Database Software Market
•Download as PPTX, PDF•
0 likes•2 views
The database software market is a rapidly evolving industry that provides solutions for managing and organizing data in various formats. Database software is used by organizations of all sizes across multiple sectors to store, retrieve, and manipulate data efficiently and securely.
Report
Share
Report
Share
Recommended
Presentation on Big Data
Big data is large and complex data that cannot be processed by traditional data management tools. It is characterized by high volume, velocity, and variety. Big data comes from many sources and in many formats, including structured, unstructured, and semi-structured data. Storing and processing big data requires specialized systems like Hadoop and NoSQL databases. Big data analytics can provide benefits like improved business decisions and customer satisfaction when applied to areas such as healthcare, security, and manufacturing. However, big data also presents risks regarding privacy, costs, and being overwhelmed by the volume of data.
Kartikey tripathi
This document provides an overview of big data, including its definition, characteristics, sources, tools, applications, risks, benefits and future. Big data is characterized by its volume, velocity and variety. It is generated from sources like users, applications, sensors and more. Tools like Hadoop and databases are used to store, process and analyze big data. Big data analytics can provide benefits across many industries and applications. However, it also poses risks around privacy, costs and skills that must be addressed. The future of big data is promising, with the market expected to grow significantly in the coming years.
Big data
This document provides an overview of big data presented by five individuals. It defines big data, discusses its three key characteristics of volume, velocity and variety. It explains how big data is stored, selected and processed using techniques like Hadoop and MapReduce. Examples of big data sources and tools are provided. Applications of big data across various industries are highlighted. Both the risks and benefits of big data are summarized. The future growth of big data and its impact on IT is also outlined.
Bigdatappt 140225061440-phpapp01
This document provides an overview of big data including:
- It defines big data and describes its three key characteristics: volume, velocity, and variety.
- It explains how big data is stored, selected, and processed using techniques like Hadoop and NoSQL databases.
- It discusses some common sources of big data, tools used to analyze it, and applications of big data analytics across different industries.
Big data
Big data refers to very large data sets that cannot be analyzed using traditional methods. It is characterized by volume, velocity, and variety. The volume of data is growing exponentially from various sources like social media and sensors. This data is generated and processed at high speeds. It also comes in different formats like text, images, videos. Storing and analyzing big data requires different techniques and tools than traditional data due to its scale. It can provide valuable insights when mined properly and has applications in many domains like healthcare, manufacturing, and retail. However, it also poses risks regarding privacy, costs and being overwhelmed by the data.
Content1. Introduction2. What is Big Data3. Characte.docx
Content
1. Introduction
2. What is Big Data
3. Characteristic of Big Data
4. Storing,selecting and processing of Big Data
5. Why Big Data
6. How it is Different
7. Big Data sources
8. Tools used in Big Data
9. Application of Big Data
10. Risks of Big Data
11. Benefits of Big Data
12. How Big Data Impact on IT
13. Future of Big Data
Introduction
• Big Data may well be the Next Big Thing in the IT
world.
• Big data burst upon the scene in the first decade of the
21st century.
• The first organizations to embrace it were online and
startup firms. Firms like Google, eBay, LinkedIn, and
Facebook were built around big data from the
beginning.
• Like many new information technologies, big data can
bring about dramatic cost reductions, substantial
improvements in the time required to perform a
computing task, or new product and service offerings.
• ‘Big Data’ is similar to ‘small data’, but bigger in
size
• but having data bigger it requires different
approaches:
– Techniques, tools and architecture
• an aim to solve new problems or old problems in a
better way
• Big Data generates value from the storage and
processing of very large quantities of digital
information that cannot be analyzed with
traditional computing techniques.
What is BIG DATA?
What is BIG DATA
• Walmart handles more than 1 million customer
transactions every hour.
• Facebook handles 40 billion photos from its user base.
• Decoding the human genome originally took 10years to
process; now it can be achieved in one week.
Three Characteristics of Big Data V3s
Volume
• Data
quantity
Velocity
• Data
Speed
Variety
• Data
Types
1st Character of Big Data
Volume
•A typical PC might have had 10 gigabytes of storage in 2000.
•Today, Facebook ingests 500 terabytes of new data every day.
•Boeing 737 will generate 240 terabytes of flight data during a single
flight across the US.
• The smart phones, the data they create and consume; sensors
embedded into everyday objects will soon result in billions of new,
constantly-updated data feeds containing environmental, location,
and other information, including video.
2nd Character of Big Data
Velocity
• Clickstreams and ad impressions capture user behavior at
millions of events per second
• high-frequency stock trading algorithms reflect market
changes within microseconds
• machine to machine processes exchange data between
billions of devices
• infrastructure and sensors generate massive log data in real-
time
• on-line gaming systems support millions of concurrent
users, each producing multiple inputs per second.
3rd Character of Big Data
Variety
• Big Data isn't just numbers, dates, and strings. Big
Data is also geospatial data, 3D data, audio and
video, and unstructured text, including log files and
social media.
• Traditional database systems were designed to
address smaller volumes of structured data, fewer
updates or a predictable, consistent data stru.
final oracle presentation
Oracle is a leading technology company focused on database software and cloud computing. It generates revenue from software licenses and cloud services. While Oracle faces competition from other large tech companies, its strengths include consulting services, global sales channels, and expertise in data storage and applications. The rise of big data presents both opportunities and challenges for Oracle to leverage new types and volumes of customer information through its products.
Let's make money from big data!
Big data is still relatively new and it is very exciting. The opportunities, if not necessarily endless, are are at least incredibly rich and varied. Aiming to bridge the link between Big Data as a Technology and Big Data as Business Value, we hope our presentation will help frame some of your thinking on how to use and benefit from this topical development.
