5 Key Data Management Trends of 2022 as observed by a data practitioner. Covers trends on data architecture, data storage, data platforms, and data operations.
Top 10 guidelines for deploying modern data architecture for the data driven ...LindaWatson19
Enterprises are facing a new revolution, powered by the rapid adoption of data analytics with modern technologies like machine learning and artificial intelligence (A).
BRIDGING DATA SILOS USING BIG DATA INTEGRATIONijmnct
With cloud computing, cheap storage and technology advancements, an enterprise uses multiple
applications to operate business functions. Applications are not limited to just transactions, customer
service, sales, finance but they also include security, application logs, marketing, engineering, operations,
HR and many more. Each business vertical uses multiple applications which generate a huge amount of
data. On top of that, social media, IoT sensors, SaaS solutions, and mobile applications record exponential
growth in data volume. In almost all enterprises, data silos exist through these applications. These
applications can produce structured, semi-structured, or unstructured data at different velocity and in
different volume. Having all data sources integrated and generating timely insights helps in overall
decision making. With recent development in Big Data Integration, data silos can be managed better and it
can generate tremendous value for enterprises. Big data integration offers flexibility, speed and scalability
for integrating large data sources. It also offers tools to generate analytical insights which can help
stakeholders to make effective decisions. This paper presents the overview on data silos, challenges with
data silos and how big data integration can help to stun them.
Cloud Data Management: The Future of Data Storage and ManagementFredReynolds2
Data is the essence of any business. It provides the organization, its people, and its customer’s timely and historical decision support. Data management’s importance must be considered. To maximize the benefits of cloud data management, businesses must first establish a mechanism for separating master data from other data types. Due diligence is required when choosing a data management platform and a data management system. Here, the potential of Cloud based Data Management emerges, enhancing the significance of these decisions.
Hybrid Cloud - Key Benefits & Must Have RequirementsJohn Katrick
Hybrid Cloud - Key Benefits & Must Have Requirements : Gartner says by 2020, a corporate "No-Cloud" Policy will be as rare as a "No-Internet" policy is today as per this article and specifically the Infrastructure as a Service (IaaS) market is projected to continue to grow more than 25 percent per year through 2019. This surge in cloud adoption also represents a huge shift in cloud spending by IT organizations, directly or indirectly affecting more than $1 trillion dollars in Cloud IT purchases by 2020, according to Gartner.
AGILE CLOUD: MANAGING THE EMPOWERED USERdinCloud Inc.
As important as the back end technologies of virtual data center operations, application virtualization
and orchestration may be, a substantial portion of cloud’s ‘magic’ is its ability to provide new forms of
support to line-of-business staff, providing advanced automation options for functions that were
previously poorly automated or entirely manual.
Enterprises have been embracing hybrid IT infrastructures that combine on-premises and public cloud capabilities, and more and more enterprises are expected to adopt this strategy in the future. But, is it right for your business?
Find out if hybrid cloud services are a winning fit for your company: https://www.oneneck.com/cloud/hybrid-it-a-winning-strategy
Top 10 guidelines for deploying modern data architecture for the data driven ...LindaWatson19
Enterprises are facing a new revolution, powered by the rapid adoption of data analytics with modern technologies like machine learning and artificial intelligence (A).
BRIDGING DATA SILOS USING BIG DATA INTEGRATIONijmnct
With cloud computing, cheap storage and technology advancements, an enterprise uses multiple
applications to operate business functions. Applications are not limited to just transactions, customer
service, sales, finance but they also include security, application logs, marketing, engineering, operations,
HR and many more. Each business vertical uses multiple applications which generate a huge amount of
data. On top of that, social media, IoT sensors, SaaS solutions, and mobile applications record exponential
growth in data volume. In almost all enterprises, data silos exist through these applications. These
applications can produce structured, semi-structured, or unstructured data at different velocity and in
different volume. Having all data sources integrated and generating timely insights helps in overall
decision making. With recent development in Big Data Integration, data silos can be managed better and it
can generate tremendous value for enterprises. Big data integration offers flexibility, speed and scalability
for integrating large data sources. It also offers tools to generate analytical insights which can help
stakeholders to make effective decisions. This paper presents the overview on data silos, challenges with
data silos and how big data integration can help to stun them.
