1) The document discusses big data strategies and technologies including Oracle's big data solutions. It describes Oracle's big data appliance which is an integrated hardware and software platform for running Apache Hadoop.
2) Key technologies that enable deeper analytics on big data are discussed including advanced analytics, data mining, text mining and Oracle R. Use cases are provided in industries like insurance, travel and gaming.
3) An example use case of a "smart mall" is described where customer profiles and purchase data are analyzed in real-time to deliver personalized offers. The technology pattern for implementing such a use case with Oracle's real-time decisions and big data platform is outlined.
Cloud Storage Spring Cleaning: A Treasure HuntSteven Moy
This is a talk by Zach and me on how to analyze your S3 storage access pattern to save storage cost by lifecycle objects at the right time to the right cost tier.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
(BI Advanced) Hiram Fleitas - SQL Server Machine Learning Predict Sentiment O...Hiram Fleitas León
- TITLE:
Using Machine Learning and Python in SQL Server To Predict The Sentiment
Speaker: Fleitas, Hiram
- ABSTRACT:
In this session, I'm very excited to show you from start to finish how to use Machine Learning to predict a sentiment in real-time with SQL Server (On-Premise).
- AGENDA:
1. Add ML Features
2. Grant Access
3. Config
4. Install Pre-Trained & Open Source ML Models (DNN)
5. Code in Python and T-SQL
6. Python Profiling
7. Real-time scoring
8. Review Sentiment Results
9. Resources
How to select a modern data warehouse and get the most out of it?Slim Baltagi
In the first part of this talk, we will give a setup and definition of modern cloud data warehouses as well as outline problems with legacy and on-premise data warehouses.
We will speak to selecting, technically justifying, and practically using modern data warehouses, including criteria for how to pick a cloud data warehouse and where to start, how to use it in an optimum way and use it cost effectively.
In the second part of this talk, we discuss the challenges and where people are not getting their investment. In this business-focused track, we cover how to get business engagement, identifying the business cases/use cases, and how to leverage data as a service and consumption models.
Fast and Furious: From POC to an Enterprise Big Data Stack in 2014MapR Technologies
View this webinar presentation as CenturyLink Technology Solutions (Formerly Savvis) and MapR as we deconstruct and demystify “the enterprise big data stack.” We provide you with a more holistic view of the landscape, explore use cases to show how you can derive business value from it, and share best practices for navigating through the fragmented big data environment.
Cloud Storage Spring Cleaning: A Treasure HuntSteven Moy
This is a talk by Zach and me on how to analyze your S3 storage access pattern to save storage cost by lifecycle objects at the right time to the right cost tier.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
(BI Advanced) Hiram Fleitas - SQL Server Machine Learning Predict Sentiment O...Hiram Fleitas León
- TITLE:
Using Machine Learning and Python in SQL Server To Predict The Sentiment
Speaker: Fleitas, Hiram
- ABSTRACT:
In this session, I'm very excited to show you from start to finish how to use Machine Learning to predict a sentiment in real-time with SQL Server (On-Premise).
- AGENDA:
1. Add ML Features
2. Grant Access
3. Config
4. Install Pre-Trained & Open Source ML Models (DNN)
5. Code in Python and T-SQL
6. Python Profiling
7. Real-time scoring
8. Review Sentiment Results
9. Resources
How to select a modern data warehouse and get the most out of it?Slim Baltagi
In the first part of this talk, we will give a setup and definition of modern cloud data warehouses as well as outline problems with legacy and on-premise data warehouses.
We will speak to selecting, technically justifying, and practically using modern data warehouses, including criteria for how to pick a cloud data warehouse and where to start, how to use it in an optimum way and use it cost effectively.
In the second part of this talk, we discuss the challenges and where people are not getting their investment. In this business-focused track, we cover how to get business engagement, identifying the business cases/use cases, and how to leverage data as a service and consumption models.
Fast and Furious: From POC to an Enterprise Big Data Stack in 2014MapR Technologies
View this webinar presentation as CenturyLink Technology Solutions (Formerly Savvis) and MapR as we deconstruct and demystify “the enterprise big data stack.” We provide you with a more holistic view of the landscape, explore use cases to show how you can derive business value from it, and share best practices for navigating through the fragmented big data environment.