Recommended
Presentation on Big Data
Big data is large and complex data that cannot be processed by traditional data management tools. It is characterized by high volume, velocity, and variety. Big data comes from many sources and in many formats, including structured, unstructured, and semi-structured data. Storing and processing big data requires specialized systems like Hadoop and NoSQL databases. Big data analytics can provide benefits like improved business decisions and customer satisfaction when applied to areas such as healthcare, security, and manufacturing. However, big data also presents risks regarding privacy, costs, and being overwhelmed by the volume of data.
Kartikey tripathi
This document provides an overview of big data, including its definition, characteristics, sources, tools, applications, risks, benefits and future. Big data is characterized by its volume, velocity and variety. It is generated from sources like users, applications, sensors and more. Tools like Hadoop and databases are used to store, process and analyze big data. Big data analytics can provide benefits across many industries and applications. However, it also poses risks around privacy, costs and skills that must be addressed. The future of big data is promising, with the market expected to grow significantly in the coming years.
Big data
This document provides an overview of big data presented by five individuals. It defines big data, discusses its three key characteristics of volume, velocity and variety. It explains how big data is stored, selected and processed using techniques like Hadoop and MapReduce. Examples of big data sources and tools are provided. Applications of big data across various industries are highlighted. Both the risks and benefits of big data are summarized. The future growth of big data and its impact on IT is also outlined.
Bigdatappt 140225061440-phpapp01
This document provides an overview of big data including:
- It defines big data and describes its three key characteristics: volume, velocity, and variety.
- It explains how big data is stored, selected, and processed using techniques like Hadoop and NoSQL databases.
- It discusses some common sources of big data, tools used to analyze it, and applications of big data analytics across different industries.
Big data
Big data refers to very large data sets that cannot be analyzed using traditional methods. It is characterized by volume, velocity, and variety. The volume of data is growing exponentially from various sources like social media and sensors. This data is generated and processed at high speeds. It also comes in different formats like text, images, videos. Storing and analyzing big data requires different techniques and tools than traditional data due to its scale. It can provide valuable insights when mined properly and has applications in many domains like healthcare, manufacturing, and retail. However, it also poses risks regarding privacy, costs and being overwhelmed by the data.
Content1. Introduction2. What is Big Data3. Characte.docx
Content
1. Introduction
2. What is Big Data
3. Characteristic of Big Data
4. Storing,selecting and processing of Big Data
5. Why Big Data
6. How it is Different
7. Big Data sources
8. Tools used in Big Data
9. Application of Big Data
10. Risks of Big Data
11. Benefits of Big Data
12. How Big Data Impact on IT
13. Future of Big Data
Introduction
• Big Data may well be the Next Big Thing in the IT
world.
• Big data burst upon the scene in the first decade of the
21st century.
• The first organizations to embrace it were online and
startup firms. Firms like Google, eBay, LinkedIn, and
Facebook were built around big data from the
beginning.
• Like many new information technologies, big data can
bring about dramatic cost reductions, substantial
improvements in the time required to perform a
computing task, or new product and service offerings.
• ‘Big Data’ is similar to ‘small data’, but bigger in
size
• but having data bigger it requires different
approaches:
– Techniques, tools and architecture
• an aim to solve new problems or old problems in a
better way
• Big Data generates value from the storage and
processing of very large quantities of digital
information that cannot be analyzed with
traditional computing techniques.
What is BIG DATA?
What is BIG DATA
• Walmart handles more than 1 million customer
transactions every hour.
• Facebook handles 40 billion photos from its user base.
• Decoding the human genome originally took 10years to
process; now it can be achieved in one week.
Three Characteristics of Big Data V3s
Volume
• Data
quantity
Velocity
• Data
Speed
Variety
• Data
Types
1st Character of Big Data
Volume
•A typical PC might have had 10 gigabytes of storage in 2000.
•Today, Facebook ingests 500 terabytes of new data every day.
•Boeing 737 will generate 240 terabytes of flight data during a single
flight across the US.
• The smart phones, the data they create and consume; sensors
embedded into everyday objects will soon result in billions of new,
constantly-updated data feeds containing environmental, location,
and other information, including video.
2nd Character of Big Data
Velocity
• Clickstreams and ad impressions capture user behavior at
millions of events per second
• high-frequency stock trading algorithms reflect market
changes within microseconds
• machine to machine processes exchange data between
billions of devices
• infrastructure and sensors generate massive log data in real-
time
• on-line gaming systems support millions of concurrent
users, each producing multiple inputs per second.
3rd Character of Big Data
Variety
• Big Data isn't just numbers, dates, and strings. Big
Data is also geospatial data, 3D data, audio and
video, and unstructured text, including log files and
social media.
• Traditional database systems were designed to
address smaller volumes of structured data, fewer
updates or a predictable, consistent data stru.
final oracle presentation
Oracle is a leading technology company focused on database software and cloud computing. It generates revenue from software licenses and cloud services. While Oracle faces competition from other large tech companies, its strengths include consulting services, global sales channels, and expertise in data storage and applications. The rise of big data presents both opportunities and challenges for Oracle to leverage new types and volumes of customer information through its products.
Let's make money from big data!
Big data is still relatively new and it is very exciting. The opportunities, if not necessarily endless, are are at least incredibly rich and varied. Aiming to bridge the link between Big Data as a Technology and Big Data as Business Value, we hope our presentation will help frame some of your thinking on how to use and benefit from this topical development.
Big data unit 2
Big data analytics tools from vendors like IBM, Tableau, and SAS can help organizations process and analyze big data. For smaller organizations, Excel is often used, while larger organizations employ data mining, predictive analytics, and dashboards. Business intelligence applications include OLAP, data mining, and decision support systems. Big data comes from many sources like web logs, sensors, social networks, and scientific research. It is defined by the volume, variety, velocity, veracity, variability, and value of the data. Hadoop and MapReduce are common technologies for storing and analyzing big data across clusters of machines. Stream analytics is useful for real-time analysis of data like sensor data.
