A presentation from the Big Data Week conference in 2016 that looks how Worldpay, a major payments provider, deployed a secure Hadoop cluster in order to meet business requirements
Data Works Berlin 2018 - Worldpay - PCI ComplianceDavid Walker
A presentation from the Data Works conference in 2018 that looks how Worldpay, a major payments provider, deployed a secure Hadoop cluster in order to meet business requirements and in the process became on e of the few fully certified PCI compliance clusters in the world
Data Works Summit Munich 2017 - Worldpay - Multi Tenancy ClustersDavid Walker
A presentation from the Data Works Summit conference in 2017 that looks how Worldpay, a major payments provider, deployed a secure Hadoop cluster to support multiple business cases in a multi-tenancy cluster.
This document discusses how data is structured and modeled in databases and data warehouses. It introduces concepts like left-to-right entity relationship diagrams and data model depth. It examines how characteristics like model depth, data volumes, and complexity affect areas like reporting structures, data warehouse design, ETL processes, data quality, and query performance. Understanding these characteristics helps reduce their negative impacts and lower project costs.
Data warehousing change in a challenging environmentDavid Walker
This white paper discusses the challenges of managing changes in a data warehousing environment. It describes a typical data warehouse architecture with source systems feeding data into a data warehouse and then into data marts or cubes. It also outlines the common processes involved like development, operations and data quality processes. The paper then discusses two major challenges - configuration/change management as there are frequent changes from source systems, applications and technologies that impact the data warehouse. The other challenge is managing and improving data quality as issues from source systems are often replicated in the data warehouse.
Openworld04 - Information Delivery - The Change In Data Management At Network...David Walker
Network Rail implemented a new information delivery strategy using Oracle technologies like the Balanced Scorecard, Discoverer, and Portal. They developed executive scorecards quickly for mandated KPIs and then additional scorecards. Data comes from various sources into staging areas and warehouses accessible with Discoverer. A portal provides integrated access. Applications replace Excel/Access and improve data quality. The approach involves a small agile team and spreading solutions across the business.
Storage Characteristics Of Call Data Records In Column Store DatabasesDavid Walker
This document summarizes the storage characteristics of call data records (CDRs) in column store databases. It discusses what CDRs are, what a column store database is, and how efficient column stores are for storing CDR and similar machine-generated data. It provides details on the structure and content of sample CDR data, how the data was loaded into a Sybase IQ column store database for testing purposes, and the results in terms of storage characteristics and what would be needed for a production environment.
IOUG93 - Technical Architecture for the Data Warehouse - PresentationDavid Walker
The document outlines a technical architecture for implementing a data warehouse. It discusses business analysis, database schema design, project management, data acquisition, building a transaction repository, data aggregation, data marts, metadata and security, middleware and presentation layers. The goal is to help users find the information they need from the data warehouse. Contact information is provided at the end.
The document discusses six governance processes for data and business intelligence: data lifecycle, data models, data quality, data security, data warehousing, and metadata. For each process, it provides an overview of why governance is important in that area, and what the governance process will do to manage issues and ensure requirements are met. The governance processes aim to balance various factors, control changes, and provide oversight and accountability for data management.
Data Works Berlin 2018 - Worldpay - PCI ComplianceDavid Walker
A presentation from the Data Works conference in 2018 that looks how Worldpay, a major payments provider, deployed a secure Hadoop cluster in order to meet business requirements and in the process became on e of the few fully certified PCI compliance clusters in the world
Data Works Summit Munich 2017 - Worldpay - Multi Tenancy ClustersDavid Walker
A presentation from the Data Works Summit conference in 2017 that looks how Worldpay, a major payments provider, deployed a secure Hadoop cluster to support multiple business cases in a multi-tenancy cluster.
This document discusses how data is structured and modeled in databases and data warehouses. It introduces concepts like left-to-right entity relationship diagrams and data model depth. It examines how characteristics like model depth, data volumes, and complexity affect areas like reporting structures, data warehouse design, ETL processes, data quality, and query performance. Understanding these characteristics helps reduce their negative impacts and lower project costs.
Data warehousing change in a challenging environmentDavid Walker
This white paper discusses the challenges of managing changes in a data warehousing environment. It describes a typical data warehouse architecture with source systems feeding data into a data warehouse and then into data marts or cubes. It also outlines the common processes involved like development, operations and data quality processes. The paper then discusses two major challenges - configuration/change management as there are frequent changes from source systems, applications and technologies that impact the data warehouse. The other challenge is managing and improving data quality as issues from source systems are often replicated in the data warehouse.
Openworld04 - Information Delivery - The Change In Data Management At Network...David Walker
Network Rail implemented a new information delivery strategy using Oracle technologies like the Balanced Scorecard, Discoverer, and Portal. They developed executive scorecards quickly for mandated KPIs and then additional scorecards. Data comes from various sources into staging areas and warehouses accessible with Discoverer. A portal provides integrated access. Applications replace Excel/Access and improve data quality. The approach involves a small agile team and spreading solutions across the business.
Storage Characteristics Of Call Data Records In Column Store DatabasesDavid Walker
This document summarizes the storage characteristics of call data records (CDRs) in column store databases. It discusses what CDRs are, what a column store database is, and how efficient column stores are for storing CDR and similar machine-generated data. It provides details on the structure and content of sample CDR data, how the data was loaded into a Sybase IQ column store database for testing purposes, and the results in terms of storage characteristics and what would be needed for a production environment.
IOUG93 - Technical Architecture for the Data Warehouse - PresentationDavid Walker
The document outlines a technical architecture for implementing a data warehouse. It discusses business analysis, database schema design, project management, data acquisition, building a transaction repository, data aggregation, data marts, metadata and security, middleware and presentation layers. The goal is to help users find the information they need from the data warehouse. Contact information is provided at the end.