Hadoop World 2011: I Want to Be BIG - Lessons Learned at Scale - David "Sunny...Cloudera, Inc.
SGI has been a leading commercial vendor of Hadoop clusters since 2008. Leveraging SGI's experience with high performance clusters at scale, SGI has delivered individual Hadoop clusters of up to 4000 nodes. Integration, performance, and management all become issues at scale, and Hadoop clusters scale! In this presentation, SGI will discuss representative customer use cases, major design considerations for performance and power optimization, how integrated Hadoop solutions leveraging CDH, SGI Rackable clusters, and SGI Management Center best meet customer needs, and how SGI envisions the needs of enterprise customers evolving as Hadoop continues to move into mainstream adoption.
Building a Modern Data Architecture by Ben Sharma at Strata + Hadoop World Sa...Zaloni
When building your data stack, the architecture could be your biggest challenge. Yet it could also be the best predictor for success. With so many elements to consider and no proven playbook, where do you begin to assemble best practices for a scalable data architecture? Ben Sharma, thought leader and coauthor of Architecting Data Lakes, offers lessons learned from the field to get you started.
In this presentation, we:
1. Look at the challenges and opportunities of the data era
2. Look at key challenges of the legacy data warehouses such as data diversity, complexity, cost, scalabilily, performance, management, ...
3. Look at how modern data warehouses in the cloud not only overcome most of these challenges but also how some of them bring additional technical innovations and capabilities such as pay as you go cloud-based services, decoupling of storage and compute, scaling up or down, effortless management, native support of semi-structured data ...
4. Show how capabilities brought by modern data warehouses in the cloud, help businesses, either new or existing ones, during the phases of their lifecycle such as launch, growth, maturity and renewal/decline.
5. Share a Near-Real-Time Data Warehousing use case built on Snowflake and give a live demo to showcase ease of use, fast provisioning, continuous data ingestion, support of JSON data ...
An overview of Hadoop and Data warehouse from technologies and business viewpoints. The presentation also includes some of my personal observations and suggestions for people who want to join the field Big Data.
Big Data Day LA 2015 - Data Lake - Re Birth of Enterprise Data Thinking by Ra...Data Con LA
Why and How has the Big Data based Enterprise Data Lake solution based on No-SQL and SQL technologies has become significantly effective in solving enterprise data challenges than its predecessor EDW which had tried and failed to solve the same problem entirely based on SQL database only.
This white paper will present the opportunities laid down by
data lake and advanced analytics, as well as, the challenges
in integrating, mining and analyzing the data collected from
these sources. It goes over the important characteristics of
the data lake architecture and Data and Analytics as a
Service (DAaaS) model. It also delves into the features of a
successful data lake and its optimal designing. It goes over
data, applications, and analytics that are strung together to
speed-up the insight brewing process for industry’s
improvements with the help of a powerful architecture for
mining and analyzing unstructured data – data lake.
If you also got the Big Data itch, here is something to ease the pain :-)
Answers to this questions will be available soon (more info in the attached link)
Which Big Data Appliance should YOU use?
(click on the attached link for Poll results)
Appliances are Small and Quick, Right?
Revealing the 6 Types of Big Data Appliances
Uncovering the Main Players
Challenges, Pitfalls, and Winning the Big Data Game
Where is all this leading YOU to?
A Data Lake is a storage repository that can store large amount of structured, semi-structured, and unstructured data. It is a place to store every type of data in its native format with no fixed limits on account size or file. It offers high data quantity to increase analytic performance and native integration.
Data Lake is like a large container which is very similar to real lake and rivers. Just like in a lake you have multiple tributaries coming in, a data lake has structured data, unstructured data, machine to machine, logs flowing through in real-time.
Creating a Next-Generation Big Data ArchitecturePerficient, Inc.
If you’ve spent time investigating Big Data, you quickly realize that the issues surrounding Big Data are often complex to analyze and solve. The sheer volume, velocity and variety changes the way we think about data – including how enterprises approach data architecture.