Big data ppt
This document provides an overview of big data. It defines big data as large volumes of diverse data that are growing rapidly and require new techniques to capture, store, distribute, manage, and analyze. The key characteristics of big data are volume, velocity, and variety. Common sources of big data include sensors, mobile devices, social media, and business transactions. Tools like Hadoop and MapReduce are used to store and process big data across distributed systems. Applications of big data include smarter healthcare, traffic control, and personalized marketing. The future of big data is promising with the market expected to grow substantially in the coming years.
Big data and analytics
1. The document discusses concepts related to big data and analytics, including how the internet of things generates large amounts of data from connected devices.
2. It describes how analytics tools can extract meaningful insights from big data in real-time to help decision making.
3. The document provides an overview of big data characteristics, sources, tools used for analysis, and applications across different industries.
Overview of Bigdata Analytics
This document discusses big data, its key characteristics of volume, velocity, and variety, and how large amounts of diverse data are being generated from various sources like mobile devices, social media, e-commerce, and emails. It explains that big data analytics can provide competitive advantages and better business decisions by examining large datasets. Hadoop and NoSQL databases are approaches for processing and storing large datasets across distributed systems.
IARE_BDBA_ PPT_0.pptx
This document provides an overview of big data and business analytics. It discusses the key characteristics of big data, including volume, variety, and velocity. Volume refers to the enormous and growing amount of data being generated. Variety means data comes in all types from structured to unstructured. Velocity indicates that data is being created in real-time and needs to be analyzed rapidly. The document also outlines some of the challenges of big data and how cloud computing and technologies like Hadoop are helping to manage and analyze large, complex data sets.
CTO Perspectives: What's Next for Data Management and Healthcare?
Health Catalyst's Chief Technology Officer, Bryan Hinton, shares his perspective, thoughts, and insights on new and emerging trends for data management in healthcare. Bryan offers a brief presentation on what hospitals and healthcare systems can expect, followed by an extended Q&A.
ppt final.pptx
This document provides an overview of big data, including its definition, characteristics, sources, tools, applications, risks, benefits and future. Big data is characterized by large volumes of data in various formats that are difficult to process using traditional data management and analysis systems. It is generated from sources like user interactions, sensors and systems logs. Tools like Hadoop and NoSQL databases enable storing, processing and analyzing big data. Organizations apply big data analytics to areas such as healthcare, retail and security. While big data poses privacy and management challenges, it also provides opportunities to gain insights and make improved decisions. The big data industry is growing rapidly and expected to be worth over $100 billion.
Microsoft India - SQL Server The Forrester Wave Enterprise Database Managemen...
Microsoft India - SQL Server The Forrester Wave Enterprise Database Managemen...Microsoft Private Cloud
In Forrester’s 153-criteria evaluation of enterprise open source and closed source database management systems (DBMSes), we found that Oracle, IBM, Microsoft, and Sybase lead the pack because each offers mature, high-performance, scalable, secure, and flexible solutions. It was no surprise to see Oracle dominating in most of the features and functionality such as performance, availability, security, and administration. IBM DB2 for Linux, UNIX, and Windows showed strong support for application and data integration, performance, scalability, and administration, while Microsoft has impressive capabilities for database programmability, application development, administration, and security. Sybase Adaptive Server Enterprise continues to show improvement in its product, offering good support for availability, performance, and administration. IBM Informix Dynamic Server, MySQL, and Ingres came out as Strong Performers, following very closely on the heels of the Leaders and offering very respectable alternatives and a multitude of choices for application developers and architects. PostgreSQL lacks the Leaders’ breadth of features but is a reputable Contender for some use cases Special issues on big data
Research Report on Big Data.
Abstract data,Methods along with example.
Wikepedia is the best example of BIG DATA.
Big_Data_ppt[1] (1).pptx
This document provides an overview of big data including:
- It defines big data and discusses its key characteristics of volume, velocity, and variety.
- It describes sources of big data like social media, sensors, and user clickstreams. Tools for big data include Hadoop, MongoDB, and cloud computing.
- Applications of big data analytics include smarter healthcare, traffic control, and personalized marketing. Risks include privacy and high costs. Benefits include better decisions, opportunities for new businesses, and improved customer experiences.
- The future of big data is strong with worldwide revenues projected to grow from $5 billion in 2012 to over $50 billion in 2017, creating millions of new jobs for data scientists and analysts
bigdataintro.pptx
Big data refers to large, complex datasets that are difficult to process using traditional database management tools. It is characterized by the 3 V's - volume, referring to the large scale of data; variety, referring to different data types; and velocity, referring to the speed at which data is generated and processed. Common sources of big data include social media, sensors, and scientific instruments. Hadoop and Spark are commonly used to process and analyze big data in distributed, parallel systems. Cloud computing provides on-demand access to computing resources and is well-suited for flexible big data applications.
Big-Data-Seminar-6-Aug-2014-Koenig
This document provides an introduction and overview of big data technologies. It begins with defining big data and its key characteristics of volume, variety and velocity. It discusses how data has exploded in recent years and examples of large scale data sources. It then covers popular big data tools and technologies like Hadoop and MapReduce. The document discusses how to get started with big data and learning related skills. Finally, it provides examples of big data projects and discusses the objectives and benefits of working with big data.
1 PSUT Big Data Class, introduction
This document provides an introduction to big data, including definitions and key concepts. It discusses the volume, velocity, and variety of big data sources and challenges in storage and processing large amounts of data from many different sources. Examples of big data applications are given in several domains like healthcare, retail, and social media. Distributed systems and architectures like Hadoop and MapReduce are introduced as approaches to analyze huge volumes of data across multiple servers and storage areas. Challenges in distributed systems and how big data is driving new technologies are also summarized.
