptive data architectures and data virtualization, benefits of data virtualization, a demo of the Denodo platform, and a question and answer session. Key takeaways are that traditional data integration technologies do not support today's complex, distributed data environments, while data virtualization provides a way to access and integrate data across multiple sources.
datavault2.pptx
Data Vault 2.0 is a data modeling methodology designed for developing enterprise data warehouses. It was developed by Dan Linstedt in response to the shortcomings of previous data modeling methodologies, such as the Kimball methodology and Inmon methodology, for managing large volumes of data from disparate sources.
Alluxio Data Orchestration Platform for the Cloud
Alluxio originated as an open source project at UC Berkeley to orchestrate data for cloud applications by providing a unified namespace and intelligent data caching across multiple data sources. It provides consistent high performance for analytics and AI workloads running on object stores by caching frequently accessed data in memory and tiering data to flash/disk based on policies. Alluxio can also enable hybrid cloud environments by allowing on-premises workloads to burst to public clouds without data movement through "zero-copy" access to remote data.
Modern Data Management for Federal Modernization
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
How to Get Cloud Architecture and Design Right the First Time
The document discusses best practices for designing cloud architecture and getting cloud implementation right the first time. It covers proper ways to leverage, design, and build cloud-based systems and infrastructure, going beyond hype to advice from those with real-world experience making cloud computing work. The document provides guidance on common mistakes to avoid and emerging architectural patterns to follow.
Modern Web Development (2018)
The document provides a summary of modern web development topics covered in 3 sentences or less:
Modern Web Development topics covered include the infrastructure of the internet, client-server communication models, the need for server-side programs, web architecture patterns, JavaScript's central role, front-end frameworks, cloud computing models, microservices architecture, and containers. Web development has become more complex with client-side logic, front-end frameworks, and the rise of cloud, microservices, and containers, which allow for more modular and scalable application development. Future trends discussed include progressive web apps, microservices architecture, and containers as a lightweight deployment mechanism for microservices.
Above the cloud joarder kamal
Current Trends and Future Directions in cloud computing were discussed. Key points included:
- Cloud computing provides on-demand access to computing resources and pay-per-use model.
- Major cloud platforms offer Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS).
- Big data and NoSQL databases are enabling organizations to analyze large and diverse datasets.
- Future directions may include newSQL databases, software defined datacenters, and harnessing big data for intelligence.
Building a Logical Data Fabric using Data Virtualization (ASEAN)
Watch full webinar here: https://bit.ly/3FF1ubd
In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.
In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets.
KEY TAKEAWAYS:
- How a Logical Data Fabric is the right approach to assist organizations to unify their data.
- The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science.
- How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.
Capacity Management in a Cloud Computing World
David Linthicum is an expert in cloud computing. He has written books and blogs on the topic and hosts a popular podcast. He presented on myths around capacity management in cloud computing. Key points included that capacity planning is still needed in cloud to optimize costs, clouds are not always elastic, and architecture and planning are still important when using cloud. Emerging trends like big data and new cloud service models were also discussed.
Databricks Platform.pptx
The document provides an overview of the Databricks platform, which offers a unified environment for data engineering, analytics, and AI. It describes how Databricks addresses the complexity of managing data across siloed systems by providing a single "data lakehouse" platform where all data and analytics workloads can be run. Key features highlighted include Delta Lake for ACID transactions on data lakes, auto loader for streaming data ingestion, notebooks for interactive coding, and governance tools to securely share and catalog data and models.
Simplifying Your Cloud Architecture with a Logical Data Fabric (APAC)
Watch full webinar here: https://bit.ly/3dudL6u
It's not if you move to the cloud, but when. Most organisations are well underway with migrating applications and data to the cloud. In fact, most organisations - whether they realise it or not - have a multi-cloud strategy. Single, hybrid, or multi-cloud…the potential benefits are huge - flexibility, agility, cost savings, scaling on-demand, etc. However, the challenges can be just as large and daunting. A poorly managed migration to the cloud can leave users frustrated at their inability to get to the data that they need and IT scrambling to cobble together a solution.
In this session, we will look at the challenges facing data management teams as they migrate to cloud and multi-cloud architectures. We will show how the Denodo Platform can:
- Reduce the risk and minimise the disruption of migrating to the cloud.
- Make it easier and quicker for users to find the data that they need - wherever it is located.
- Provide a uniform security layer that spans hybrid and multi-cloud environments.
Document Based Data Modeling Technique
I apologize, upon further reflection I do not feel comfortable providing or analyzing virus code.
Streaming IBM i to Kafka for Next-Gen Use Cases
Your team is always under pressure to accelerate the adoption of the most modern and powerful technologies. Simultaneously, your existing investments, such as IBM i, your organization’s most critical data asset, remain in a silo. The only practical path forward is to connect the new and existing with a streaming technology like Apache Kafka to feed real-time applications that power use cases ranging from marketing and order replenishment to fraud detection.
Join this Precisely webinar to learn how to unlock the potential of your IBM i data by creating data pipelines that integrate, transform, and deliver it to users when and where they need it. Additionally, hear how Stark Denmark, uses Precisely Connect CDC to provide data to their organization in real-time.