Significant reduction in costs for processing, managing, and storing data, combined with the need for business agility and analytics, requires CIOs and enterprise architects to rethink their enterprise data architecture and develop a next-generation approach to solve the complexities of Big Data.
Creating the data architecture while integrating Big Data into the heart of the enterprise data architecture is a challenge. This webinar covered:
-Why Big Data capabilities must be strategically integrated into an enterprise’s data architecture
-How a next-generation architecture can be conceptualized
-The key components to a robust next generation architecture
-How to incrementally transition to a next generation data architecture
Big data architectures and the data lakeJames Serra
With so many new technologies it can get confusing on the best approach to building a big data architecture. The data lake is a great new concept, usually built in Hadoop, but what exactly is it and how does it fit in? In this presentation I'll discuss the four most common patterns in big data production implementations, the top-down vs bottoms-up approach to analytics, and how you can use a data lake and a RDBMS data warehouse together. We will go into detail on the characteristics of a data lake and its benefits, and how you still need to perform the same data governance tasks in a data lake as you do in a data warehouse. Come to this presentation to make sure your data lake does not turn into a data swamp!
Hadoop World 2011: I Want to Be BIG - Lessons Learned at Scale - David "Sunny...Cloudera, Inc.
SGI has been a leading commercial vendor of Hadoop clusters since 2008. Leveraging SGI's experience with high performance clusters at scale, SGI has delivered individual Hadoop clusters of up to 4000 nodes. Integration, performance, and management all become issues at scale, and Hadoop clusters scale! In this presentation, SGI will discuss representative customer use cases, major design considerations for performance and power optimization, how integrated Hadoop solutions leveraging CDH, SGI Rackable clusters, and SGI Management Center best meet customer needs, and how SGI envisions the needs of enterprise customers evolving as Hadoop continues to move into mainstream adoption.
Building a Modern Data Architecture by Ben Sharma at Strata + Hadoop World Sa...Zaloni
When building your data stack, the architecture could be your biggest challenge. Yet it could also be the best predictor for success. With so many elements to consider and no proven playbook, where do you begin to assemble best practices for a scalable data architecture? Ben Sharma, thought leader and coauthor of Architecting Data Lakes, offers lessons learned from the field to get you started.
In this presentation, we:
1. Look at the challenges and opportunities of the data era
2. Look at key challenges of the legacy data warehouses such as data diversity, complexity, cost, scalabilily, performance, management, ...
3. Look at how modern data warehouses in the cloud not only overcome most of these challenges but also how some of them bring additional technical innovations and capabilities such as pay as you go cloud-based services, decoupling of storage and compute, scaling up or down, effortless management, native support of semi-structured data ...
4. Show how capabilities brought by modern data warehouses in the cloud, help businesses, either new or existing ones, during the phases of their lifecycle such as launch, growth, maturity and renewal/decline.
5. Share a Near-Real-Time Data Warehousing use case built on Snowflake and give a live demo to showcase ease of use, fast provisioning, continuous data ingestion, support of JSON data ...
An overview of Hadoop and Data warehouse from technologies and business viewpoints. The presentation also includes some of my personal observations and suggestions for people who want to join the field Big Data.
Big Data Day LA 2015 - Data Lake - Re Birth of Enterprise Data Thinking by Ra...Data Con LA
Why and How has the Big Data based Enterprise Data Lake solution based on No-SQL and SQL technologies has become significantly effective in solving enterprise data challenges than its predecessor EDW which had tried and failed to solve the same problem entirely based on SQL database only.
This white paper will present the opportunities laid down by
data lake and advanced analytics, as well as, the challenges
in integrating, mining and analyzing the data collected from
these sources. It goes over the important characteristics of
the data lake architecture and Data and Analytics as a
Service (DAaaS) model. It also delves into the features of a
successful data lake and its optimal designing. It goes over
data, applications, and analytics that are strung together to
speed-up the insight brewing process for industry’s
improvements with the help of a powerful architecture for
mining and analyzing unstructured data – data lake.
If you also got the Big Data itch, here is something to ease the pain :-)
Answers to this questions will be available soon (more info in the attached link)
Which Big Data Appliance should YOU use?