Cloud Data Management: The Future of Data Storage and ManagementFredReynolds2
Data is the essence of any business. It provides the organization, its people, and its customer’s timely and historical decision support. Data management’s importance must be considered. To maximize the benefits of cloud data management, businesses must first establish a mechanism for separating master data from other data types. Due diligence is required when choosing a data management platform and a data management system. Here, the potential of Cloud based Data Management emerges, enhancing the significance of these decisions.
Hybrid Cloud - Key Benefits & Must Have RequirementsJohn Katrick
Hybrid Cloud - Key Benefits & Must Have Requirements : Gartner says by 2020, a corporate "No-Cloud" Policy will be as rare as a "No-Internet" policy is today as per this article and specifically the Infrastructure as a Service (IaaS) market is projected to continue to grow more than 25 percent per year through 2019. This surge in cloud adoption also represents a huge shift in cloud spending by IT organizations, directly or indirectly affecting more than $1 trillion dollars in Cloud IT purchases by 2020, according to Gartner.
AGILE CLOUD: MANAGING THE EMPOWERED USERdinCloud Inc.
As important as the back end technologies of virtual data center operations, application virtualization
and orchestration may be, a substantial portion of cloud’s ‘magic’ is its ability to provide new forms of
support to line-of-business staff, providing advanced automation options for functions that were
previously poorly automated or entirely manual.
Enterprises have been embracing hybrid IT infrastructures that combine on-premises and public cloud capabilities, and more and more enterprises are expected to adopt this strategy in the future. But, is it right for your business?
Find out if hybrid cloud services are a winning fit for your company: https://www.oneneck.com/cloud/hybrid-it-a-winning-strategy
No Code is the Future of Digital Transformation - UDPaaS White Paper - Nov 20...KarinaArzubiaga
White paper about no code / low code capable platforms and their benefits. Specifically, our Universal Design Platform as Service (UDPaaS) cloud-based product.
IDC Study on Enterprise Hybrid Cloud StrategiesEMC
White Paper discussing IDC Survey of over 650 enterprise IT decision makers that was designed to understand the evolution of the cloud across world’s largest IT organizations.
Data Systems Integration & Business Value Pt. 2: CloudDATAVERSITY
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Data Systems Integration & Business Value Pt. 2: CloudData Blueprint
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Many organizations are modifying their IT portfolios to fully take advantage of the benefits of cloud computing. While the motivation is specific and focuses on broad-based challenges, all organizations are prepared to benefit from aspects of the cloud. This is accomplished by ensuring that cloud-hosted data share three attributes. Cloud-hosted datasets must be of:
Higher quality data than those data residing outside of the cloud;
Lower volume (1/5 the size of data collections) than similar collections residing outside of the cloud; and
Increased share-ability than data residing outside the cloud.
Increases in capacity utilization, improved IT flexibility and responsiveness, as well as the forecast decreases in cost accruing to cloud-based computing are all possible after these first three conditions have been met. Necessary investments in data engineering can help organizations to save even more money by reducing the amount of resources required to perform their duties and increasing the effectiveness of their duties and decision-making. This webinar will show you how to recognize the opportunities, ‘size up’ the required investment, and properly supervise your efforts to take advantage of the opportunities presented by the cloud.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
NFRASTRUCTURE MODERNIZATION REVIEW
Analyze the issues
Hardware
Over-running volume of data is a problem that should be addressed by data management and storage management. Data is being constantly collected but poorly analyzed which leads to excessive amounts of data occupying storage and delay in operations which inevitably affect production, sales and profits. If this remains unresolved, current data may have to be moved to external storage and recovered if needed. There is also the risk of data not being encoded into computers and thus will remain in manual state. This can be a case of redundant or extraneous data that is not yet cleaned and normalized by operations managers with the guidance of IT. This situation is known as data overload where companies actually use only a fraction of the data they capture and store. Many companies simply hoard data to make sure that they are readily available when they are needed. This negatively impacts the Corporation when assessing data relevance, accuracies and timeliness (Marr, 2016).