The document discusses six governance processes for data and business intelligence: data lifecycle, data models, data quality, data security, data warehousing, and metadata. For each process, it provides an overview of why governance is important in that area, and what the governance process will do to manage issues and ensure requirements are met. The governance processes aim to balance various factors, control changes, and provide oversight and accountability for data management.
Oracle BI06 From Volume To Value - PresentationDavid Walker
The document discusses challenges with a European mobile telco's data warehouse that contains over 150 billion call detail records. It takes too long to get answers from the data warehouse and it is underutilized. The document recommends establishing quick service teams, performing data profiling and cleansing, integrating the data warehouse into business processes, using business information portals, and RSS feeds to address engagement, user, and technical issues. This will help users get timely, accurate information and increase adoption of the data warehouse.
How Real TIme Data Changes the Data Warehousemark madsen
Surveys show a growing demand for more up-to-date data in our BI environments. To meet these needs requires changing from a strict reliance on nightly batch-style ETL to other methods. What is often ignored is how this affects the data warehouse. This shift introduces new technology and methods, which means the warehouse must support new types of workloads.
• Methods and tools for processing up-to-date data
• New requirements for your data warehouse database or platform
• What to look for as you address these requirements
These are the slides from my talk at Data Day Texas 2016 (#ddtx16).
The world of data warehousing has changed! With the advent of Big Data, Streaming Data, IoT, and The Cloud, what is a modern data management professional to do? It may seem to be a very different world with different concepts, terms, and techniques. Or is it? Lots of people still talk about having a data warehouse or several data marts across their organization. But what does that really mean today in 2016? How about the Corporate Information Factory (CIF), the Data Vault, an Operational Data Store (ODS), or just star schemas? Where do they fit now (or do they)? And now we have the Extended Data Warehouse (XDW) as well. How do all these things help us bring value and data-based decisions to our organizations? Where do Big Data and the Cloud fit? Is there a coherent architecture we can define? This talk will endeavor to cut through the hype and the buzzword bingo to help you figure out what part of this is helpful. I will discuss what I have seen in the real world (working and not working!) and a bit of where I think we are going and need to go in 2016 and beyond.
Are You Killing the Benefits of Your Data Lake?Denodo
Watch the full webinar on-demand here: https://goo.gl/RL1ZSa
Data lakes are centralized data repositories. Data needed by data scientists is physically copied to a data lake which serves as a one storage environment. This way, data scientists can access all the data from only one entry point – a one-stop shop to get the right data. However, such an approach is not always feasible for all the data and limits it’s use to solely data scientists, making it a single-purpose system.
So, what’s the solution?
A multi-purpose data lake allows a broader and deeper use of the data lake without minimizing the potential value for data science and without making it an inflexible environment
Attend this session to learn:
• Disadvantages and limitations that are weakening or even killing the potential benefits of a data lake.
• Why a multi-purpose data lake is essential in building a universal data delivery system.
• How to build a logical multi-purpose data lake using data virtualization.
Do not miss this opportunity to make your data lake project successful and beneficial.
SQL Azure Database is a cloud database service from Microsoft. SQL Azure provides web-facing database functionality as a utility service. Cloud-based database solutions such as SQL Azure can provide many benefits, including rapid provisioning, cost-effective scalability, high availability, and reduced management overhead. This paper provides an overview on some scale out strategies, challenges with scaling out on-premise and how you can benefit with scaling out with SQL Azure.
Big Data comes from a variety of sources as human activities online generate vast amounts of data every day through intentional, accidental, and unknown means. This includes activities on social media, sensors, logs, and more. Content delivery networks (CDNs) can help distribute big data by caching content on servers located closer to users. While pushing content to CDNs offloads work from origin servers and improves performance, it also segments users and requires replication strategies to maintain consistency. Techniques include pre-computing static content from dynamic sources, pushing searches and other functions to CDNs, and experimenting with different cache models. Overall, CDNs can be an effective way to distribute big data but also introduce more complexity and dependence on the CDN
Govern and Protect Your End User InformationDenodo
Watch this Fast Data Strategy session with speakers Clinton Cohagan, Chief Enterprise Data Architect, Lawrence Livermore National Lab & Nageswar Cherukupalli, Vice President & Group Manager, Infosys here: https://buff.ly/2k8f8M5
In its recent report “Predictions 2018: A year of reckoning”, Forrester predicts that 80% of firms affected by GDPR will not comply with the regulation by May 2018. Of those noncompliant firms, 50% will intentionally not comply.
Compliance doesn’t have to be this difficult! What if you have an opportunity to facilitate compliance with a mature technology and significant cost reduction? Data virtualization is a mature, cost-effective technology that enables privacy by design to facilitate compliance.
Attend this session to learn:
• How data virtualization provides a compliance foundation with data catalog, auditing, and data security.
• How you can enable single enterprise-wide data access layer with guardrails.
• Why data virtualization is a must-have capability for compliance use cases.
• How Denodo’s customers have facilitated compliance.
Wallchart - Data Warehouse Documentation RoadmapDavid Walker
This document outlines the key components and processes involved in planning, designing, building, implementing and managing a data warehouse architecture. It includes sections on business requirements, data requirements, technical architecture, data modeling, ETL processes, testing, implementation, project management and documentation. The document provides a roadmap to guide an organization through each stage of developing an enterprise data warehouse.
A strong relationship with the founder
of Data Vault for over 3 years now.
Supporting your business with 40+
certified consultants.
Incorporated as the preferred
Enterprise Data Warehouse modelling
paradigm in the Logica BI Framework.