(click on the attached link for Poll results)
Appliances are Small and Quick, Right?
Revealing the 6 Types of Big Data Appliances
Uncovering the Main Players
Challenges, Pitfalls, and Winning the Big Data Game
Where is all this leading YOU to?
A Data Lake is a storage repository that can store large amount of structured, semi-structured, and unstructured data. It is a place to store every type of data in its native format with no fixed limits on account size or file. It offers high data quantity to increase analytic performance and native integration.
Data Lake is like a large container which is very similar to real lake and rivers. Just like in a lake you have multiple tributaries coming in, a data lake has structured data, unstructured data, machine to machine, logs flowing through in real-time.
Creating a Next-Generation Big Data ArchitecturePerficient, Inc.
If you’ve spent time investigating Big Data, you quickly realize that the issues surrounding Big Data are often complex to analyze and solve. The sheer volume, velocity and variety changes the way we think about data – including how enterprises approach data architecture.
Significant reduction in costs for processing, managing, and storing data, combined with the need for business agility and analytics, requires CIOs and enterprise architects to rethink their enterprise data architecture and develop a next-generation approach to solve the complexities of Big Data.
Creating the data architecture while integrating Big Data into the heart of the enterprise data architecture is a challenge. This webinar covered:
-Why Big Data capabilities must be strategically integrated into an enterprise’s data architecture
-How a next-generation architecture can be conceptualized
-The key components to a robust next generation architecture
-How to incrementally transition to a next generation data architecture
Big data architectures and the data lakeJames Serra
With so many new technologies it can get confusing on the best approach to building a big data architecture. The data lake is a great new concept, usually built in Hadoop, but what exactly is it and how does it fit in? In this presentation I'll discuss the four most common patterns in big data production implementations, the top-down vs bottoms-up approach to analytics, and how you can use a data lake and a RDBMS data warehouse together. We will go into detail on the characteristics of a data lake and its benefits, and how you still need to perform the same data governance tasks in a data lake as you do in a data warehouse. Come to this presentation to make sure your data lake does not turn into a data swamp!
Oracle Openworld Presentation with Paul Kent (SAS) on Big Data Appliance and ...jdijcks
Learn about the benefits of Oracle Big Data Appliance and how it can drive business value underneath applications and tools. This includes a section by Paul Kent, VP Big Data SAS describing how SAS runs well on Oracle Engineered Systems and on Oracle Big Data Appliance specifically.
Oracle Big Data Appliance and Big Data SQL for advanced analyticsjdijcks
Overview presentation showing Oracle Big Data Appliance and Oracle Big Data SQL in combination with why this really matters. Big Data SQL brings you the unique ability to analyze data across the entire spectrum of system, NoSQL, Hadoop and Oracle Database.
Introducing the Big Data Ecosystem with Caserta Concepts & TalendCaserta
In this one-hour webinar, Caserta Concepts and Talend described an approach to achieve an architectural framework and roadmap to extend a traditional enterprise data warehouse environment, into a Big Data ecosystem.
They illustrated the architectural components involved for collecting, analyzing and delivering Big Data, with a focus on the importance of Hadoop, Data Integration, Machine Learning, NoSQL, Business Intelligence and Analytics.
Attendees learned:
Which Big Data technologies can’t be ignored
Considerations when extending the data ecosystem
What happens to your existing investment
What are the points of integration
Does Big Data = better data?
To find access the recorded webinar or to learn more, visit http://www.casertaconcepts.com/.
Контроль зверей: инструменты для управления и мониторинга распределенных сист...yaevents
Александр Козлов, Cloudera Inc.
Александр Козлов, старший архитектор в Cloudera Inc., работает с большими компаниями, многие из которых находятся в рейтинге Fortune 500, над проектами по созданию систем анализа большого количества данных. Закончил аспирантуру физического факультета Московского государственного университета, после чего также получил степень Ph.D. в Стэнфорде. До Cloudera и после окончания учебы работал над статистическим анализом данных и соответствующими компьютерными технологиями в SGI, Hewlett-Packard, а также стартапе Turn.
