Aginity "Big Data" Research Lab

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Introducing Aginity’s“Big Data” Research Lab

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How much MPP power can $5,682.10 buy in 2009? ,[object Object],Then And now

The Hardware Parts List and Cost: $5,682.10

The Databases We Are Testing ,[object Object]

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Some assembly required – about 11 hours A very ordinary box A partial shipment arrives Pieces parts Fine-tuning the OS Few tools required The assembly line Snapping the front panel in Ready to boot The right driver helps 2/3rds complete All 9 yards….so to speak

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In-Database Analytics ,[object Object],[object Object],[object Object]

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Contenu connexe

Tendances

Big Data Tech Stack

Big Data Tech Stack

Big Data Tech Stack

Abdullah Çetin ÇAVDAR

Bigdata. Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."[2] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[3] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[4] connectomics, complex physics simulations, biology and environmental research.[5] Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[10] Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[11] What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."

Bigdata " new level"

Bigdata " new level"

Bigdata " new level"

Vamshikrishna Goud

big data overview ppt

big data overview ppt

big data overview ppt

Big Data Analytics

Big Data Analytics

Big Data Analytics

Sreedhar Chowdam

Big Data Analytics MIS presentation

Big Data Analytics MIS presentation

Big Data Analytics MIS presentation

Big Data in Action : Operations, Analytics and more

Big Data in Action : Operations, Analytics and more

Big Data in Action : Operations, Analytics and more

Softweb Solutions

Big data abstract

Big data abstract

Big data abstract

nandhiniarumugam619

Building Your Own Facebook Real Time Analytics System with Cassandra and GigaSpaces. Facebook's real time analytics system is a good reference for those looking to build their real time analytics system for big data. The first part covers the lessons from Facebook's experience and the reason they chose HBase over Cassandra. In the second part of the session, we learn how we can build our own Real Time Analytics system, achieve better performance, gain real business insights, and business analytics on our big data, and make the deployment and scaling significantly simpler using the new version of Cassandra and GigaSpaces Cloudify.

Big Data Real Time Analytics - A Facebook Case Study

Big Data Real Time Analytics - A Facebook Case Study

Big Data Real Time Analytics - A Facebook Case Study

Analysis of big data in pandemic case

Analysis of big data in pandemic case

Analysis of big data in pandemic case

Big data unit 2

Big data unit 2

Big data unit 2

Big data (4Vs,history,concept,algorithm) analysis and applications #bigdata #...

Big data (4Vs,history,concept,algorithm) analysis and applications #bigdata #...

Big data (4Vs,history,concept,algorithm) analysis and applications #bigdata #...

BigData

SUM TWO is making 'serious investments' in big data, cloud, mobility !!! “Big data refers to the datasets whose size is beyond the ability of atypical database software tools to capture ,store, manage and analyze.defines big data the following way: “Big data is data that exceeds theprocessing capacity of conventional database systems. The data is too big, moves toofast, or doesnt fit the strictures of your database architectures. The 3 Vs of Big data.Apache Hadoop is 100% open source, and pioneered a fundamentally new way of storing and processing data. Instead of relying on expensive, proprietary hardware and different systems to store and process data, Hadoop enables distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that both store and process the data, and can scale without limits. With Hadoop, no data is too big. And in today’s hyper-connected world where more and more data is being created every day, Hadoop’s breakthrough advantages mean that businesses and organizations can now find value in data that was recently considered useless.Hadoop’s cost advantages over legacy systems redefine the economics of data. Legacy systems, while fine for certain workloads, simply were not engineered with the needs of Big Data in mind and are far too expensive to be used for general purpose with today's largest data sets.One of the cost advantages of Hadoop is that because it relies in an internally redundant data structure and is deployed on industry standard servers rather than expensive specialized data storage systems, you can afford to store data not previously viable . And we all know that once data is on tape, it’s essentially the same as if it had been deleted - accessible only in extreme circumstances.Make Big Data the Lifeblood of Your Enterprise With data growing so rapidly and the rise of unstructured data accounting for 90% of the data today, the time has come for enterprises to re-evaluate their approach to data storage, management and analytics. Legacy systems will remain necessary for specific high-value, low-volume workloads, and compliment the use of Hadoop-optimizing the data management structure in your organization by putting the right Big Data workloads in the right systems. The cost-effectiveness, scalability and streamlined architectures of Hadoop will make the technology more and more attractive. In fact, the need for Hadoop is no longer a question.