Satisfied customers in many countries
and industry sectors
Ten Pillars of World Class Data VirtualizationDenodo
This presentation describes how to achieve a successful and mature enterprise data virtualization solution. You will learn the key attributes to look for in an enterprise DV platform, the journey to maturity from an implementation perspective and how a solution can impact your fast data-driven business outcomes.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/tHWXuO.
Data Science Operationalization: The Journey of Enterprise AIDenodo
Watch full webinar here: https://bit.ly/3kVmYJl
As we move into a world driven by AI initiatives, we find ourselves facing new and diverse challenges when it comes to operationalization. Creating a solution and putting it into practice, is certainly not the same. The challenges span various organizational and data facades. In many instances, the data scientists may be working in silos and connecting to the live data may not always be possible. But how does one guarantee their developed model in a silo is still relevant to live data? How can we manage the data flow and data access across the entire AI operationalization cycle?
Watch on-demand to explore:
- The journey and challenges of the Data Scientist
- How Denodo data virtualization with data movement streamlines operationalization
- The best practices and techniques when dealing with siloed data
- How customers have used data virtualization in their data science initiatives
How Data Virtualization Puts Machine Learning into Production (APAC)Denodo
Watch full webinar here: https://bit.ly/3mJJ4w9
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spend most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Attend this session to learn how companies can use data virtualization to:
- Create a logical architecture to make all enterprise data available for advanced analytics exercise
- Accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- Integrate popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc
Implementing Data Virtualization for Data Warehouses and Master Data Manageme...Denodo
The ongoing evolution of business requirements and growth of data volumes continue to put added challenges on existing DW and MDM implementations. Challenges that in many cases cannot be met. Data Virtualization compliments existing DW, MDM and other architectures and business initiatives, providing the agility and flexibility - at a lower cost – for the enablement of Virtual MDM, self-service BI, operational BI, rapid prototyping and real-time analytics.
More information and FREE registrations for this webinar: http://goo.gl/asYztF
Landing page for the entire Packed Lunch webinar series: http://goo.gl/NATMHw
Attend & get unique insights into:
How Data Virtualization can provide a simple and low cost alternative to traditional DW and MDM solutions
How Data Virtualization can enhance and extend existing DW or MDM solutions to provide a more agile data integration architecture
Case studies that demonstrate how Data Virtualization has increased agility to meet complex information needs
Data Lakes - The Key to a Scalable Data ArchitectureZaloni
Data lakes are central to modern data architectures. They can store all types of raw data, create refined datasets for various use cases, and provide shorter time-to-insight with proper management and governance. The document discusses how a data lake reference architecture can include landing, raw, refined, and trusted zones to enable analytics while governing data. It also outlines considerations for implementing a scalable, secure, and governed data lake platform.
Analyst View of Data Virtualization: Conversations with Boulder Business Inte...Denodo
In this presentation, executives from Denodo preview the new Denodo Platform 6.0 release that delivers Dynamic Query Optimizer, cloud offering on Amazon Web Services, and self-service data discovery and search. Over 30 analysts, led by Claudia Imhoff, provide input on strategic direction and benefits of Denodo 6.0 to the data virtualization and the broader data integration market.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/DR6r3m.
Data Governance, Compliance and Security in Hadoop with ClouderaCaserta
The document discusses data governance, compliance and security in Hadoop. It provides an agenda for an event on this topic, including presentations from Joe Caserta of Caserta Concepts on data governance in big data, and Patrick Angeles of Cloudera on using Cloudera for data governance in Hadoop. The document also includes background information on Caserta Concepts and their expertise in data warehousing, business intelligence and big data analytics.
In this document, we will present a very brief introduction to BigData (what is BigData?), Hadoop (how does Hadoop fits the picture?) and Cloudera Hadoop (what is the difference between Cloudera Hadoop and regular Hadoop?).
Please note that this document is for Hadoop beginners looking for a place to start.
SAP Analytics Cloud: Haben Sie schon alle Datenquellen im Live-Zugriff?Denodo
Watch full webinar here: https://bit.ly/3hfEO6d
Die SAP Analytics Cloud (kurz "SAC" genannt) ist ein Service in der Cloud, der umfangreiche Analysefunktionen für Benutzer in einem Produkt bereit stellt. Wie immer bei der SAP ist auch die SAC technologisch gut integriert in die Welt der SAP Systeme.
Doch die Daten, die Unternehmen heutzutage analysieren möchten, befinden sich sehr häufig in den unterschiedlichsten Datenquellen: In relationalen Datenbanken, in Data Lakes, in Webservices, in Dateien, in NoSQL Datenbanken,... Und so stellt sich zwangsläufig die Frage, wie Sie aus der SAC heraus alle Daten konnektieren, transformieren und kombinieren können. Und das möglichst live, d.h. mit Abfragen auf Echtzeit-Daten! Hier kommt die Datenvirtualisierung ins Spiel: Sie bietet Anwendungen (so auch der SAC) einen einheitlichen, integrierten und performanten Zugriff auf SAP Daten und non-SAP Daten.
Erfahren Sie in diesem Webcast:
- Wie die Datenvirtualisierung funktioniert (in a Nutshell)
- Wie Sie aus der SAC heraus auf alle ihre Daten in Echtzeit zugreifen können ("Live Data Connection" genannt)
- Wie die Datenvirtualisierung die Performance auch für Abfragen auf grossen Datenmengen optimiert
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
Not Just a necessary evil, it’s good for business: implementing PCI DSS contr...DataWorks Summit
For firms in the financial industry, especially within regulated organizations such as credit card processors and banks, PCI DSS compliance has become a business and operational necessity. Although the blueprint of a PCI-compliant architecture varies from organization to organization, the mixture of modern Hadoop-based data lakes and legacy systems are a common theme.