Тема доклада
Контроль зверей: инструменты для управления и мониторинга распределенных систем от Cloudera.
Тезисы
Поддержание распределенных систем, состоящих из тысяч компьютеров, является сложной задачей. Компания Cloudera, которая специализируется на создании распределенных технологий, разработала набор средств для централизованного управления распределенных Hadoop/HBase кластеров. Hadoop и HBase являются проектами Apache Software Foundation, и их применение для анализа частично структурированных данных ускоряется во всем мире. В этом докладе будет рассказано о SCM, системе для конфигурации, настройки, и управления Hadoop/HBase и Activity Monitor, системе для мониторинга ряда ОС и Hadoop/HBase метрик, а также об особенностях подхода Cloudera в отличие от существующих решений для мониторинга (Tivoli, xCat, Ganglia, Nagios и т.д.).
SplunkLive: New Visibility=New Opportunity: How IT Can Drive Business Value Splunk
We know Splunk helps us solve problems at the IT operations level. But more and more Splunk helps us to make machine-generated data relevant for non-technical business users. With Splunk you can ask any question at any time, without planning questions or structures in advance. And once you’ve built initial dashboards, you can empower business users to access them so they can get instant, accurate data on their own. Join us for this session where we’ll review how to build custom dashboards that provide both up-to-the-minute and long-term trending analysis that business users need to make the decisions that impact revenue.
Big Data, Big Content, and Aligning Your Storage StrategyHitachi Vantara
Fred Oh's presentation for SNW Spring, Monday 4/2/12, 1:00–1:45PM
Unstructured data growth is in an explosive state, and has no signs of slowing down. Costs continue to rise along with new regulations mandating longer data retention. Moreover, disparate silos, multivendor storage assets and less than optimal use of existing assets have all contributed to ‘accidental architectures.’ And while they can be key drivers for organizations to explore incremental, innovative solutions to their data challenges, they may provide only short-term gain. Join us for this session as we outline the business benefits of a truly unified, integrated platform to manage all block, file and object data that allows enterprises can make the most out of their storage resources. We explore the benefits of an integrated approach to multiprotocol file sharing, intelligent file tiering, federated search and active archiving; how to simplify and reduce the need for backup without the risk of losing availability; and the economic benefits of an integrated architecture approach that leads to lowering TCSO by 35% or more.
Hadoop World 2011: The Blind Men and the Elephant - Matthew Aslett - The 451 ...Cloudera, Inc.
Who is contributing to the Hadoop ecosystem, what are they contributing, and why? Who are the vendors that are supplying Hadoop-related products and services and what do they want from Hadoop? How is the expanding ecosystem benefiting or damaging the Apache Hadoop project? What are the emerging alternatives to Hadoop and what chance do they have? In this session, the 451 Group will seek to answer these questions based on their latest research and present their perspective of where Hadoop fits in the total data management landscape.
Explores the notion of "Hadoop as a Data Refinery" within an organisation, be it one with an existing Business Intelligence system or none - looks at 'agile data' as a a benefit of using Hadoop as the store for historical, unstructured and very-large-scale datasets.
The final slides look at the challenge of an organisation becoming "data driven"
Hadoop as Data Refinery - Steve LoughranJAX London
Apache Hadoop is often described as a "Big Data Platform" but what does that mean? One way to better understand Hadoop is to talk about how Hadoop is used. This talk discusses using Hadoop as a "Data Refinery", which is a common use case. The concept is very much like a traditional oil refinery except with data, pulling in large quantities of "crude data" over pipelines, refining some into useful business intelligence; refining other pieces into slightly less crude data that stays in the cluster until needed later. This metaphor proves useful when considering how Hadoop could be adopted in an organisation that already has data warehousing and business intelligence systems -and when contemplating how to hook up a Hadoop cluster to the sources of data inside and outside that organisation. A key point to remember is that storing data in Hadoop is not a means to an end any more than storing data in a database is: it is extracting information from that data. Using Hadoop as a front end "data refinery" means that it can integrate with existing Business Intelligence systems, while providing the platform for new applications.