Bigdata

Saravanan Manoharan

Big Data & Data Science

Big Data & Data Science

Big Data & Data Science

Big data ppt

Big data no company

Big data no company

Big data no company

Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include different types such as structured/unstructured and streaming/batch, and different sizes from terabytes to zettabytes. Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale. Analyzing big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in significantly better and faster decisions.

Big Data Analytics

Big Data Analytics

Big Data Analytics

Exploring Big Data Analytics Tools

Exploring Big Data Analytics Tools

Exploring Big Data Analytics Tools

Multisoft Virtual Academy

Data lake ppt

Big data Ppt

Prashant Navatre

Tendances (20)

Big Data Tech Stack

Big Data Tech Stack

Big Data Tech Stack

Bigdata " new level"

Bigdata " new level"

Bigdata " new level"

big data overview ppt

big data overview ppt

big data overview ppt

Big Data Analytics

Big Data Analytics

Big Data Analytics

Big Data Analytics MIS presentation

Big Data Analytics MIS presentation

Big Data Analytics MIS presentation

Big Data in Action : Operations, Analytics and more

Big Data in Action : Operations, Analytics and more

Big Data in Action : Operations, Analytics and more

Big data abstract

Big data abstract

Big data abstract

Big Data Real Time Analytics - A Facebook Case Study

Big Data Real Time Analytics - A Facebook Case Study

Big Data Real Time Analytics - A Facebook Case Study

Analysis of big data in pandemic case

Analysis of big data in pandemic case

Analysis of big data in pandemic case

Big data unit 2

Big data unit 2

Big data unit 2

Big data (4Vs,history,concept,algorithm) analysis and applications #bigdata #...

Big data (4Vs,history,concept,algorithm) analysis and applications #bigdata #...

Big data (4Vs,history,concept,algorithm) analysis and applications #bigdata #...

BigData

Bigdata

Big Data & Data Science

Big Data & Data Science

Big Data & Data Science

Big data ppt

Big data no company

Big data no company

Big data no company

Big Data Analytics

Big Data Analytics

Big Data Analytics

Exploring Big Data Analytics Tools

Exploring Big Data Analytics Tools

Exploring Big Data Analytics Tools

Data lake ppt

Big data Ppt

En vedette

Business unIntelligence, Chapter 5

Business unIntelligence, Chapter 5

Business unIntelligence, Chapter 5

Aginity Big Data Research Lab

Aginity Big Data Research Lab

Aginity Big Data Research Lab

IBM Netezza - The data warehouse in a big data strategy

IBM Netezza - The data warehouse in a big data strategy

IBM Netezza - The data warehouse in a big data strategy

netezza-pdf

Part 2 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Part 2 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Part 2 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Andreas Buckenhofer

Part 1 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Part 1 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Part 1 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Andreas Buckenhofer

Part 3 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Part 3 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Part 3 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Andreas Buckenhofer

Netezza Deep Dives

Netezza Deep Dives

Netezza Deep Dives

The IBM Netezza datawarehouse appliance

The IBM Netezza datawarehouse appliance

The IBM Netezza datawarehouse appliance

Präsentation auf der DOAG Konferenz Metadaten sind ein häufig vernachlässigtes Thema, da Metadaten als langweilig betrachtet oder auch nicht bewusst wahr genommen werden. Auch die eher abstrakten Beschreibungen wie "Metadaten sind Daten über Daten" sind nicht gerade hilfreich. In der Präsentation werden die verschiedenen Arten von Metadaten (fachlich, technisch, prozessual) besprochen. Es wird darauf eingegangen, wie diese in einem Data Vault Projekt genutzt wurden, um z.B. Vorgaben festzulegen oder Code zu generieren.