In this talk, we will discuss recent updates to PCI DSS and how significant portions of PCI DSS compliance controls can be achieved using open source Hadoop security stack and technologies for the Hadoop ecosystem. We will provide a broad overview of implementing key aspects of PCI DSS standards at WorldPay such as encryption management, data protection with anonymization, separation of duties, and deployment considerations regarding securing the Hadoop clusters at the network layer from a practitioner’s perspective. The talk will provide patterns and practices map current Hadoop security capabilities to security controls that a PCI-compliant environment requires.
Speaker
David Walker, Enterprise Data Platform Programme Director, Worldpay
Srikanth Venkat, Senior Director Product Management, Hortonworks
Worldpay processes billions of transactions annually and stores vast amounts of transaction and customer data. In 2015, Worldpay committed to building a new enterprise data platform on Hadoop to provide analytics, reporting, and machine learning capabilities. The platform uses a multi-tenancy model with different "tenancy types" like data warehousing, decision services, APIs, and technical insights. Each tenancy type has its own components and services. Worldpay's platform currently has live implementations for data warehousing and is developing multiple decision services, with a goal of supporting tens of services within two years.
Oracle BI06 From Volume To Value - PresentationDavid Walker
The document discusses challenges with a European mobile telco's data warehouse that contains over 150 billion call detail records. It takes too long to get answers from the data warehouse and it is underutilized. The document recommends establishing quick service teams, performing data profiling and cleansing, integrating the data warehouse into business processes, using business information portals, and RSS feeds to address engagement, user, and technical issues. This will help users get timely, accurate information and increase adoption of the data warehouse.
How Real TIme Data Changes the Data Warehousemark madsen
Surveys show a growing demand for more up-to-date data in our BI environments. To meet these needs requires changing from a strict reliance on nightly batch-style ETL to other methods. What is often ignored is how this affects the data warehouse. This shift introduces new technology and methods, which means the warehouse must support new types of workloads.
• Methods and tools for processing up-to-date data
• New requirements for your data warehouse database or platform
• What to look for as you address these requirements
These are the slides from my talk at Data Day Texas 2016 (#ddtx16).
The world of data warehousing has changed! With the advent of Big Data, Streaming Data, IoT, and The Cloud, what is a modern data management professional to do? It may seem to be a very different world with different concepts, terms, and techniques. Or is it? Lots of people still talk about having a data warehouse or several data marts across their organization. But what does that really mean today in 2016? How about the Corporate Information Factory (CIF), the Data Vault, an Operational Data Store (ODS), or just star schemas? Where do they fit now (or do they)? And now we have the Extended Data Warehouse (XDW) as well. How do all these things help us bring value and data-based decisions to our organizations? Where do Big Data and the Cloud fit? Is there a coherent architecture we can define? This talk will endeavor to cut through the hype and the buzzword bingo to help you figure out what part of this is helpful. I will discuss what I have seen in the real world (working and not working!) and a bit of where I think we are going and need to go in 2016 and beyond.
Are You Killing the Benefits of Your Data Lake?Denodo
Watch the full webinar on-demand here: https://goo.gl/RL1ZSa
Data lakes are centralized data repositories. Data needed by data scientists is physically copied to a data lake which serves as a one storage environment. This way, data scientists can access all the data from only one entry point – a one-stop shop to get the right data. However, such an approach is not always feasible for all the data and limits it’s use to solely data scientists, making it a single-purpose system.
So, what’s the solution?
A multi-purpose data lake allows a broader and deeper use of the data lake without minimizing the potential value for data science and without making it an inflexible environment
Attend this session to learn:
• Disadvantages and limitations that are weakening or even killing the potential benefits of a data lake.
• Why a multi-purpose data lake is essential in building a universal data delivery system.
• How to build a logical multi-purpose data lake using data virtualization.
Do not miss this opportunity to make your data lake project successful and beneficial.
SQL Azure Database is a cloud database service from Microsoft. SQL Azure provides web-facing database functionality as a utility service. Cloud-based database solutions such as SQL Azure can provide many benefits, including rapid provisioning, cost-effective scalability, high availability, and reduced management overhead. This paper provides an overview on some scale out strategies, challenges with scaling out on-premise and how you can benefit with scaling out with SQL Azure.
Big Data comes from a variety of sources as human activities online generate vast amounts of data every day through intentional, accidental, and unknown means. This includes activities on social media, sensors, logs, and more. Content delivery networks (CDNs) can help distribute big data by caching content on servers located closer to users. While pushing content to CDNs offloads work from origin servers and improves performance, it also segments users and requires replication strategies to maintain consistency. Techniques include pre-computing static content from dynamic sources, pushing searches and other functions to CDNs, and experimenting with different cache models. Overall, CDNs can be an effective way to distribute big data but also introduce more complexity and dependence on the CDN
Govern and Protect Your End User InformationDenodo
Watch this Fast Data Strategy session with speakers Clinton Cohagan, Chief Enterprise Data Architect, Lawrence Livermore National Lab & Nageswar Cherukupalli, Vice President & Group Manager, Infosys here: https://buff.ly/2k8f8M5
In its recent report “Predictions 2018: A year of reckoning”, Forrester predicts that 80% of firms affected by GDPR will not comply with the regulation by May 2018. Of those noncompliant firms, 50% will intentionally not comply.
Compliance doesn’t have to be this difficult! What if you have an opportunity to facilitate compliance with a mature technology and significant cost reduction? Data virtualization is a mature, cost-effective technology that enables privacy by design to facilitate compliance.
Attend this session to learn:
• How data virtualization provides a compliance foundation with data catalog, auditing, and data security.
• How you can enable single enterprise-wide data access layer with guardrails.
• Why data virtualization is a must-have capability for compliance use cases.
• How Denodo’s customers have facilitated compliance.