Big Data Beyond Hadoop*: Research Directions for the FutureOdinot Stanislas
Michael Wrinn
Research Program Director, University Research Office,
Intel Corporation
Jason Dai
Engineering Director and Principal Engineer,
Intel Corporation
Left Brain, Right Brain: How to Unify Enterprise AnalyticsInside Analysis
The Briefing Room with Robin Bloor and Teradata
Live Webcast on Jan. 29, 2013
Despite its name, effective Data Science requires a certain amount of artistic flair. Analysts must be creative about how and where they find the insights that will drive business value. One classic roadblock to that kind of frictionless process? Programming. Not everyone can code Java, which makes the unstructured domain of Hadoop quite challenging for the average business analyst.
Check out the slides from this episode of the Briefing Room to hear veteran Analyst Dr. Robin Bloor explain how a new generation of analytical platforms will solve the complexity of unifying structured and unstructured data. He'll be briefed by Steve Wooledge of Teradata Aster who will tout his company's Big Data Appliance, which leverages the SQL-H bridge, an innovation designed to connect Hadoop with SQL.
Visit: http://www.insideanalysis.com
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
4. Big Data
React to an Event Pro-Actively Change Outcomes
“Technology presents the opportunity
to transform business“*
Mark Hurd, President, Oracle
* Oracle Profit Magazine, Volume 17, Number 1
5. Big Data’s Key Ingredient
“ Improvement merely lets you Big Data transforms
hit the numbers. Creativity is our business 5%
what transforms.“*
Ron Johnson, CEO, JCPenney
Big Data improves
our business 20%
What is Big Data?
75%
* Fortune Magazine VOL. 165, NO. 4
6. Big Data Extends the Breadth and Speed of Data
Video and Images
Big Data:
Decisions based Documents
on all your data
Social Data
Machine-Generated Data
Information
Architectures
Today: Transactions
Decisions based
on database data
7. Big Data Extends the Depth of Analytics
Graph Analytics
Statistics
Query and Reporting Data Mining
2 miles
Spatial Analytics
Text Analytics
8. Big Data Defined
Big Data: Techniques and
Technologies that Enable Enterprises
to Effectively and Economically
Analyze All of their Data
12. Oracle Big Data Strategy
BI Tools
Semantic Text
CEP Data
& Advanced
RTD
Management Analytics
Graph Spatial
Data Discovery Tools
Management Infrastructure
Build Acquire Adopt Engineer
14. Big Data Appliance
Hardware:
• 288 CPU cores with 1152 GB RAM
• 648 TB of raw disk storage
• 40 Gb/s InfiniBand
Integrated Software:
• Oracle Linux
• Oracle Java VM
• Cloudera Distribution of Apache Hadoop (CDH)
• Cloudera Manager
• Open-source distribution of R
• NoSQL Database Community Edition
All integrated software (except NoSQL DB CE) is supported as part of Premier Support for Systems and Premier Support for
