Optimize Data for the Logical Data WarehouseAttunity
Rodan Zadeh, Director of Product Management at Attunity talks about how to optimize data for the logical data warehouse for the Cisco Virtual Tradeshow.
Optimize Data for the Logical Data WarehouseAttunity
Rodan Zadeh, Director of Product Management at Attunity talks about how to optimize data for the logical data warehouse for the Cisco Virtual Tradeshow.
Big Data Day LA 2016/ Use Case Driven track - How to Use Design Thinking to J...Data Con LA
There is a novel approach to identifying big data use cases, one which will ultimately lower the barrier to entry to big data projects and increase overall implementation success. This talk describes the approach used by big data pioneer and Datameer CEO Stefan Groschupf to drive over 200 production implementations.
Gerhard Pretorius, Cloud Architect, Rackspace Asia presented at the Accion Cloud in Practice event in Singapore, where he described how enterprises can benefit from adopting the cloud, and what they need to consider while doing so
Accion Labs offers a range of Big Data
consulting & implementation services to
develop your big data solutions. With a
team of big-data architects, data scientists,
PMs and consultants trained at leading Big
Data user firms such as Google, Yahoo and
Facebook, we offer a balanced mix of
strategy, design and implementation
expertise
Disaster Recovery for Big Data by Carlos Izquierdo at Big Data Spain 2017Big Data Spain
All modern Big Data solutions, like Hadoop, Kafka or the rest of the ecosystem tools, are designed as distributed processes and as such include some sort of redundancy for High Availability.
https://www.bigdataspain.org/2017/talk/disaster-recovery-for-big-data
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Introduction to Kudu - StampedeCon 2016StampedeCon
Over the past several years, the Hadoop ecosystem has made great strides in its real-time access capabilities, narrowing the gap compared to traditional database technologies. With systems such as Impala and Spark, analysts can now run complex queries or jobs over large datasets within a matter of seconds. With systems such as Apache HBase and Apache Phoenix, applications can achieve millisecond-scale random access to arbitrarily-sized datasets.
Despite these advances, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing workloads.
This talk will investigate the trade-offs between real-time transactional access and fast analytic performance from the perspective of storage engine internals. It will also describe Kudu, the new addition to the open source Hadoop ecosystem that fills the gap described above, complementing HDFS and HBase to provide a new option to achieve fast scans and fast random access from a single API.
The Practice of Big Data - The Hadoop ecosystem explained with usage scenarioskcmallu
What's the origin of Big Data? What are the real life usage scenarios where Hadoop has been successfully adopted? How do you get started within your organizations?
3 Things to Learn About:
* How Sparklyr supports a complete backend for dplyr, a popular tool for working with data frame objects both in memory and out of memory
* How Sparklyr llows data scientists to use dplyr to translate R code into Spark SQL
* How Sparklyr supports MLlib so data scientists can run classifiers, regressions, and many other machine learning algorithms in Spark
Securing your Big Data Environments in the CloudDataWorks Summit
Big Data tools are becoming a critical part of enterprise architectures and as such securing the data, at rest, and in motion is a necessity. More so, when you’re implementing these solutions in the cloud and the data doesn't reside within the confines of your trusted data center. Also, there is a fine balance between implementing enterprise-grade security and negotiating utmost performance given the overheads of encryption and/or identity management.
This session is designed to tackle these challenges head on and explain the various options available in the cloud. The focal points are the implementation of tools like Ranger and Knox for cloud deployments, but we also pay attention to the security features offered in the cloud that complement this process and secure the data in unprecedented ways.
Cloud Security + OSS Security tools are a deadly combination, when it comes to securing your Data Lake.
Pentaho Big Data Analytics with Vertica and HadoopMark Kromer
Overview of the Pentaho Big Data Analytics Suite from the Pentaho + Vertica presentation at Big Data Techcon 2014 in Boston for the session called "The Ultimate Selfie | Picture Yourself with the Fastest Analytics on Hadoop with HP Vertica and Pentaho"
Empowering you with Democratized Data Access, Data Science and Machine LearningDataWorks Summit
Data science with its specialized tools and knowledge has been a forte of data scientists. However, it is not easy even for data scientists to get access to data that could be in different data stores in the organization. To unleash the power of data and gain valuable insights, machine learning needs to be made easily consumable by various stake holders and access to data made simpler. As an organization's data volumes continue to grow, delivering these insights real time is a complex challenge to solve.