Wallchart - Data Warehouse Documentation RoadmapDavid Walker
This document outlines the key components and processes involved in planning, designing, building, implementing and managing a data warehouse architecture. It includes sections on business requirements, data requirements, technical architecture, data modeling, ETL processes, testing, implementation, project management and documentation. The document provides a roadmap to guide an organization through each stage of developing an enterprise data warehouse.
A strong relationship with the founder
of Data Vault for over 3 years now.
Supporting your business with 40+
certified consultants.
Incorporated as the preferred
Enterprise Data Warehouse modelling
paradigm in the Logica BI Framework.
Satisfied customers in many countries
and industry sectors
Ten Pillars of World Class Data VirtualizationDenodo
This presentation describes how to achieve a successful and mature enterprise data virtualization solution. You will learn the key attributes to look for in an enterprise DV platform, the journey to maturity from an implementation perspective and how a solution can impact your fast data-driven business outcomes.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/tHWXuO.
Data Science Operationalization: The Journey of Enterprise AIDenodo
Watch full webinar here: https://bit.ly/3kVmYJl
As we move into a world driven by AI initiatives, we find ourselves facing new and diverse challenges when it comes to operationalization. Creating a solution and putting it into practice, is certainly not the same. The challenges span various organizational and data facades. In many instances, the data scientists may be working in silos and connecting to the live data may not always be possible. But how does one guarantee their developed model in a silo is still relevant to live data? How can we manage the data flow and data access across the entire AI operationalization cycle?
Watch on-demand to explore:
- The journey and challenges of the Data Scientist
- How Denodo data virtualization with data movement streamlines operationalization
- The best practices and techniques when dealing with siloed data
- How customers have used data virtualization in their data science initiatives
How Data Virtualization Puts Machine Learning into Production (APAC)Denodo
Watch full webinar here: https://bit.ly/3mJJ4w9
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spend most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Attend this session to learn how companies can use data virtualization to:
- Create a logical architecture to make all enterprise data available for advanced analytics exercise
- Accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- Integrate popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc
Implementing Data Virtualization for Data Warehouses and Master Data Manageme...Denodo
The ongoing evolution of business requirements and growth of data volumes continue to put added challenges on existing DW and MDM implementations. Challenges that in many cases cannot be met. Data Virtualization compliments existing DW, MDM and other architectures and business initiatives, providing the agility and flexibility - at a lower cost – for the enablement of Virtual MDM, self-service BI, operational BI, rapid prototyping and real-time analytics.
More information and FREE registrations for this webinar: http://goo.gl/asYztF
Landing page for the entire Packed Lunch webinar series: http://goo.gl/NATMHw
Attend & get unique insights into:
How Data Virtualization can provide a simple and low cost alternative to traditional DW and MDM solutions
How Data Virtualization can enhance and extend existing DW or MDM solutions to provide a more agile data integration architecture
Case studies that demonstrate how Data Virtualization has increased agility to meet complex information needs
Data Lakes - The Key to a Scalable Data ArchitectureZaloni
Data lakes are central to modern data architectures. They can store all types of raw data, create refined datasets for various use cases, and provide shorter time-to-insight with proper management and governance. The document discusses how a data lake reference architecture can include landing, raw, refined, and trusted zones to enable analytics while governing data. It also outlines considerations for implementing a scalable, secure, and governed data lake platform.
Analyst View of Data Virtualization: Conversations with Boulder Business Inte...Denodo
In this presentation, executives from Denodo preview the new Denodo Platform 6.0 release that delivers Dynamic Query Optimizer, cloud offering on Amazon Web Services, and self-service data discovery and search. Over 30 analysts, led by Claudia Imhoff, provide input on strategic direction and benefits of Denodo 6.0 to the data virtualization and the broader data integration market.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/DR6r3m.
Data Governance, Compliance and Security in Hadoop with ClouderaCaserta
The document discusses data governance, compliance and security in Hadoop. It provides an agenda for an event on this topic, including presentations from Joe Caserta of Caserta Concepts on data governance in big data, and Patrick Angeles of Cloudera on using Cloudera for data governance in Hadoop. The document also includes background information on Caserta Concepts and their expertise in data warehousing, business intelligence and big data analytics.
In this document, we will present a very brief introduction to BigData (what is BigData?), Hadoop (how does Hadoop fits the picture?) and Cloudera Hadoop (what is the difference between Cloudera Hadoop and regular Hadoop?).
Please note that this document is for Hadoop beginners looking for a place to start.
SAP Analytics Cloud: Haben Sie schon alle Datenquellen im Live-Zugriff?Denodo
Watch full webinar here: https://bit.ly/3hfEO6d
Die SAP Analytics Cloud (kurz "SAC" genannt) ist ein Service in der Cloud, der umfangreiche Analysefunktionen für Benutzer in einem Produkt bereit stellt. Wie immer bei der SAP ist auch die SAC technologisch gut integriert in die Welt der SAP Systeme.
Doch die Daten, die Unternehmen heutzutage analysieren möchten, befinden sich sehr häufig in den unterschiedlichsten Datenquellen: In relationalen Datenbanken, in Data Lakes, in Webservices, in Dateien, in NoSQL Datenbanken,... Und so stellt sich zwangsläufig die Frage, wie Sie aus der SAC heraus alle Daten konnektieren, transformieren und kombinieren können. Und das möglichst live, d.h. mit Abfragen auf Echtzeit-Daten! Hier kommt die Datenvirtualisierung ins Spiel: Sie bietet Anwendungen (so auch der SAC) einen einheitlichen, integrierten und performanten Zugriff auf SAP Daten und non-SAP Daten.