Business Insight
This document discusses big data and how organizations can gain insights from data. It notes that by 2015, organizations that have built a modern information system will financially exceed their competitors by 20%. It describes different types of structured and unstructured data organizations are dealing with, including machine-generated data from sensors, satellites, science experiments, videos, and more. It also lists common uses of big data like recommendations, smart meter monitoring, equipment monitoring, advertising analysis, and more. The document then discusses how Microsoft can help users gain better insights through self-service BI, connecting and collaborating in Office 365, and answering questions. It outlines different data sources including non-relational data stored in HDInsight on Azure.
[Webinar] Getting to Insights Faster: A Framework for Agile Big Data
[Webinar] Getting to Insights Faster: A Framework for Agile Big DataInfochimps, a CSC Big Data Business
In this slidedeck, Infochimps Director of Product, Tim Gasper, discusses how Infochimps tackles business problems for customers by deploying a comprehensive Big Data infrastructure in days; sometimes in just hours. Tim unlocks how Infochimps is now taking that same aggressive approach to deliver faster time to value by helping customers develop analytic applications with impeccable speed.Modern Data Challenges require Modern Graph Technology
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
Domino and AWS: collaborative analytics and model governance at financial ser...
The document discusses how financial services firms use analytics for tasks like predictive modeling, validation, pricing, and research. It notes the challenges of legacy systems, collaboration across teams, and reproducibility. It then provides an example of how DBRS, a credit rating agency, uses Domino and AWS for securitization analysis. Models are developed in Jupyter notebooks and governed via a GitHub repository, with analysts interacting through Excel/R Shiny frontends on Domino. This allows for an auditable, scalable, and collaborative workflow while developers maintain control. The document concludes that collaborative platforms like Domino enable subject matter experts to focus on models rather than infrastructure.
Big data by Mithlesh sadh
This document provides an overview of big data, including its definition, characteristics, sources, tools used, applications, benefits, and impact on IT. Big data is a term used to describe the large volumes of data, both structured and unstructured, that are so large they are difficult to process using traditional database and software techniques. It is characterized by high volume, velocity, variety, and veracity. Common sources of big data include mobile devices, sensors, social media, and software/application logs. Tools like Hadoop, MongoDB, and MapReduce are used to store, process, and analyze big data. Key applications areas include homeland security, healthcare, manufacturing, and financial trading. Benefits include better decision making, cost reductions
Neo4j GraphTalks - Einführung in Graphdatenbanken
The document describes an event for GraphTalks Zürich in July 2017. It includes an agenda with presentations on graph databases and Neo4j, visualizing big data sets in the pharmaceutical industry using graph databases, and an open networking session.
Oracle canvas 140604 2
This document discusses Oracle's value proposition for its big data solutions. Key points include:
- Oracle offers engineered systems that integrate hardware and software to securely manage both new and existing data types and formats for big data.
- The solutions allow customers to acquire, organize, analyze and make decisions from big data to develop predictive analytics and gain competitive advantages.
- Oracle partners with other companies through its Oracle Partner Network to increase sales and empower partners with Oracle resources and specializations.
- Oracle solutions serve many customer segments including telecommunications, energy, life sciences, healthcare, oil and gas, manufacturing, and retail.
Unified Threat Management Market Players Gaining Attractive Investments
UTM provides a centralized security solution that integrates firewall, intrusion detection, antivirus, VPN, content filtering, email filtering, web application firewall, and SIEM capabilities. This consolidated approach simplifies management of network security for small and medium businesses. The global UTM market report provides regional breakdowns of North America, Europe, Asia-Pacific, South America, and Middle East/Africa and forecasts market growth from 2023-2029. It is aimed at evaluating market risk and opportunities to support strategic decision making.
Apparel Retail Market Growth Improvement Highly Witness
Apparel retail refers to the business of selling clothing and accessories through various channels like stores, e-commerce, and mobile apps. Key aspects of apparel retail include responding to fashion trends, managing the global supply chain, operating through multiple sales channels, branding and marketing strategies, providing good customer experiences, adopting sustainable practices, and using technology. The apparel industry also experiences seasonal fluctuations and can be analyzed by geographic regions.
More Related Content
Similar to Database Software Market
Big data unit 2
Big data analytics tools from vendors like IBM, Tableau, and SAS can help organizations process and analyze big data. For smaller organizations, Excel is often used, while larger organizations employ data mining, predictive analytics, and dashboards. Business intelligence applications include OLAP, data mining, and decision support systems. Big data comes from many sources like web logs, sensors, social networks, and scientific research. It is defined by the volume, variety, velocity, veracity, variability, and value of the data. Hadoop and MapReduce are common technologies for storing and analyzing big data across clusters of machines. Stream analytics is useful for real-time analysis of data like sensor data.
Big data ppt
This document provides an overview of big data. It defines big data as large volumes of diverse data that are growing rapidly and require new techniques to capture, store, distribute, manage, and analyze. The key characteristics of big data are volume, velocity, and variety. Common sources of big data include sensors, mobile devices, social media, and business transactions. Tools like Hadoop and MapReduce are used to store and process big data across distributed systems. Applications of big data include smarter healthcare, traffic control, and personalized marketing. The future of big data is promising with the market expected to grow substantially in the coming years.
Big data and analytics
1. The document discusses concepts related to big data and analytics, including how the internet of things generates large amounts of data from connected devices.
2. It describes how analytics tools can extract meaningful insights from big data in real-time to help decision making.
3. The document provides an overview of big data characteristics, sources, tools used for analysis, and applications across different industries.
Overview of Bigdata Analytics
This document discusses big data, its key characteristics of volume, velocity, and variety, and how large amounts of diverse data are being generated from various sources like mobile devices, social media, e-commerce, and emails. It explains that big data analytics can provide competitive advantages and better business decisions by examining large datasets. Hadoop and NoSQL databases are approaches for processing and storing large datasets across distributed systems.