This session will provide on overview of an approach to building a scalable solution where machine and deep learning and access to data is made much more consumable and simpler by the fastest SQL on Hadoop engine on the planet, a rich data scientist toolset and an infrastructure that can deliver the responsiveness needed for production environments.
Speakers:
Pandit Prasad, Program Director, IBM
Ashutosh Mate, Global Senior Solutions Architect, IBM
These slides provide highlights of my book HDInsight Essentials. Book link is here: http://www.packtpub.com/establish-a-big-data-solution-using-hdinsight/book
Here I talk about examples and use cases for Big Data & Big Data Analytics and how we accomplished massive-scale sentiment, campaign and marketing analytics for Razorfish using a collecting of database, Big Data and analytics technologies.
Cloud and Analytics - From Platforms to an EcosystemDatabricks
Zurich North America is one of the largest providers of insurance solutions and services in the world with customers representing a wide range of industries from agriculture to construction and more than 90 percent of the Fortune 500.
Big Data Taiwan 2014 Track2-2: Informatica Big Data SolutionEtu Solution
講者：Informatica 資深產品顧問 | 尹寒柏
議題簡介：Big Data 時代，比的不是數據數量，而是了解數據的深度。現在，因為 Big Data 技術的成熟，讓非資訊背景的 CXO 們，可以讓過去像是專有名詞的 CI (Customer Intelligence) 變成動詞，從 BI 進入 CI，更連結消費者經濟的脈動，洞悉顧客的意圖。不過，有個 Big Data 時代要 注意的思維，那就是競爭到最後，不單只是看數據量的增長，還要比誰能更了解數據的深度。而 Informatica 正是這個最佳解決的答案。我們透過 Informatica 解決在企業及時提供可信賴數據的巨大壓力;同時隨著日益增高的數據量和複雜程度，Informatica 也有能力提供更快速彙集數據技術，從而讓數據變的有意義並可供企業用來促進效率提升、完善品質、保證確定性和發揮優勢的功能。Inforamtica 提供了更為快速有效地實現此目標的方案，是精誠集團在 Big Data 時代的最佳工具。
Solving Big Data Challenges for Enterprise Application Performance ManagementTilmann Rabl
This is a presentation that was held at the 38th Conference on Very Large Databases (VLDB), 2012.
Full paper and additional information available at:
http://msrg.org/papers/vldb12-bigdata
Abstract:
As the complexity of enterprise systems increases, the need for monitoring and analyzing such systems also grows. A number of companies have built sophisticated monitoring tools that go far beyond simple resource utilization reports. For example, based on instrumentation and specialized APIs, it is now possible to monitor single method invocations and trace individual transactions across geographically distributed systems. This high-level of detail enables more precise forms of analysis and prediction but comes at the price of high data rates (i.e., big data). To maximize the benefit of data monitoring, the data has to be stored for an extended period of time for ulterior analysis. This new wave of big data analytics imposes new challenges especially for the application performance monitoring systems. The monitoring data has to be stored in a system that can sustain the high data rates and at the same time enable an up-to-date view of the underlying infrastructure. With the advent of modern key-value stores, a variety of data storage systems have emerged that are built with a focus on scalability and high data rates as predominant in this monitoring use case.
In this work, we present our experience and a comprehensive performance evaluation of six modern (open-source) data stores in the context of application performance monitoring as part of CA Technologies initiative. We evaluated these systems with data and workloads that can be found in application performance monitoring, as well as, on-line advertisement, power monitoring, and many other use cases. We present our insights not only as performance results but also as lessons learned and our experience relating to the setup and configuration complexity of these data stores in an industry setting.
Big Data Day LA 2016/ Use Case Driven track - How to Use Design Thinking to J...Data Con LA
There is a novel approach to identifying big data use cases, one which will ultimately lower the barrier to entry to big data projects and increase overall implementation success. This talk describes the approach used by big data pioneer and Datameer CEO Stefan Groschupf to drive over 200 production implementations.