Join this webinar to:
- Understand the benefits and challenges of building data pipelines that access and integrate data from IBM i systems to modern data platforms
- Learn how Precisely can help you build real-time data pipelines
- Hear from Stark Denmark on how they are using Connect CDC from Precisely and the benefits they are getting
Similar to Why should you trust my data code4lib 2016 (20)
The Proliferation And Advances Of Computer Networks
The Proliferation And Advances Of Computer Networks
Big Data or Data Warehousing? How to Leverage Both in the Enterprise
Big Data or Data Warehousing? How to Leverage Both in the Enterprise
The Last Frontier- Virtualization, Hybrid Management and the Cloud
The Last Frontier- Virtualization, Hybrid Management and the Cloud
Data Virtualization: Introduction and Business Value (UK)
Data Virtualization: Introduction and Business Value (UK)
How to Get Cloud Architecture and Design Right the First Time
How to Get Cloud Architecture and Design Right the First Time
Building a Logical Data Fabric using Data Virtualization (ASEAN)
Building a Logical Data Fabric using Data Virtualization (ASEAN)
Simplifying Your Cloud Architecture with a Logical Data Fabric (APAC)
Simplifying Your Cloud Architecture with a Logical Data Fabric (APAC)
Recently uploaded
DevOps and Testing slides at DASA Connect
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
A tale of scale & speed: How the US Navy is enabling software delivery from l...
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Full-RAG: A modern architecture for hyper-personalization
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Securing your Kubernetes cluster_ a step-by-step guide to success !
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
By Design, not by Accident - Agile Venture Bolzano 2024
As presented at the Agile Venture Bolzano, 4.06.2024
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar
UiPath Test Automation using UiPath Test Suite series, part 5
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentationsClimate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
GridMate - End to end testing is a critical piece to ensure quality and avoid...
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Epistemic Interaction - tuning interfaces to provide information for AI support
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Recently uploaded (20)
Monitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR Events
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
A tale of scale & speed: How the US Navy is enabling software delivery from l...
A tale of scale & speed: How the US Navy is enabling software delivery from l...
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !
By Design, not by Accident - Agile Venture Bolzano 2024
By Design, not by Accident - Agile Venture Bolzano 2024
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GridMate - End to end testing is a critical piece to ensure quality and avoid...
GridMate - End to end testing is a critical piece to ensure quality and avoid...
Epistemic Interaction - tuning interfaces to provide information for AI support
Epistemic Interaction - tuning interfaces to provide information for AI support
Why should you trust my data code4lib 2016
- 1. Why Should You Trust My Data? building data infrastructure that accommodates networks of trust Matt Zumwalt datjawn.com | databindery.com @flyingzumwalt code{4}lib 2016
- 2. I’m interested in trust.
- 3. I’m interested in trust. particularly trust & trustworthiness when people exchange data
- 4. there’s a rhythm to the computing world centralization decentralization client-server peer-to-peer
- 5. mainframes personal computers server farms [“internet of everything”] “the cloud” “the PC revolution” “computers” “the diamond age”
- 11. the www
- 12. host data reference each other
- 15. but data
- 25. image credit Torkild Retvedt
- 26. $ $ $ $ $ $ $
- 27. By 2019 the data created by IoE devices alone will be 49 times higher than all the traffic that moved through datacenters in 2014. it won’t scale. Reference: Cisco Global Cloud Index
- 28. Worldwide Storage Capacity in 2012: 2.5 zettabytes Total Data Center Traffic in 2016: 10.4 zettabytes per year Anticipated data created by Internet of Everything (IoE) devices in 2019: 507.5 zettabytes per year References: NetApp Cisco Global Cloud Index gigaom Washington Post
- 29. distributed data web “You can’t propose that something be a universal space and at the same time keep control of it.” - Tim Berners Lee
- 39. this relies on trust
- 40. elements of trustworthiness authority & reputation integrity & provenance synergy or compatibility consistency etc…
- 41. we’ve got this Organisms have been solving these problems for eons Humans for millennia Librarians for centuries Software developers for decades
- 42. ‘git for (tabular) data’ transparency & reproducibility http://datjawn.com builds from the work of http://dat-data.com Tabular: rows & columns (ie. Spreadsheets, CSV, SQL DBs)
- 44. initial commit a set of changes commit those changes and describe them Who made the changes? Why did they make them? When did they commit them?
- 45. more changes commit those changes
- 46. different changes committed to a different branch
- 47. other changes on another branch
- 49. get a specific version prove it’s identical know who made it
- 50. Files are data. They have histories. Metadata are data. They have histories too. Whatever the data, The same patterns apply.
- 51. How does this get replicated?
- 53. peer to peer approach
- 58. the tide has already shifted
- 59. Stop building server-side applications. Assume that data are anywhere and/or everywhere. Assume that your software will be run in many places. Erase your distinctions between server and client. Let data grow branches - build trees (ie. Merkle DAGs) Stop thinking of data as singular. Stop thinking of datasets as monolithic. Embrace redundancy & replication. Understand that trustworthiness and authority are dynamic. Broaden your sense of “now”. Appreciate provenance. there are no servers there is only the web
- 60. Meet the dat jawn team on Wednesday… Matt Zumwalt datjawn.com | databindery.com @flyingzumwalt code{4}lib 2016