IARE_BDBA_ PPT_0.pptx
This document provides an overview of big data and business analytics. It discusses the key characteristics of big data, including volume, variety, and velocity. Volume refers to the enormous and growing amount of data being generated. Variety means data comes in all types from structured to unstructured. Velocity indicates that data is being created in real-time and needs to be analyzed rapidly. The document also outlines some of the challenges of big data and how cloud computing and technologies like Hadoop are helping to manage and analyze large, complex data sets.
CTO Perspectives: What's Next for Data Management and Healthcare?
Health Catalyst's Chief Technology Officer, Bryan Hinton, shares his perspective, thoughts, and insights on new and emerging trends for data management in healthcare. Bryan offers a brief presentation on what hospitals and healthcare systems can expect, followed by an extended Q&A.
ppt final.pptx
This document provides an overview of big data, including its definition, characteristics, sources, tools, applications, risks, benefits and future. Big data is characterized by large volumes of data in various formats that are difficult to process using traditional data management and analysis systems. It is generated from sources like user interactions, sensors and systems logs. Tools like Hadoop and NoSQL databases enable storing, processing and analyzing big data. Organizations apply big data analytics to areas such as healthcare, retail and security. While big data poses privacy and management challenges, it also provides opportunities to gain insights and make improved decisions. The big data industry is growing rapidly and expected to be worth over $100 billion.
Microsoft India - SQL Server The Forrester Wave Enterprise Database Managemen...
Microsoft India - SQL Server The Forrester Wave Enterprise Database Managemen...Microsoft Private Cloud
In Forrester’s 153-criteria evaluation of enterprise open source and closed source database management systems (DBMSes), we found that Oracle, IBM, Microsoft, and Sybase lead the pack because each offers mature, high-performance, scalable, secure, and flexible solutions. It was no surprise to see Oracle dominating in most of the features and functionality such as performance, availability, security, and administration. IBM DB2 for Linux, UNIX, and Windows showed strong support for application and data integration, performance, scalability, and administration, while Microsoft has impressive capabilities for database programmability, application development, administration, and security. Sybase Adaptive Server Enterprise continues to show improvement in its product, offering good support for availability, performance, and administration. IBM Informix Dynamic Server, MySQL, and Ingres came out as Strong Performers, following very closely on the heels of the Leaders and offering very respectable alternatives and a multitude of choices for application developers and architects. PostgreSQL lacks the Leaders’ breadth of features but is a reputable Contender for some use cases Special issues on big data
Research Report on Big Data.
Abstract data,Methods along with example.
Wikepedia is the best example of BIG DATA.
Big_Data_ppt[1] (1).pptx
This document provides an overview of big data including:
- It defines big data and discusses its key characteristics of volume, velocity, and variety.
- It describes sources of big data like social media, sensors, and user clickstreams. Tools for big data include Hadoop, MongoDB, and cloud computing.
- Applications of big data analytics include smarter healthcare, traffic control, and personalized marketing. Risks include privacy and high costs. Benefits include better decisions, opportunities for new businesses, and improved customer experiences.
- The future of big data is strong with worldwide revenues projected to grow from $5 billion in 2012 to over $50 billion in 2017, creating millions of new jobs for data scientists and analysts
bigdataintro.pptx
Big data refers to large, complex datasets that are difficult to process using traditional database management tools. It is characterized by the 3 V's - volume, referring to the large scale of data; variety, referring to different data types; and velocity, referring to the speed at which data is generated and processed. Common sources of big data include social media, sensors, and scientific instruments. Hadoop and Spark are commonly used to process and analyze big data in distributed, parallel systems. Cloud computing provides on-demand access to computing resources and is well-suited for flexible big data applications.
Big-Data-Seminar-6-Aug-2014-Koenig
This document provides an introduction and overview of big data technologies. It begins with defining big data and its key characteristics of volume, variety and velocity. It discusses how data has exploded in recent years and examples of large scale data sources. It then covers popular big data tools and technologies like Hadoop and MapReduce. The document discusses how to get started with big data and learning related skills. Finally, it provides examples of big data projects and discusses the objectives and benefits of working with big data.
1 PSUT Big Data Class, introduction
This document provides an introduction to big data, including definitions and key concepts. It discusses the volume, velocity, and variety of big data sources and challenges in storage and processing large amounts of data from many different sources. Examples of big data applications are given in several domains like healthcare, retail, and social media. Distributed systems and architectures like Hadoop and MapReduce are introduced as approaches to analyze huge volumes of data across multiple servers and storage areas. Challenges in distributed systems and how big data is driving new technologies are also summarized.
Business Insight
This document discusses big data and how organizations can gain insights from data. It notes that by 2015, organizations that have built a modern information system will financially exceed their competitors by 20%. It describes different types of structured and unstructured data organizations are dealing with, including machine-generated data from sensors, satellites, science experiments, videos, and more. It also lists common uses of big data like recommendations, smart meter monitoring, equipment monitoring, advertising analysis, and more. The document then discusses how Microsoft can help users gain better insights through self-service BI, connecting and collaborating in Office 365, and answering questions. It outlines different data sources including non-relational data stored in HDInsight on Azure.
[Webinar] Getting to Insights Faster: A Framework for Agile Big Data
[Webinar] Getting to Insights Faster: A Framework for Agile Big DataInfochimps, a CSC Big Data Business
In this slidedeck, Infochimps Director of Product, Tim Gasper, discusses how Infochimps tackles business problems for customers by deploying a comprehensive Big Data infrastructure in days; sometimes in just hours. Tim unlocks how Infochimps is now taking that same aggressive approach to deliver faster time to value by helping customers develop analytic applications with impeccable speed.Modern Data Challenges require Modern Graph Technology
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
Domino and AWS: collaborative analytics and model governance at financial ser...