Gerhard Pretorius, Cloud Architect, Rackspace Asia presented at the Accion Cloud in Practice event in Singapore, where he described how enterprises can benefit from adopting the cloud, and what they need to consider while doing so
Accion Labs offers a range of Big Data
consulting & implementation services to
develop your big data solutions. With a
team of big-data architects, data scientists,
PMs and consultants trained at leading Big
Data user firms such as Google, Yahoo and
Facebook, we offer a balanced mix of
strategy, design and implementation
expertise
Disaster Recovery for Big Data by Carlos Izquierdo at Big Data Spain 2017Big Data Spain
All modern Big Data solutions, like Hadoop, Kafka or the rest of the ecosystem tools, are designed as distributed processes and as such include some sort of redundancy for High Availability.
https://www.bigdataspain.org/2017/talk/disaster-recovery-for-big-data
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Introduction to Kudu - StampedeCon 2016StampedeCon
Over the past several years, the Hadoop ecosystem has made great strides in its real-time access capabilities, narrowing the gap compared to traditional database technologies. With systems such as Impala and Spark, analysts can now run complex queries or jobs over large datasets within a matter of seconds. With systems such as Apache HBase and Apache Phoenix, applications can achieve millisecond-scale random access to arbitrarily-sized datasets.
Despite these advances, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing workloads.
This talk will investigate the trade-offs between real-time transactional access and fast analytic performance from the perspective of storage engine internals. It will also describe Kudu, the new addition to the open source Hadoop ecosystem that fills the gap described above, complementing HDFS and HBase to provide a new option to achieve fast scans and fast random access from a single API.
The Practice of Big Data - The Hadoop ecosystem explained with usage scenarioskcmallu
What's the origin of Big Data? What are the real life usage scenarios where Hadoop has been successfully adopted? How do you get started within your organizations?
3 Things to Learn About:
* How Sparklyr supports a complete backend for dplyr, a popular tool for working with data frame objects both in memory and out of memory
* How Sparklyr llows data scientists to use dplyr to translate R code into Spark SQL
* How Sparklyr supports MLlib so data scientists can run classifiers, regressions, and many other machine learning algorithms in Spark
Securing your Big Data Environments in the CloudDataWorks Summit
Big Data tools are becoming a critical part of enterprise architectures and as such securing the data, at rest, and in motion is a necessity. More so, when you’re implementing these solutions in the cloud and the data doesn't reside within the confines of your trusted data center. Also, there is a fine balance between implementing enterprise-grade security and negotiating utmost performance given the overheads of encryption and/or identity management.
This session is designed to tackle these challenges head on and explain the various options available in the cloud. The focal points are the implementation of tools like Ranger and Knox for cloud deployments, but we also pay attention to the security features offered in the cloud that complement this process and secure the data in unprecedented ways.
Cloud Security + OSS Security tools are a deadly combination, when it comes to securing your Data Lake.
Pentaho Big Data Analytics with Vertica and HadoopMark Kromer
Overview of the Pentaho Big Data Analytics Suite from the Pentaho + Vertica presentation at Big Data Techcon 2014 in Boston for the session called "The Ultimate Selfie | Picture Yourself with the Fastest Analytics on Hadoop with HP Vertica and Pentaho"
Empowering you with Democratized Data Access, Data Science and Machine LearningDataWorks Summit
Data science with its specialized tools and knowledge has been a forte of data scientists. However, it is not easy even for data scientists to get access to data that could be in different data stores in the organization. To unleash the power of data and gain valuable insights, machine learning needs to be made easily consumable by various stake holders and access to data made simpler. As an organization's data volumes continue to grow, delivering these insights real time is a complex challenge to solve.
This session will provide on overview of an approach to building a scalable solution where machine and deep learning and access to data is made much more consumable and simpler by the fastest SQL on Hadoop engine on the planet, a rich data scientist toolset and an infrastructure that can deliver the responsiveness needed for production environments.