Operating Systems
15. Oracle Big Data Appliance
File System Mount UI Framework SDK
FUSE-DFS HUE HUE SDK
Workflow Scheduling Metadata
APACHE OOZIE APACHE OOZIE APACHE HIVE
Languages / Compilers
APACHE PIG, APACHE HIVE, APACHE MAHOUT
Fast
Data
Read/Write
Integration
Access
APACHE
FLUME, APACHE APACHE HBASE
SQOOP
HDFS, MAPREDUCE
Coordination
APACHE ZOOKEEPER
16. Why Cloudera?
• Includes Open Source Apache Hadoop
– Fast evolution in critical features
– Proven at very large scale
• Managed Distribution
– Components certified to work together in regular updates
– Cloudera Manager provides Management GUI
• Most popular distribution in the market
17. Oracle and Cloudera
• All Cloudera software pre-installed and pre-configured
on BDA
– Engineered with Cloudera
• All Cloudera assets included
– Single Oracle Product SKU for HW & SW
– Single Oracle Support SKU for HW & SW (life of the machine)
• Oracle is the single point of contact for the solution
18. Price comparison
Oracle Big Data Appliance “Build-Your-Own” – HP hardware and Cloudera
Year 1 Year 2 Year 3 Total Year 1 Year 2 Year 3 Total
Servers and
BDA Cost $450,000 $428,220
switches
Support
$54,000 $54,000 $54,000 Support Cost $136,233 $72,000 $72,000
Cost
On-site Installation &
Installation $14,150 configuration
not included
Total $518,150 $54,000 $54,000 $626,150 Total $564,453 $72,000 $72,000 $708,453
Full details at https://blogs.oracle.com/datawarehousing/entry/price_comparison_for_big_data
19. Oracle NoSQL Database
A distributed, scalable key-value database
• Simple Data Model
• Key-value pair with major+sub-key paradigm Application Application
• Read/insert/update/delete operations NoSQLDB Driver NoSQLDB Driver
• Scalability
• Dynamic data partitioning and distribution
• Optimized data access via intelligent driver
• High availability
• One or more replicas
• Disaster recovery through location of replicas
• Resilient to partition master failures
• No single point of failure
• Transparent load balancing Storage Nodes Storage Nodes
• Reads from master or replicas Data Center A Data Center B
• Driver is network topology & latency aware
20. Big Data Connectors
Optimized integration of Hadoop with Oracle Database
and Oracle Exadata
• Oracle Loader for Hadoop
• Oracle Direct Connector for Hadoop Distributed File System
(HDFS)
• Oracle Data Integrator Application Adapter for Hadoop
• Oracle R Connector for Hadoop
• Does not require Big Data Appliance – can be licensed for
Hadoop running on non-Oracle hardware
21. Oracle Loader for Hadoop
Use The Cluster
ORACLE LOADER FOR HADOOP
MAP
REDUCE
MAP Last stage in MapReduce
MAP
SHUFFLE
/SORT
REDUCE workflow
Partitioned and non-
MAP REDUCE partitioned tables
MAP REDUCE
SHUFFLE
MAP /SORT REDUCE
Online and offline loads
22. Oracle Direct Connector for HDFS
Direct Access from Oracle Database
HDFS Oracle Database
SQL Query
SQL access to HDFS
External
Table External table view
Data query or import
DCH
DCH
HDFS
Infini
Band DCH
Client
23. Oracle Data Integrator
Simplifying MapReduce
Oracle
Data
Integrator Automatically generates
MapReduce code
Oracle
Loader for Manages the process
Hadoop
Loads into Data Warehouse
24. What is Data Discovery?
Simplified
Quickly explore all relevant data
Relationships Advanced search Structured
undefined or unknown Faceted navigation Semi-structured
No pre-defined model Analytics Unstructured
required Messy data
Rapid, iterative change Beyond the data
warehouse
25. Business Intelligence and Data Discovery
Complementary Solutions, Integrated Business Processes
Known & Clearly Uncertain or
Defined Questions Open-Ended Questions
Who, What, When? Why, How, What Else?
Un-modeled Data Insights yield
Data Discovery
mature models
Diverse and Changing Models and KPIs
Fast Answers to New Questions
New questions
Modeled Data Business Intelligence
require new
Proven Answers to Known
Conforms to a Single Model Questions
data, explorati
on
26. Oracle Endeca Information Discovery
A platform for data discovery applications across the enterprise
Endeca Information Discovery
(EID) helps organizations
quickly explore all relevant data
• Combine structured & unstructured
data from disparate systems
• Rapidly assemble easy to use
analysis applications
• Automatically organize information
for search, discovery & analysis
Hadoop, you may want to either access that data from Oracle Database by issuing SQL against HDFS files or by moving the data into Oracle tables.Lets start with the latter -- moving the data into Oracle tables. Oracle Loader for Hadoop (or OLH) is a high performance loader for fast movement of data from any Hadoop cluster into Oracle Database tables. Like all other parts the Big Data Connectors, it is available on any Hadoop cluster based on Apache Hadoop in addition to the Big Data Appliance.If you want to take the results and perform additional analysis using advanced BI and data warehousing technologies or incorporate in other applications, OLH is both fast and reduces the processing load on the Database server. It runs as a map reduce job and uses the Hadoop server’s processing resources to sample, sort and pre-partition the data based on the target database metadata. It can automatically take input in delimited text files (CSV) or Hive tables or you can write your own input format. OLH can either directly load the results into the database using the parallel direct path load interface or JDBC, create Oracle formatted Datapump files. OLH has built into load balancing across the reducer nodes that prevents performance from degrading due to unbalanced loads.