Erfahren Sie in diesem Webcast:
- Wie die Datenvirtualisierung funktioniert (in a Nutshell)
- Wie Sie aus der SAC heraus auf alle ihre Daten in Echtzeit zugreifen können ("Live Data Connection" genannt)
- Wie die Datenvirtualisierung die Performance auch für Abfragen auf grossen Datenmengen optimiert
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
Not Just a necessary evil, it’s good for business: implementing PCI DSS contr...DataWorks Summit
For firms in the financial industry, especially within regulated organizations such as credit card processors and banks, PCI DSS compliance has become a business and operational necessity. Although the blueprint of a PCI-compliant architecture varies from organization to organization, the mixture of modern Hadoop-based data lakes and legacy systems are a common theme.
In this talk, we will discuss recent updates to PCI DSS and how significant portions of PCI DSS compliance controls can be achieved using open source Hadoop security stack and technologies for the Hadoop ecosystem. We will provide a broad overview of implementing key aspects of PCI DSS standards at WorldPay such as encryption management, data protection with anonymization, separation of duties, and deployment considerations regarding securing the Hadoop clusters at the network layer from a practitioner’s perspective. The talk will provide patterns and practices map current Hadoop security capabilities to security controls that a PCI-compliant environment requires.
Speaker
David Walker, Enterprise Data Platform Programme Director, Worldpay
Srikanth Venkat, Senior Director Product Management, Hortonworks
Worldpay processes billions of transactions annually and stores vast amounts of transaction and customer data. In 2015, Worldpay committed to building a new enterprise data platform on Hadoop to provide analytics, reporting, and machine learning capabilities. The platform uses a multi-tenancy model with different "tenancy types" like data warehousing, decision services, APIs, and technical insights. Each tenancy type has its own components and services. Worldpay's platform currently has live implementations for data warehousing and is developing multiple decision services, with a goal of supporting tens of services within two years.
Don’t Get Caught in a PCI Pickle: Meet Compliance and Protect Payment Card Da...DataStax
Data security is an absolute requirement for any organization – large or small – that handles debit, credit and pre-paid cards. But navigating, understanding and complying with PCI-DSS (Payment Card Industry – Data Security Standards) regulations can be tough. In this webinar, we’ll examine the guidelines for securing payment card data and show you how a combined solution from DataStax and Gazzang can put you on course for compliance.
Will Your Cloud Be Compliant? OpenStack SecurityScott Carlson
This document discusses cloud compliance and OpenStack security. It provides an overview of common compliance standards like PCI DSS and explains that while standards are generic, controls are similar across standards. The document outlines typical enterprise ecosystems and responsibilities in cloud environments. It also summarizes OpenStack security guidelines and provides examples of how to design private clouds for compliance, including configuring infrastructure, networking, and handling data, based on PayPal's methodology.
Rubrik offers a software-defined data management platform that can help organizations accelerate their GDPR compliance efforts. The platform provides centralized management of data across on-premises, edge, and cloud environments. It employs security measures like encryption and immutable storage that are designed with privacy and compliance in mind. Rubrik also simplifies compliance through policy-driven automation that enforces data protection, retention, and deletion policies. Reporting tools give insights into policy effectiveness. The unified platform streamlines compliance processes around identifying, managing, and securing personal data.
This document discusses application architectures using Hadoop. It provides an example case study of clickstream analysis. It covers challenges of Hadoop implementation and various architectural considerations for data storage and modeling, data ingestion, and data processing. For data processing, it discusses different processing engines like MapReduce, Pig, Hive, Spark and Impala. It also discusses what specific processing needs to be done for the clickstream data like sessionization and filtering.
Application Architectures with Hadoop | Data Day Texas 2015Cloudera, Inc.
This document discusses application architectures using Hadoop. It begins with an introduction to the speaker and his book on Hadoop architectures. It then presents a case study on clickstream analysis, describing how web logs could be analyzed in Hadoop. The document discusses challenges of Hadoop implementation and various architectural considerations for data storage, modeling, ingestion, processing and more. It focuses on choices for storage layers, file formats, schema design and processing engines like MapReduce, Spark and Impala.
User management - the next-gen of authentication meetup 27012022lior mazor
Authentication is evolving. Customers are expecting much more from the user management experience in applications they are using today. Join us virtually for our upcoming "User Management - the next-gen of Authentication" meetup to learn about the secrets of building user management the right way, the secure way.
Webinar | Aligning GDPR Requirements with Today's Hybrid Cloud RealitiesDataStax
This webinar discussed how DataStax and Thales eSecurity can help organizations comply with GDPR requirements in today's hybrid cloud environments. The key points are:
1) GDPR compliance and hybrid cloud are realities organizations must address
2) A single "point solution" is insufficient - partnerships between data platform and security services providers are needed
3) DataStax and Thales eSecurity can provide the necessary access controls, authentication, encryption, auditing and other capabilities across disparate environments to meet the 7 key GDPR security requirements.
Turning Petabytes of Data into Profit with Hadoop for the World’s Biggest Ret...Cloudera, Inc.
PRGX is the world's leading provider of accounts payable audit services and works with leading global retailers. As new forms of data started to flow into their organizations, standard RDBMS systems were not allowing them to scale. Now, by using Talend with Cloudera Enterprise, they are able to acheive a 9-10x performance benefit in processing data, reduce errors, and now provide more innovative products and services to end customers.
Watch this webinar to learn how PRGX worked with Cloudera and Talend to create a high-performance computing platform for data analytics and discovery that rapidly allows them to process, model, and serve massive amount of structured and unstructured data.
Automatic Data Encryption (ADE) is a security tool available for UniData and UniVerse. This session will concentrate on more real world topics rather than ‘how to’ . The intent is to provide the knowledge required for creating a strategy for your application and customers.
- Splunk has been used at athenahealth for 3 years to correlate security information from various tools in a centralized dashboard. It is used by their security incident response team and security teams.