The document discusses how financial services firms use analytics for tasks like predictive modeling, validation, pricing, and research. It notes the challenges of legacy systems, collaboration across teams, and reproducibility. It then provides an example of how DBRS, a credit rating agency, uses Domino and AWS for securitization analysis. Models are developed in Jupyter notebooks and governed via a GitHub repository, with analysts interacting through Excel/R Shiny frontends on Domino. This allows for an auditable, scalable, and collaborative workflow while developers maintain control. The document concludes that collaborative platforms like Domino enable subject matter experts to focus on models rather than infrastructure.
Big data by Mithlesh sadh
This document provides an overview of big data, including its definition, characteristics, sources, tools used, applications, benefits, and impact on IT. Big data is a term used to describe the large volumes of data, both structured and unstructured, that are so large they are difficult to process using traditional database and software techniques. It is characterized by high volume, velocity, variety, and veracity. Common sources of big data include mobile devices, sensors, social media, and software/application logs. Tools like Hadoop, MongoDB, and MapReduce are used to store, process, and analyze big data. Key applications areas include homeland security, healthcare, manufacturing, and financial trading. Benefits include better decision making, cost reductions
Neo4j GraphTalks - Einführung in Graphdatenbanken
The document describes an event for GraphTalks Zürich in July 2017. It includes an agenda with presentations on graph databases and Neo4j, visualizing big data sets in the pharmaceutical industry using graph databases, and an open networking session.
Oracle canvas 140604 2
This document discusses Oracle's value proposition for its big data solutions. Key points include:
- Oracle offers engineered systems that integrate hardware and software to securely manage both new and existing data types and formats for big data.
- The solutions allow customers to acquire, organize, analyze and make decisions from big data to develop predictive analytics and gain competitive advantages.
- Oracle partners with other companies through its Oracle Partner Network to increase sales and empower partners with Oracle resources and specializations.
- Oracle solutions serve many customer segments including telecommunications, energy, life sciences, healthcare, oil and gas, manufacturing, and retail.
Similar to Database Software Market (20)
CTO Perspectives: What's Next for Data Management and Healthcare?
CTO Perspectives: What's Next for Data Management and Healthcare?
Microsoft India - SQL Server The Forrester Wave Enterprise Database Managemen...
Microsoft India - SQL Server The Forrester Wave Enterprise Database Managemen...
[Webinar] Getting to Insights Faster: A Framework for Agile Big Data
[Webinar] Getting to Insights Faster: A Framework for Agile Big Data
Modern Data Challenges require Modern Graph Technology
Modern Data Challenges require Modern Graph Technology
Domino and AWS: collaborative analytics and model governance at financial ser...
Domino and AWS: collaborative analytics and model governance at financial ser...
More from KaustubhBhandari6
Unified Threat Management Market Players Gaining Attractive Investments
UTM provides a centralized security solution that integrates firewall, intrusion detection, antivirus, VPN, content filtering, email filtering, web application firewall, and SIEM capabilities. This consolidated approach simplifies management of network security for small and medium businesses. The global UTM market report provides regional breakdowns of North America, Europe, Asia-Pacific, South America, and Middle East/Africa and forecasts market growth from 2023-2029. It is aimed at evaluating market risk and opportunities to support strategic decision making.
Apparel Retail Market Growth Improvement Highly Witness
Apparel retail refers to the business of selling clothing and accessories through various channels like stores, e-commerce, and mobile apps. Key aspects of apparel retail include responding to fashion trends, managing the global supply chain, operating through multiple sales channels, branding and marketing strategies, providing good customer experiences, adopting sustainable practices, and using technology. The apparel industry also experiences seasonal fluctuations and can be analyzed by geographic regions.
Sodium Nitrate Market
Sodium nitrate has several uses including as a food preservative, fertilizer, in pyrotechnics and fireworks, glass manufacturing, and as a heat transfer fluid. It provides benefits such as preventing bacterial growth in meats, supplying nitrogen as a plant nutrient, acting as an oxidizer in explosives and fireworks, lowering the melting temperature of glass, and storing thermal energy from the sun. Sodium nitrate is also used to produce soda-lime glass and was historically used in explosives like gunpowder by providing oxygen for combustion, though it has been replaced in modern applications by more stable compounds.
Spreadsheet Software Market
The document discusses spreadsheet software and the expanding market. Spreadsheet software allows users to organize and analyze tabular data using formulas and functions. Key features include grids, data entry, visualization, sorting, and collaboration. Popular examples are Microsoft Excel, Google Sheets, LibreOffice Calc, and Apple Numbers. The spreadsheet software market is segmented by region and is projected to grow robustly led by major players.
Foodtech Market
This document discusses the foodtech market and its continued potential for outperformance. It defines foodtech as the intersection of food and technology, using technology to improve various aspects of the food industry. Some key areas of foodtech discussed include using technology to enhance food production, processing, delivery, safety, waste reduction, nutrition, alternative proteins, smart kitchen appliances, and restaurant operations. The foodtech market aims to make the food system more efficient, sustainable, and consumer-friendly while addressing issues like food security, safety, and sustainability.
Soft Skills Training Market
Soft skills training refers to developing interpersonal attributes that enable effective work and relationships. It can benefit career and personal growth. Common soft skills include communication, emotional intelligence, teamwork, problem solving, adaptability, leadership, time management, conflict resolution, networking, and relationship building. Soft skills training involves workshops, courses, coaching and activities to practice these skills, important for teamwork, leadership and work outcomes. Investing in soft skills training supports personal and professional development.