Oracle Direct Connector for HDFS makes it possible to access to data on the Hadoop cluster in HDFS from Oracle using SQL. It provides a virtual table view of the HDFS files and the allows for parallel query access to data using the standard Oracle database external table mechanism. If you are using BDA and Exadata, the connectivity occurs using infiniband network fabric so the database access to HDFS, in the very scientific words of the development manager, “flies”. If you need to import the data in HDFS into Oracle, the Direct Connector does not require a file copy and without using Linux Fuse. Instead it uses the native Oracle Loader interface.
If you already use Oracle Data Integrator (or are familiar with this kind of tool and want to use ODI), then it can simplify the MapReduce process.As long as you can describe the transformation that you need to perform on the data, ODI can generate the MapReduce code for you and run that process. It can even invoke Oracle Loader for Hadoop at the end of the cycle.So if you are not an expert in Java, parallel algorithms and the Hadoop framework, there is still a way to use it all to organize your code.Note:ODI generates SQL code which is then passed into Hive (a component of many Hadoop distributions) which generates the actual Java MapReduce codeYou need Big Data Connectors, specifically the ODI Application Adaptor for Hadoop, to make all this work
Our view of the BI landscape is that there are fundamentally two dominant types of problems.On one hand there are questions where we can define up-front both the process and the data required to answer them. What are sales forecasts by region? What is my performance relative to expectation?On the other hand are questions where either the process or the data cannot be defined ahead of time; these questions are open ended by nature. What customers should I target? Why are my sales going down? It's also interesting to point out that these questions are far more transient than the other type, and this follows from their open ended nature. Each question leads to new questions. The interaction model for the former is more like “looking it up”; it’s a report or dashboard. On the other hand, when you don't know exactly what you need or how to ask for ii, the necessary interaction model is exploration and discovery. A dialog with the data.<transition>It also follows that, as a matter of practice, some data is modeled and other data is not. We take modeled to mean that there is a single, overarching semantic model. Of course, modeling costs time and money and so we generally only make the investment in cases where the expected return on that investment is large enough to justify the effort.The cost of storing un-modeled data has continued to drop but importantly, with the popularization of Hadoop, the promise of deriving value from un-modeled data is rising rapidly. The result is an explosion in the capture of un-modeled data.Through this view of the BI landscape we can see how Traditional Business Intelligence and Data Discovery fit in.<transition>Traditional Business Intelligence is purpose built and very strong for known questions and modeled data. Friction arises when organizations attempt to use these products for new and unpredictable questions, which require similarly new and unpredictable data models to meet the need.<transition>In the other space is the emerging market category of data discovery, where the goal is to provide everyday business users with fast answers to new questions to make better, more informed business decisions. Data discovery tools follow several key market trends:First, the growth in data volume, diversity, and complexity. Not much to say here that hasn't already been said. Organizations today are beginning to understand the value inherent in this information and are looking for tools that can unlock that value to give them competitive advantage. And more and more users need to access and understand this information.Second, the consumerization of business software. When IT is unable to deliver, business users are increasingly willing to go outside of IT in order to meet their own needs. Empowered with their choice of tools, and with expectations formed in the consumer world, expectations for amazing user experiences have never been higher.
How do we do it. Endeca Information Discovery provides a full featured platform for creating discovery applications that provide access to all kinds of informationDrilling into the architecture, we accomplish this with three tiers
Notes:This slide is a logical representation of the scope of a Big Data solution. It provides the basis for describing data flows in each stage of the Big Data process in the following slides.The scope of a Big Data solution includes taking actions and decisions on the results of analysis, hence integration with Applications.Real-time event detection can be part of a Big Data solution. This is an important point to draw out because IBM claims it’s Steams capability is a USP, see the book Understanding Big Data, Analytics for Enterprise Class Hadoop and data Streaming.