Metadaten und Data Vault (Meta Vault)

Metadaten und Data Vault (Meta Vault)

Metadaten und Data Vault (Meta Vault)

Andreas Buckenhofer

An Introduction to Netezza

An Introduction to Netezza

An Introduction to Netezza

Vijaya Chandrika

Netezza fundamentals for developers

Netezza fundamentals for developers

Netezza fundamentals for developers

Part 4 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Part 4 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Part 4 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Andreas Buckenhofer

IBM Pure Data System for Analytics (Netezza)

IBM Pure Data System for Analytics (Netezza)

IBM Pure Data System for Analytics (Netezza)

Girish Srivastava

En vedette (14)

Business unIntelligence, Chapter 5

Business unIntelligence, Chapter 5

Business unIntelligence, Chapter 5

Aginity Big Data Research Lab

Aginity Big Data Research Lab

Aginity Big Data Research Lab

IBM Netezza - The data warehouse in a big data strategy

IBM Netezza - The data warehouse in a big data strategy

IBM Netezza - The data warehouse in a big data strategy

netezza-pdf

Part 2 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Part 2 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Part 2 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Part 1 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Part 1 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Part 1 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Part 3 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Part 3 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Part 3 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Netezza Deep Dives

Netezza Deep Dives

Netezza Deep Dives

The IBM Netezza datawarehouse appliance

The IBM Netezza datawarehouse appliance

The IBM Netezza datawarehouse appliance

Metadaten und Data Vault (Meta Vault)

Metadaten und Data Vault (Meta Vault)

Metadaten und Data Vault (Meta Vault)

An Introduction to Netezza

An Introduction to Netezza

An Introduction to Netezza

Netezza fundamentals for developers

Netezza fundamentals for developers

Netezza fundamentals for developers

Part 4 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Part 4 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

Part 4 - Data Warehousing Lecture at BW Cooperative State University (DHBW)

IBM Pure Data System for Analytics (Netezza)

IBM Pure Data System for Analytics (Netezza)

IBM Pure Data System for Analytics (Netezza)

Similaire à Aginity "Big Data" Research Lab

Aginity Big Data Research Lab

Aginity Big Data Research Lab

Aginity Big Data Research Lab

Map reduce advantages over parallel databases report

Map reduce advantages over parallel databases report

Map reduce advantages over parallel databases report

hadoop seminar training report

hadoop seminar training report

hadoop seminar training report

Big Data using NoSQL Technologies

Big Data using NoSQL Technologies

Big Data using NoSQL Technologies

Big Data

Big data - what, why, where, when and how

Big data - what, why, where, when and how

Big data - what, why, where, when and how

Lecture 5 - Big Data and Hadoop Intro.ppt

Lecture 5 - Big Data and Hadoop Intro.ppt

Lecture 5 - Big Data and Hadoop Intro.ppt

almaraniabwmalk

journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals, yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal

International Journal of Engineering Research and Development (IJERD)

International Journal of Engineering Research and Development (IJERD)

International Journal of Engineering Research and Development (IJERD)

Big data and hadoop ecosystem essentials for managers

Big data and hadoop ecosystem essentials for managers

Big data and hadoop ecosystem essentials for managers

Manjeet Singh Nagi

In large scale datasets, mining frequent itemsets using existing parallel mining algorithm is to balance the load by distributing such enormous data between collections of computers. But we identify high performance issue in existing mining algorithms [1]. To handle this problem, we introduce a new approach called data partitioning using Map Reduce programming model.In our proposed system, we have introduced new technique called frequent itemset ultrametric tree rather than conservative FP-trees. An investigational outcome tells us that, eradicating redundant transaction results in improving the performance by reducing computing loads.

Web Oriented FIM for large scale dataset using Hadoop

Web Oriented FIM for large scale dataset using Hadoop

Web Oriented FIM for large scale dataset using Hadoop

Today, practically every firm uses big data to gain a competitive advantage in the market. With this in mind, freely available big data tools for analysis and processing are a cost-effective and beneficial choice for enterprises. Hadoop is the sector’s leading open-source initiative and big data tidal roller. Moreover, this is not the final chapter! Numerous other businesses pursue Hadoop’s free and open-source path.