- Splunk ingest 400GB of data per day from over 100 forwarders, including Windows logs, firewall logs, and other security data. They aim to retain 2 years of searchable data.
- Splunk has provided value through improved visibility, flexibility to ingest various data sources, ability to customize alerts and searches, and more efficient incident response by reducing time spent searching multiple systems.
- Splunk has been used at athenahealth for 3 years to correlate security information from multiple tools in a centralized dashboard. It is used by the security incident response team and security teams.
- Splunk ingest 400GB of data per day from over 100 forwarders, including Windows logs, firewall logs, and application logs. It aims to retain searchable data for 2 years.
- Splunk provides athenahealth immediate visibility, flexibility to ingest various data sources, granular alerting and searching capabilities, and helps improve team efficiency by reducing time spent searching multiple systems.
This document describes Raxsonic's cloud-based data protection solution. It provides complete backup and recovery for servers, applications, desktops, laptops, and mobile devices. The solution protects data across multiple locations through server backup, endpoint backup, archiving, file sharing, and disaster recovery. It uses a unique three-tier encryption process to securely store data in their private and public cloud infrastructure or on-premise with a cloud-connected appliance. The solution is affordable and scalable to meet the needs of organizations of any size.
Comprehensive Security for the Enterprise III: Protecting Data at Rest and In...Cloudera, Inc.
This webinar discusses how you can use Navigator capabilities such as Encrypt and Key Trustee to secure data and enable compliance. Additionally, we will discuss our joint work with Intel on Project Rhino (an initiative to improve data security in Hadoop). We also hear from a security architect at a financial services company that is using encryption and key management to meet financial regulatory requirements.
JMS Secure Data presentation designed to give businesses who use PC's, Laptops, Servers including portable and mobile devices including magnetic media, used to store and transmit personal information, how to treat data and keep their business compliant.
Losing data which could cause damage or distress to individuals may lead to enforcement action against your business including financial penalties.
The document discusses application architectures using Hadoop. It provides an example case study of clickstream analysis of web logs. It discusses challenges of Hadoop implementation and various architectural considerations for data storage, modeling, ingestion, processing and what specific processing needs to happen for the case study. These include sessionization, filtering, and business intelligence/discovery. Storage options, file formats, schema design, and processing engines like MapReduce, Spark and Impala are also covered.
The document summarizes key points from a presentation on privacy for tech startups. It discusses why privacy is important for startups to consider, providing practical information security controls startups can implement, and new privacy principles from the GDPR that startups should be aware of. Some highlights include:
- Privacy should be a priority from the start and can help startups win trust among users and investors.
- Practical security controls include encrypting data, patching systems, training employees, and monitoring for vulnerabilities.
- The GDPR introduces new principles like data protection by design, security of processing, breach notification requirements, data protection impact assessments, and data protection officers.
Get to know which security standards are applicable to OpenStack clouds
Evgeniya Shumakher, Mirantis
Compliance with critical industry and regulatory standards used to be mostly the concern of application makers and customers integrating their solutions. Cloud computing – especially IaaS – has made things a lot more complicated. Meanwhile, emerging cloud-specific standards, like FedRAMP or CSA cloud security guidelines, are suggesting new, complex and stringent requirements – while also offering critical guidance.
The presentation offers an inside look at the process:
The most important compliance and security standards for cloud builders,
Where existing OpenStack resources can fully or partially solve common compliance problems
Where standards support within OpenStack is currently thin
The common workflow for architecting standards-compliant clouds,
Common risks and emerging opportunities.
Take a closer look at PCI Compliance for private OpenStack clouds
Scott Carlson, PayPal
PCI Compliance is very important for large financial institutions. As one of the larger installations of OpenStack within the Financial space, PayPal has driven forward the PCI conversation and will be sharing the technical perspective on the following related to PCI and OpenStack Private Clouds:
How does OpenStack fit into an existing PCI-Compliant Environment
When there is not an external Cloud Service Provider, how does your team need to compensate
What are the design choices required to continue to be PCI-Compliant
Physical versus Logical devices
Hypervisor versus Guest compliance
Management Networks for PCI and non-PCI Zones
The case study won’t give a fully prescriptive talk on how to obtain PCI compliance, because there is a lot more to gaining compliance than just making your cloud compliant, but will help to understand:
Where existing OpenStack resources can fully or partially solve PCI compliance problems,
Where OpenStack community needs to join together to solve in order to continue growth
into PCI-compliant spaces.
Your database holds your company's most sensitive and important assets- your data. All those customers' personal details, credit card numbers, social security numbers- you can't afford leaving them vulnerable to any- outside or inside- breaches.
Similar to Big Data Week 2016 - Worldpay - Deploying Secure Clusters (20)
Big Data Analytics 2017 - Worldpay - Empowering PaymentsDavid Walker
A presentation from the Big Data Analytics conference in 2017 that looks how Worldpay, a major payments provider, uses data science and big data analytics to influence successful card payments.
A discussion on how insurance companies could use telematics data, social media and open data sources to analyse and better price policies for their customers
Data Driven Insurance Underwriting (Dutch Language Version)David Walker
A discussion on how insurance companies could use telematics data, social media and open data sources to analyse and better price policies for their customers
An introduction to data virtualization in business intelligenceDavid Walker
A brief description of what Data Virtualisation is and how it can be used to support business intelligence applications and development. Originally presented to the ETIS Conference in Riga, Latvia in October 2013
A presentation to the ETIS Business Intelligence & Data Warehousing Working Group in Brussels 22-Mar-13 discussing what Saas & Cloud means and how they will affect BI in Telcos
1. The document describes building an analytical platform for a retailer by using open source tools R and RStudio along with SAP Sybase IQ database.
2. Key aspects included setting up SAP Sybase IQ as a column-store database for storage and querying of data, implementing R and RStudio for statistical analysis, and automating running of statistical models on new data.