Speakers:
Pandit Prasad, Program Director, IBM
Ashutosh Mate, Global Senior Solutions Architect, IBM
These slides provide highlights of my book HDInsight Essentials. Book link is here: http://www.packtpub.com/establish-a-big-data-solution-using-hdinsight/book
Here I talk about examples and use cases for Big Data & Big Data Analytics and how we accomplished massive-scale sentiment, campaign and marketing analytics for Razorfish using a collecting of database, Big Data and analytics technologies.
Cloud and Analytics - From Platforms to an EcosystemDatabricks
Zurich North America is one of the largest providers of insurance solutions and services in the world with customers representing a wide range of industries from agriculture to construction and more than 90 percent of the Fortune 500.
Big Data Taiwan 2014 Track2-2: Informatica Big Data SolutionEtu Solution
講者：Informatica 資深產品顧問 | 尹寒柏
議題簡介：Big Data 時代，比的不是數據數量，而是了解數據的深度。現在，因為 Big Data 技術的成熟，讓非資訊背景的 CXO 們，可以讓過去像是專有名詞的 CI (Customer Intelligence) 變成動詞，從 BI 進入 CI，更連結消費者經濟的脈動，洞悉顧客的意圖。不過，有個 Big Data 時代要 注意的思維，那就是競爭到最後，不單只是看數據量的增長，還要比誰能更了解數據的深度。而 Informatica 正是這個最佳解決的答案。我們透過 Informatica 解決在企業及時提供可信賴數據的巨大壓力;同時隨著日益增高的數據量和複雜程度，Informatica 也有能力提供更快速彙集數據技術，從而讓數據變的有意義並可供企業用來促進效率提升、完善品質、保證確定性和發揮優勢的功能。Inforamtica 提供了更為快速有效地實現此目標的方案，是精誠集團在 Big Data 時代的最佳工具。
Solving Big Data Challenges for Enterprise Application Performance ManagementTilmann Rabl
This is a presentation that was held at the 38th Conference on Very Large Databases (VLDB), 2012.
Full paper and additional information available at:
http://msrg.org/papers/vldb12-bigdata
Abstract:
As the complexity of enterprise systems increases, the need for monitoring and analyzing such systems also grows. A number of companies have built sophisticated monitoring tools that go far beyond simple resource utilization reports. For example, based on instrumentation and specialized APIs, it is now possible to monitor single method invocations and trace individual transactions across geographically distributed systems. This high-level of detail enables more precise forms of analysis and prediction but comes at the price of high data rates (i.e., big data). To maximize the benefit of data monitoring, the data has to be stored for an extended period of time for ulterior analysis. This new wave of big data analytics imposes new challenges especially for the application performance monitoring systems. The monitoring data has to be stored in a system that can sustain the high data rates and at the same time enable an up-to-date view of the underlying infrastructure. With the advent of modern key-value stores, a variety of data storage systems have emerged that are built with a focus on scalability and high data rates as predominant in this monitoring use case.
In this work, we present our experience and a comprehensive performance evaluation of six modern (open-source) data stores in the context of application performance monitoring as part of CA Technologies initiative. We evaluated these systems with data and workloads that can be found in application performance monitoring, as well as, on-line advertisement, power monitoring, and many other use cases. We present our insights not only as performance results but also as lessons learned and our experience relating to the setup and configuration complexity of these data stores in an industry setting.
How Intronis Reduced the Data Import Process With RingLead RingLead
"RingLead has been incredibly powerful. The power lies in its simplicity.”
Learn how Intronis uses RingLead to reduce the data import process from seven steps to one step.
Dimension Data – Enabling the Journey to the Cloud: Real Examplesitnewsafrica
Dimension Data – Enabling the Journey to the Cloud: Real Examples.
Presented by Grant Morgan, General Manager: Cloud at Dimension Data.
September 05, 2013 edition of the IT News Africa Innovation Dinner (www.innovationdinner.co.za)
Best Practices for Big Data Analytics with Machine Learning by DatameerDatameer
Don't forget! You can watch the full Datameer recording here:
http://info.datameer.com/Online-Slideshare-Big-Data-Analytics-Machine-Learning-OnDemand.html
Learn through industry use cases, how to empower users to identify patterns & relationships for recommendations using big data analytics.