Big Data Tools: A Deep Dive into Essential Tools

Big Data Tools: A Deep Dive into Essential Tools

Big Data Tools: A Deep Dive into Essential Tools

Big Data Driven Solutions to Combat Covid' 19

Big Data Driven Solutions to Combat Covid' 19

Big Data Driven Solutions to Combat Covid' 19

Prof.Balakrishnan S

BigData Analysis

BigData Analysis

BigData Analysis

Innfinision Cloud and BigData Solutions

Big data is the biggest challenges as we need huge processing power system and good algorithms to make a decision. We need Hadoop environment with pig hive, machine learning and hadoopecosystem components. The data comes from industries. Many devices around us and sensor, and from social media sites. According to McKinsey There will be a shortage of 15000000 big data professionals by the end of 2020. There are lots of technologies to solve the problem of big data Storage and processing. Such technologies are Apache Hadoop, Apache Spark, Apache Kafka, and many more. Here we analyse the processing speed for the 4GB data on cloudx lab with Hadoop mapreduce with varing mappers and reducers and with pig script and Hive querries and spark environment along with machine learning technology and from the results we can say that machine learning with Hadoop will enhance the processing performance along with with spark, and also we can say that spark is better than Hadoop mapreduce pig and hive, spark with hive and machine learning will be the best performance enhanced compared with pig and hive, Hadoop mapreduce jar.

Performance evaluation of Map-reduce jar pig hive and spark with machine lear...

Performance evaluation of Map-reduce jar pig hive and spark with machine lear...

Performance evaluation of Map-reduce jar pig hive and spark with machine lear...

Big Data Companies and Apache Software

Big Data Companies and Apache Software

Big Data Companies and Apache Software

IRJET - Survey Paper on Map Reduce Processing using HADOOP

IRJET - Survey Paper on Map Reduce Processing using HADOOP

IRJET - Survey Paper on Map Reduce Processing using HADOOP

Big Data with Hadoop – For Data Management, Processing and Storing

Big Data with Hadoop – For Data Management, Processing and Storing

Big Data with Hadoop – For Data Management, Processing and Storing

10-Hot-Data-Analytics-Tre-8904178.ppsx

10-Hot-Data-Analytics-Tre-8904178.ppsx

10-Hot-Data-Analytics-Tre-8904178.ppsx

SangeetaTripathi8

The book of elephant tattoo

The book of elephant tattoo

The book of elephant tattoo

Mankind has stored more than 295 billion gigabytes (or 295 Exabyte) of data since 1986, as per a report by the University of Southern California. Storing and monitoring this data in widely distributed environments for 24/7 is a huge task for global service organizations. These datasets require high processing power which can’t be offered by traditional databases as they are stored in an unstructured format. Although one can use Map Reduce paradigm to solve this problem using java based Hadoop, it cannot provide us with maximum functionality. Drawbacks can be overcome using Hadoop-streaming techniques that allow users to define non-java executable for processing this datasets. This paper proposes a THESAURUS model which allows a faster and easier version of business analysis.

Unstructured Datasets Analysis: Thesaurus Model

Unstructured Datasets Analysis: Thesaurus Model

Unstructured Datasets Analysis: Thesaurus Model

Similaire à Aginity "Big Data" Research Lab (20)

Aginity Big Data Research Lab

Aginity Big Data Research Lab

Aginity Big Data Research Lab

Map reduce advantages over parallel databases report

Map reduce advantages over parallel databases report

Map reduce advantages over parallel databases report

hadoop seminar training report

hadoop seminar training report

hadoop seminar training report

Big Data using NoSQL Technologies

Big Data using NoSQL Technologies

Big Data using NoSQL Technologies

Big Data

Big data - what, why, where, when and how

Big data - what, why, where, when and how

Big data - what, why, where, when and how

Lecture 5 - Big Data and Hadoop Intro.ppt

Lecture 5 - Big Data and Hadoop Intro.ppt

Lecture 5 - Big Data and Hadoop Intro.ppt

International Journal of Engineering Research and Development (IJERD)

International Journal of Engineering Research and Development (IJERD)

International Journal of Engineering Research and Development (IJERD)

Big data and hadoop ecosystem essentials for managers

Big data and hadoop ecosystem essentials for managers

Big data and hadoop ecosystem essentials for managers

Web Oriented FIM for large scale dataset using Hadoop

Web Oriented FIM for large scale dataset using Hadoop

Web Oriented FIM for large scale dataset using Hadoop

Big Data Tools: A Deep Dive into Essential Tools

Big Data Tools: A Deep Dive into Essential Tools

Big Data Tools: A Deep Dive into Essential Tools

Big Data Driven Solutions to Combat Covid' 19

Big Data Driven Solutions to Combat Covid' 19

Big Data Driven Solutions to Combat Covid' 19

BigData Analysis

BigData Analysis

BigData Analysis

Performance evaluation of Map-reduce jar pig hive and spark with machine lear...