3. The solution provided a low-cost platform capable of rapid prototyping of analytical models and production use for predictive analytics.
Gathering Business Requirements for Data WarehousesDavid Walker
This document provides an overview of the process for gathering business requirements for a data management and warehousing project. It discusses why requirements are gathered, the types of requirements needed, how business processes create data in the form of dimensions and measures, and how the gathered requirements will be used to design reports to meet business needs. A straw-man proposal is presented as a starting point for further discussion.
Building a data warehouse of call data recordsDavid Walker
This document discusses considerations for building a data warehouse to archive call detail records (CDRs) for a mobile virtual network operator (MVNO). The MVNO needed to improve compliance with data retention laws and enable more flexible analysis of CDR data. Key factors examined were whether to use Hadoop/NoSQL solutions and relational databases. While Hadoop can handle unstructured data, the CDRs have a defined structure and the IT team lacked NoSQL skills, so a relational database was deemed more suitable.
Those responsible for data management often struggle due to the many responsibilities involved. While organizations recognize data as a key asset, they are often unable to properly manage it. Creating a "Literal Staging Area" or LSA platform can help take a holistic view of improving overall data management. An LSA makes a copy of business systems that is refreshed daily and can be used for tasks like data quality monitoring, analysis, and operational reporting to help address data management challenges in a cost effective way for approximately $120,000.
A linux mac os x command line interfaceDavid Walker
This document describes a Linux/Mac OS X command line interface for interacting with the AffiliateWindow API. It provides scripts that allow sending API requests via cURL or Wget from the command line. The scripts read an XML request file, send it to the AffiliateWindow API server, and write the response to an XML file. This provides an alternative to PHP for accessing the API from the command line for testing, auditing, or using other development tools.
Connections a life in the day of - david walkerDavid Walker
David Walker is a Principal Consultant who leads large data warehousing projects with staff sizes between 1 to 20 people. He enjoys rugby and spends time with his family in Dorset when not traveling for work. The document provides biographical details about Walker's background, responsibilities, interests, and perspectives on technology and business challenges.
Conspectus data warehousing appliances – fad or futureDavid Walker
Data warehousing appliances aim to simplify and accelerate the process of extracting, transforming, and loading data from multiple source systems into a dedicated database for analysis. Traditional data warehousing systems are complex and expensive to implement and maintain over time as data volumes increase. Data warehousing appliances use commodity hardware and specialized database engines to radically reduce data loading times, improve query performance, and simplify administration. While appliances introduce new challenges around proprietary technologies and credibility of performance claims, organizations that have implemented them report major gains in query speed and storage efficiency with reduced support costs. As more vendors enter the market, appliances are poised to become a key part of many organizations' data warehousing strategies.
Using the right data model in a data martDavid Walker
A presentation describing how to choose the right data model design for your data mart. Discusses the pros and benefits of different data models with different rdbms technologies and tools
The document discusses spatial data and analysis. It defines spatial data as information that can be analyzed based on geographic context, such as locations, distances and boundaries. It then describes the three common types of spatial data - points, lines and polygons - and how they are used to answer questions about proximity and relationships between objects. Finally, it outlines some of the key sources for spatial data, challenges in working with spatial data, and provides a model for how to deliver spatial data and analysis.
UKOUG06 - An Introduction To Process Neutral Data Modelling - PresentationDavid Walker
Data Management & Warehousing is a consulting firm that specializes in enterprise data warehousing. The document discusses process neutral data modeling, which is a technique for designing data warehouse models that are less impacted by changes in source systems or business processes. It does this by incorporating metadata into the data model similar to how XML includes metadata in data files. The approach defines major entities, their types and properties, relationships between entities, and occurrences to model interactions between entities in a consistent way that supports managing changes.
IRM09 - What Can IT Really Deliver For BI and DW - PresentationDavid Walker
This document summarizes a discussion between the Data Management and Carehousing business about delivering Business Intelligence. Some of the key points covered include:
1. The business has substantial front-loaded costs to pay for Business Intelligence and Carehousing. There are also ongoing costs for system changes and maintenance.
2. The business must understand that Business Intelligence is an ongoing, long-term development and not a one-off project.
3. It is important for the business and IT to agree on what a successful Business Intelligence solution would look like.
ETIS11 - Agile Business Intelligence - PresentationDavid Walker
The document discusses techniques for becoming more agile in business intelligence projects. It advocates for establishing small, skilled teams with strong user relationships and delegated authority. True agile organizations allow teams to operate outside standard corporate procedures and regularly deliver incremental improvements. Large organizations tend to prioritize processes and risk avoidance over agility, creativity, and benefits. Successful examples demonstrate recognizing the need to overcome bureaucracy through practices like Lockheed Martin's SkunkWorks model.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Project Management Semester Long Project - Acuityjpupo2018
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Webinar: Designing a schema for a Data WarehouseFederico Razzoli
Are you new to data warehouses (DWH)? Do you need to check whether your data warehouse follows the best practices for a good design? In both cases, this webinar is for you.
A data warehouse is a central relational database that contains all measurements about a business or an organisation. This data comes from a variety of heterogeneous data sources, which includes databases of any type that back the applications used by the company, data files exported by some applications, or APIs provided by internal or external services.
But designing a data warehouse correctly is a hard task, which requires gathering information about the business processes that need to be analysed in the first place. These processes must be translated into so-called star schemas, which means, denormalised databases where each table represents a dimension or facts.
We will discuss these topics:
- How to gather information about a business;
- Understanding dictionaries and how to identify business entities;
- Dimensions and facts;
- Setting a table granularity;
- Types of facts;
- Types of dimensions;
- Snowflakes and how to avoid them;
- Expanding existing dimensions and facts.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.