Big data is data that, by virtue of its velocity, volume, or variety (the three Vs), cannot be easily stored or analyzed with traditional methods. Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
Hadoop as we know is a Java based massive scalable distributed framework for processing large data (several peta bytes) across a cluster (1000s) of commodity computers.
The Hadoop ecosystem has grown over the last few years and there is a lot of jargon in terms of tools as well as frameworks.
Many organizations are investing & innovating heavily in Hadoop to make it better and easier. The mind map on the next slide should be useful to get a high level picture of the ecosystem.
A short overview of Bigdata along with its popularity, ups and downs from past to present. We had a look of its needs, challenges and risks too. Architectures involved in it. Vendors associated with it.
Big data: Descoberta de conhecimento em ambientes de big data e computação na...Rio Info
Palestra sobre Big data: Descoberta de conhecimento em ambientes de big data e computação na nuvem apresentada por Nelson Favilla durante o Rio Info 2014
Big Data Predictive Analytics with Revolution R Enterprise (Gartner BI Summit...Revolution Analytics
Presented by David Smith, Chief Community Officer, Revolution Analytics at Garner Business Intelligence and Analytics Summit, April 2014.
In this presentation, I'll introduce the open source R language — the modern standard for Data Science — and the enhanced performance, scalability and ease-of-use capabilities of Revolution R Enterprise. Customer case studies will illustrate Revolution R Enterprise as a component of the real-time analytics deployment process, via integration with Hadoop, database warehousing systems and Cloud platforms, to implement data-driven end-user applications.
Hadoop Reporting and Analysis - JaspersoftHortonworks
Hadoop is deployed for a variety of uses, including web analytics, fraud detection, security monitoring, healthcare, environmental analysis, social media monitoring, and other purposes.
Bridging the Big Data Gap in the Software-Driven WorldCA Technologies
Implementing and managing a Big Data environment effectively requires essential efficiencies such as automation, performance monitoring and flexible infrastructure management. Discover new innovations that enable you to manage entire Big Data environments with unparalleled ease of use and clear enterprise visibility across a variety of data repositories.
To learn more about Mainframe solutions from CA Technologies, visit: http://bit.ly/1wbiPkl
Infrastructure Considerations for Analytical WorkloadsCognizant
Using Apache Hadoop clusters and Mahout for analyzing big data workloads yields extraordinary performance; we offer a detailed comparison of running Hadoop in a physical vs. virtual infrastructure environment.
A short presentation on big data and the technologies available for managing Big Data. and it also contains a brief description of the Apache Hadoop Framework
Cloudera's open-source Apache Hadoop distribution, CDH (Cloudera Distribution Including Apache Hadoop), targets enterprise-class deployments of that technology. Cloudera says that more than 50% of its engineering output is donated upstream to the various Apache-licensed open source projects.
https://www.pass4sureexam.com/ccD-410.html
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...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 the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
10. “ Pervasive TurboRush for Hive is exciting news for SQL analysts looking to discover trends, patterns and value in their Big Data on Hadoop. The faster their HIVE SQL queries perform, the faster their insights are achieved. Our work with Pervasive is another important step in Karmasphere’s commitment to deliver products that help developers, analysts and business users unlock the power of Big Data on Hadoop and expand its use beyond IT departments.” Martin Hall, Co-founder, Chairman and Executive Vice President of Corporate Development, Karmasphere, Inc.
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12. “ We’ve put Pervasive DataRush 5.0 to work inside wukong, our open source tool for executing Ruby tasks on Hadoop clusters , which we use to drive Infochimps’ production jobs on Amazon’s EC2. The result is exactly on-target: improving computational efficiency means we can get the largest jobs done using fewer resources in the cloud, which translates to hard-dollar cost savings .” Philip Kromer, CTO at Infochimps
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15. “ As more and more organizations adopt Hadoop to take on large scale data challenges, organizations risk wasting resources by scaling out with inefficient server workloads. Products like Pervasive DataRush can help increase the efficiency of each node in a Hadoop cluster to maximize throughput and help harness ever-growing data volumes.” David Menninger, VP and Research Director, Ventana Research