Performance evaluation of Map-reduce jar pig hive and spark with machine lear...

Performance evaluation of Map-reduce jar pig hive and spark with machine lear...

Big Data Companies and Apache Software

Big Data Companies and Apache Software

Big Data Companies and Apache Software

IRJET - Survey Paper on Map Reduce Processing using HADOOP

IRJET - Survey Paper on Map Reduce Processing using HADOOP

IRJET - Survey Paper on Map Reduce Processing using HADOOP

Big Data with Hadoop – For Data Management, Processing and Storing

Big Data with Hadoop – For Data Management, Processing and Storing

Big Data with Hadoop – For Data Management, Processing and Storing

10-Hot-Data-Analytics-Tre-8904178.ppsx

10-Hot-Data-Analytics-Tre-8904178.ppsx

10-Hot-Data-Analytics-Tre-8904178.ppsx

The book of elephant tattoo

The book of elephant tattoo

The book of elephant tattoo

Unstructured Datasets Analysis: Thesaurus Model

Unstructured Datasets Analysis: Thesaurus Model

Unstructured Datasets Analysis: Thesaurus Model

Dernier

Corporate and higher education. Two industries that, in the past, have had a clear divide with very little crossover. The difference in goals, learning styles and objectives paved the way for differing learning technologies platforms to evolve. Now, those stark lines are blurring as both sides are discovering they have content that’s relevant to the other. Join Tammy Rutherford as she walks through the pros and cons of corporate and higher ed collaborating. And the challenges of these different technology platforms working together for a brighter future.

Corporate and higher education May webinar.pptx

Corporate and higher education May webinar.pptx

Corporate and higher education May webinar.pptx

Rustici Software

Passkeys: Developing APIs to enable passwordless authentication Cody Salas, Sr Developer Advocate | Solutions Architect - Yubico Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...

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Six Myths about Ontologies: The Basics of Formal Ontology

Six Myths about Ontologies: The Basics of Formal Ontology

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johnbeverley2021

Boost Fertility New Invention Ups Success Rates.pdf

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sudhanshuwaghmare1

Vector Search -An Introduction in Oracle Database 23ai.pptx

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Remote DBA Services

Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

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Join our latest Connector Corner webinar to discover how UiPath Integration Service revolutionizes API-centric automation in a 'Quote to Cash' process—and how that automation empowers businesses to accelerate revenue generation. A comprehensive demo will explore connecting systems, GenAI, and people, through powerful pre-built connectors designed to speed process cycle times. Speakers: James Dickson, Senior Software Engineer Charlie Greenberg, Host, Product Marketing Manager

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

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FWD Group - Insurer Innovation Award 2024

FWD Group - Insurer Innovation Award 2024

FWD Group - Insurer Innovation Award 2024

The Digital Insurer

Architecting Cloud Native Applications

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Understanding the FAA Part 107 License ..

Understanding the FAA Part 107 License ..

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Christopher Logan Kennedy

MS Copilot expands with MS Graph connectors

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MS Copilot expands with MS Graph connectors

Nanddeep Nachan

Dubai, often portrayed as a shimmering oasis in the desert, faces its own set of challenges, including the occasional threat of flooding. Despite its reputation for opulence and modernity, the emirate is not immune to the forces of nature. In recent years, Dubai has experienced sporadic but significant floods, testing the resilience of its infrastructure and communities. Among the critical lifelines in this bustling metropolis is the Dubai International Airport, a bustling hub that connects the city to the world. This article explores the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

Following the popularity of “Cloud Revolution: Exploring the New Wave of Serverless Spatial Data,” we’re thrilled to announce this much-anticipated encore webinar. In this sequel, we’ll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you’re building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