Armored Vehicle Market
An armored vehicle is a motorized vehicle designed to withstand various threats through enhanced armor protection. These vehicles are used by militaries, law enforcement, and security firms to transport people and cargo through high-risk areas. Key features include ballistic armor, blast protection, weapons systems, run-flat tires, sealed windows, communication systems, and enhanced suspension to handle added weight. Armored vehicles play crucial roles in military operations, peacekeeping, VIP protection, and secure transport of goods in dangerous regions. The specific design and capabilities vary based on intended use and required protection level.
Helicopter Blades Market.pdf
Helicopter blades are key components that enable helicopters to fly. They generate lift and thrust by rotating. Main rotor blades lift the helicopter and provide maneuverability, while tail rotor blades control yaw. Blades are made of lightweight strong materials and precisely designed with airfoils, twist, and adjustable pitch for flight control. The rotor head attaches blades and allows flapping, lead-lag, and feathering movements. By tilting blades collectively or individually, pilots control lift, attitude, and direction. Regular maintenance is needed to ensure blade integrity and safety. The size and speed of blades determine a helicopter's lifting capacity and performance. The helicopter blades market is growing, with investors seeking segments like North America, Europe, and Asia
Optical Fiber Cable Market.pdf
The document discusses optical fiber cable, which transmits data in the form of light signals through thin strands of glass or plastic. It notes that fiber optic cable is critical to modern telecommunications and data infrastructure. Key points covered include the fiber optic core and cladding layer, how light is used for transmission instead of electrical signals, the types of fiber cables, and the benefits of high bandwidth, low latency, immunity to EMI, and security. Applications discussed include telecommunications, data centers, cable television, and military/aerospace. The document concludes that fiber optic cable offers advantages over copper and will continue playing a crucial role in supporting technologies like 5G and IoT due to the growing demand for high-speed data transmission.
Armored Vehicle Market
An armored vehicle is designed with enhanced protection to withstand attacks like bullets, explosives, or chemical agents. These vehicles are used by militaries, law enforcement, and security companies. Key features include ballistic armor, blast protection, weapons systems, run-flat tires, sealed windows, communication systems, and enhanced suspension. Armored vehicles play crucial roles in military operations, peacekeeping, VIP protection, and secure transportation in high-risk areas. Their design and capabilities vary based on intended use and required protection level.
Soft Skills Training Market
Soft skills training refers to developing interpersonal attributes like communication, emotional intelligence, teamwork, problem solving, and adaptability. These skills are important for career and personal success. Common skills trained are communication, emotional intelligence, teamwork, problem solving, leadership, time management, and conflict resolution. Soft skills training takes various forms like workshops, courses, and coaching. It is growing in importance as employers seek candidates with strong soft skills. Investing in soft skills can benefit personal and professional development.
Dehydration Monitoring Systems Market.pdf
Dehydration monitoring systems track an individual's hydration levels through wearable sensors, smart water bottles, fitness trackers, and other technologies. These systems are important for healthcare settings and those at risk of dehydration. Common monitoring methods include hydration sensors, smart water bottles, fitness trackers, urine color charts, bioimpedance analysis, remote patient monitoring systems, mobile apps, and smart hydration bottles. Dehydration monitoring helps maintain good health by keeping hydration levels in check and preventing issues ranging from mild discomfort to serious medical problems.
Data Historian Market.pdf
The document discusses the data historian market and how it is likely to boost future growth by 2030. It defines a data historian as specialized software that collects, stores, and manages time-series data from various sources. Key features of data historians include data collection, time-series data storage, high performance, compression and aggregation, tagging and annotation, and query and analysis capabilities. Popular data historian software includes OSIsoft PI System, Wonderware Historian, and GE Digital Historian. The data historian market is segmented by region and is projected to experience growth between 2023-2029.
Large Wind Turbine Market
Large wind turbines are a type of renewable energy technology used to generate significant amounts of electricity, typically for power grids or large-scale industrial operations. They can have rotor diameters up to 165 meters and tower heights over 100 meters. Key components include the tower, nacelle containing critical components like the generator and gearbox, and blades that efficiently capture wind energy. Large wind turbines can generate 2-8 MW of power or more, depending on wind speed and location, and are often installed offshore or in wind farms where winds are stronger and more consistent. They play an important role in transitioning to renewable energy and reducing reliance on fossil fuels.
Network Management Market.pdf
The document discusses network management, which refers to administering and maintaining a computer network infrastructure. It involves monitoring network devices, configuring settings, identifying and resolving issues, optimizing performance, implementing security measures, backing up configurations, documenting the network, managing changes, and allowing remote management. Effective network management requires technical skills, networking knowledge, and tools to maintain reliability, security, and performance of modern networks. The network management market report would analyze the market by regions including North America, Europe, Asia-Pacific, South America, and the Middle East and Africa.
Gps Trackers Market
The document discusses the GPS tracker market and its expected revolutionary growth. It describes how GPS trackers work by receiving satellite signals to determine precise location data. It then outlines several common applications of GPS trackers, including vehicle tracking, personal tracking, asset tracking, fitness/outdoor activities, child/elderly safety, and wildlife/environmental research. The document also mentions geofencing capabilities and use of GPS in aviation/navigation. It notes privacy considerations with individual tracking and varying laws by jurisdiction.
Baby diaper bag Market
This document discusses the key features and essential items commonly found in baby diaper bags. It notes that diaper bags come in various sizes and designs to hold diapers, wipes, bottles, snacks, and other baby essentials. The document also lists examples of items that parents typically pack, such as diapers, wipes, creams, clothes, toys, and sanitization products. Additionally, it recommends regularly checking and restocking the diaper bag based on the baby's needs and age.
Patient Lift Accessories
The patient lift accessories market focuses on developing, manufacturing, and distributing accessories that enhance safety and convenience when transferring patients with limited mobility. These accessories are used with patient lift systems and include slings, transfer belts, transfer boards, and scale systems. The global patient lift accessories market covers regions such as North America, Europe, Asia-Pacific, South America, and the Middle East and Africa.
Recycled Paper Market
The recycled paper market is expected to experience strong growth by 2030. Recycled paper is made from fibers that were previously used in other paper products. The recycling process involves collecting, sorting, shredding, cleaning, refining, and papermaking to transform used paper materials into new recycled paper sheets. Using recycled paper promotes sustainability and reduces the environmental impact of paper production compared to using only virgin fibers. The global recycled paper market covers major regions including North America, Europe, Asia-Pacific, South America, and the Middle East and Africa.
Bio Vanillin Market
Bio vanillin refers to vanillin produced through biological processes rather than chemical synthesis. There are two primary sources of bio vanillin: natural extraction from vanilla beans and bioconversion from other natural compounds like ferulic acid. Bio vanillin has gained popularity due to its natural origin and potentially more sustainable production methods compared to synthetic vanillin. As consumers increasingly demand natural and sustainable products, the bio vanillin market is expected to see continued growth in the coming years.
More from KaustubhBhandari6 (20)
Unified Threat Management Market Players Gaining Attractive Investments
Unified Threat Management Market Players Gaining Attractive Investments
Apparel Retail Market Growth Improvement Highly Witness
Apparel Retail Market Growth Improvement Highly Witness
Recently uploaded
Choosing The Best AWS Service For Your Website + API.pptx
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Northern Engraving | Nameplate Manufacturing Process - 2024
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Essentials of Automations: Exploring Attributes & Automation Parameters
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
What is an RPA CoE? Session 1 – CoE Vision
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Skybuffer SAM4U tool for SAP license adoption
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Mutation Testing for Task-Oriented Chatbots
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
Tomaz Bratanic
Graph ML and GenAI Expert - Neo4j
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Artificial Intelligence and Electronic Warfare
Artificial Intelligence and Electronic WarfarePapadakis K.-Cyber-Information Warfare Analyst & Cyber Defense/Security Consultant-Hellenic MoD
AI & Electronic WarfareDandelion Hashtable: beyond billion requests per second on a commodity server
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Dmitrii Kamaev, PhD
Senior Product Owner - QIAGEN
Harnessing the Power of NLP and Knowledge Graphs for Opioid Research
Gursev Pirge, PhD
Senior Data Scientist - JohnSnowLabs
Recently uploaded (20)
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
Northern Engraving | Nameplate Manufacturing Process - 2024
Northern Engraving | Nameplate Manufacturing Process - 2024
Essentials of Automations: Exploring Attributes & Automation Parameters
Essentials of Automations: Exploring Attributes & Automation Parameters
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
Dandelion Hashtable: beyond billion requests per second on a commodity server
Dandelion Hashtable: beyond billion requests per second on a commodity server
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Harnessing the Power of NLP and Knowledge Graphs for Opioid Research
Harnessing the Power of NLP and Knowledge Graphs for Opioid Research
Database Software Market
- 1. Database Software Market is Booming Worldwide with Latest Rising Trends
- 2. • The database software market is a competitive industry that focuses on developing and providing software solutions for managing and organizing data. Database software is essential for businesses and organizations to store, retrieve, and manipulate data efficiently and securely.
- 3. • Here are some key points about the database software market: • Market Overview: The market for database software has experienced significant growth over the years due to the increasing volume and complexity of data generated by businesses. The demand for efficient data management and analysis tools has been a driving force behind the market's expansion. • Types of Database Software: There are various types of database software available, including relational databases, NoSQL databases, cloud databases, graph databases, and more. Each type has its own strengths and use cases, catering to different data management needs. • Major Players: The market is dominated by several established companies and open-source projects. Some of the prominent players in the database software market include Oracle Corporation, Microsoft Corporation, IBM Corporation, SAP SE, Amazon Web Services (AWS), and MongoDB Inc. • Open-Source Solutions: Open-source database software, such as MySQL, PostgreSQL, and MongoDB, have gained popularity due to their flexibility, cost-effectiveness, and active community support. These solutions are widely adopted by small and medium-sized businesses, as well as large enterprises.
- 4. • Cloud-Based Solutions: With the rise of cloud computing, many organizations are opting for cloud-based database solutions. Providers like AWS, Microsoft Azure, and Google Cloud offer managed database services, allowing businesses to store and access their data in the cloud, reducing infrastructure costs and improving scalability. • Big Data and Analytics: The growing adoption of big data technologies has driven the demand for advanced database software capable of handling large volumes of data and supporting real-time analytics. Solutions like Hadoop, Apache Spark, and distributed databases have become essential for organizations dealing with massive data sets. • Security and Compliance: Data security and compliance regulations, such as GDPR and CCPA, have made it crucial for businesses to choose database software that provides robust security features and ensures data privacy. Encryption, access controls, and audit logs are among the security measures offered by modern database solutions.
- 5. • Database Software market Key Players • Smith & Nephew Plc. market • Blue Belt Technologies, Inc. (United States) • Intuitive Surgical (United States) • Auris Surgical Robotics, Inc. (Hansen Medical Inc.) (United States) • KUKA AG (Germany) • Stryker Corporation (MAKO Surgical Corp.) (United States) • Mazor Robotics (Israel) • Renishaw plc (United Kingdom) • Medtronic plc (United States) • Zimmer Biomet Holdings Inc. (United States) • THINK Surgical Inc. (United States)
- 6. • It's important to note that market dynamics and trends can evolve rapidly, so it's advisable to consult the latest reports and research for the most up-to-date information on the database software market. • https://www.htfmarketintelligence.com/report /global-database-software-market
- 7. • Contact Us : Craig Francis (PR & Marketing Manager) HTF Market Intelligence Consulting Private Limited Phone: +1 434 322 0091 sales@htfmarketintelligence.com Connect with us at LinkedIn | Facebook | Twitter