AWS Community Day CPH - Three problems of Terraform

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Andrey Devyatkin

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

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Juan lago vázquez

Tracing the root cause of a performance issue requires a lot of patience, experience, and focus. It’s so hard that we sometimes attempt to guess by trying out tentative fixes, but that usually results in frustration, messy code, and a considerable waste of time and money. This talk explains how to correctly zoom in on a performance bottleneck using three levels of profiling: distributed tracing, metrics, and method profiling. After we learn to read the JVM profiler output as a flame graph, we explore a series of bottlenecks typical for backend systems, like connection/thread pool starvation, invisible aspects, blocking code, hot CPU methods, lock contention, and Virtual Thread pinning, and we learn to trace them even if they occur in library code you are not familiar with. Attend this talk and prepare for the performance issues that will eventually hit any successful system. About authorWith two decades of experience, Victor is a Java Champion working as a trainer for top companies in Europe. Five thousands developers in 120 companies attended his workshops, so he gets to debate every week the challenges that various projects struggle with. In return, Victor summarizes key points from these workshops in conference talks and online meetups for the European Software Crafters, the world’s largest developer community around architecture, refactoring, and testing. Discover how Victor can help you on victorrentea.ro : company training catalog, consultancy and YouTube playlists.

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

The value of a flexible API Management solution for Open Banking Steve Melan, Manager for IT Innovation and Architecture - State's and Saving's Bank of Luxembourg Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - The value of a flexible API Management solution for O...

Apidays New York 2024 - The value of a flexible API Management solution for O...

Apidays New York 2024 - The value of a flexible API Management solution for O...

Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.

Why Teams call analytics are critical to your entire business

Why Teams call analytics are critical to your entire business

Why Teams call analytics are critical to your entire business

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 deployment of external web forms using Jotform for Bonterra Impact Management. This solution can be customized to your organization’s needs and deployed to support the common use cases below: - Intake and consent - Assessments - Surveys - Applications - Program registration Interested in deploying web form automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Jeffrey Haguewood

MINDCTI Revenue Release Quarter One 2024

MINDCTI Revenue Release Quarter One 2024

MINDCTI Revenue Release Quarter One 2024

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Corporate and higher education May webinar.pptx

Corporate and higher education May webinar.pptx

Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...

Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...

Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...

Six Myths about Ontologies: The Basics of Formal Ontology

Six Myths about Ontologies: The Basics of Formal Ontology

Six Myths about Ontologies: The Basics of Formal Ontology

Boost Fertility New Invention Ups Success Rates.pdf

Boost Fertility New Invention Ups Success Rates.pdf

Boost Fertility New Invention Ups Success Rates.pdf

Vector Search -An Introduction in Oracle Database 23ai.pptx

Vector Search -An Introduction in Oracle Database 23ai.pptx

Vector Search -An Introduction in Oracle Database 23ai.pptx

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

FWD Group - Insurer Innovation Award 2024

FWD Group - Insurer Innovation Award 2024

FWD Group - Insurer Innovation Award 2024

Architecting Cloud Native Applications

Architecting Cloud Native Applications

Architecting Cloud Native Applications

Understanding the FAA Part 107 License ..

Understanding the FAA Part 107 License ..

Understanding the FAA Part 107 License ..

MS Copilot expands with MS Graph connectors

MS Copilot expands with MS Graph connectors

MS Copilot expands with MS Graph connectors

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

AWS Community Day CPH - Three problems of Terraform

AWS Community Day CPH - Three problems of Terraform

AWS Community Day CPH - Three problems of Terraform

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Apidays New York 2024 - The value of a flexible API Management solution for O...

Apidays New York 2024 - The value of a flexible API Management solution for O...

Apidays New York 2024 - The value of a flexible API Management solution for O...

Why Teams call analytics are critical to your entire business

Why Teams call analytics are critical to your entire business

Why Teams call analytics are critical to your entire business

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

MINDCTI Revenue Release Quarter One 2024

MINDCTI Revenue Release Quarter One 2024

MINDCTI Revenue Release Quarter One 2024

Aginity "Big Data" Research Lab

1. Introducing Aginity’s“Big Data” Research Lab

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4. The Hardware Parts List and Cost: $5,682.10

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7. Some assembly required – about 11 hours A very ordinary box A partial shipment arrives Pieces parts Fine-tuning the OS Few tools required The assembly line Snapping the front panel in Ready to boot The right driver helps 2/3rds complete All 9 yards….so to speak

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