Software
The Largo Corporation (LC) seems to running on an enterprise resource planning system that is probably as long as 20 years old. Initially, LC has had success with the old system because they were able to establish themselves in various industries such as healthcare, media, government, etc. But due to various concerns, the Corporation is currently running on an outdated system because it is unable to provide services that keeps the Corporation a float. The LC is losing revenue and customers. Complete data without analysis is invaluable because, no information and insights can be produced that will support decisions. Customer data should lead to the best marketing and sales campaigns. The Corporation needs to recognize its weaknesses and implement changes to their software by incorporating funding for a new system that is reliable, secure, and has the ability to run on integrated systems; all of which will streamline data organization and analysis for the enterprise. (Rouse, n.d).
Network/Telecommunications
The network that was built in the 1980’s has become slow and unreliable affecting business operations. The problems caused by the old network are; lack of integration and communication between departments affecting the work flow, supply vs. demand, and inability to analyze data to carry out these operations. The Corporation should have taken into consideration the growth of the company by expanding and upgrading their networks along with their services. They should also take into consideration the number of departments, the number of users and their skill level, storage and bandwidth, and budget (Rasmussen, 2011). The current network does not allow employees to connect on their mobile devices which restricts flexibility and places limitations on productivity and portability.
Management
The responses of both IT and the business group are both juxtaposed against e ...
Cloud Analytics Ability to Design, Build, Secure, and Maintain Analytics Solu...YogeshIJTSRD
Cloud Analytics is another area in the IT field where different services like Software, Infrastructure, storage etc. are offered as services online. Users of cloud services are under constant fear of data loss, security threats, and availability issues. However, the major challenge in these methods is obtaining real time and unbiased datasets. Many datasets are internal and cannot be shared due to privacy issues or may lack certain statistical characteristics. As a result of this, researchers prefer to generate datasets for training and testing purposes in simulated or closed experimental environments which may lack comprehensiveness. Advances in sensor technology, the Internet of things IoT , social networking, wireless communications, and huge collection of data from years have all contributed to a new field of study Big Data is discussed in this paper. Through this analysis and investigation, we provide recommendations for the research public on future directions on providing data based decisions for cloud supported Big Data computing and analytic solutions. This paper concentrates upon the recent trends in Big Data storage and analysing, in the clouds, and also points out the security limitations. Rajan Ramvilas Saroj "Cloud Analytics: Ability to Design, Build, Secure, and Maintain Analytics Solutions on the Cloud" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd43728.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/43728/cloud-analytics-ability-to-design-build-secure-and-maintain-analytics-solutions-on-the-cloud/rajan-ramvilas-saroj
Get ahead of the cloud or get left behindMatt Mandich
An enterprise cloud computing strategy results in:
Broad consensus on goals and expected results of moving select processes to the cloud
Standardized, consistent approach to evaluating the benefits and challenges of cloud projects
Clear requirements for the negotiation and monitoring of partnerships with cloud service providers
Understanding and consensus on the enabling and managing role IT will play in future cloud initiatives
Goals and a roadmap for transforming internal IT from asset managers to service broker
leewayhertz.com-AI in Master Data Management MDM Pioneering next-generation d...KristiLBurns
Master data refers to the critical, core data within an enterprise that is essential for conducting business operations and making informed decisions. This data encompasses vital information about the primary entities around which business transactions revolve and generally changes infrequently. Master data is not transactional but rather plays a key role in defining and guiding transactions.
Is there anything that can double the advantages of hybrid cloud hosting without requiring heavy IT investment? Yes, there is. Effective resource allocation and cost management can help improve hybrid cloud benefits. Read to know how.
Analyst Webinar: Discover how a logical data fabric helps organizations avoid...Denodo
Watch full webinar here: https://bit.ly/3zVUXWp
In this webinar, we’ll be tackling the question of where our data is and how we can avoid it falling into a black hole.
We’ll examine how data blackholes and silos come to be and the challenges these pose to organisations. We will also look at the impact of data silos as organisations adopt more complex multi-cloud setups. Finally, we will discuss the opportunities a logical data fabric poses to assist organisations to avoid data silos and manage data in a centrally governed and controlled environment.
Join us and Barc’s Jacqueline Bloemen on this webinar to get the answer and further insights on how to better avoid falling into a #datablackhole. Hope to see you connected!
Running head Database and Data Warehousing design1Database and.docxhealdkathaleen
Running head: Database and Data Warehousing design 1
Database and Data Warehousing Design 3
Database and Data Warehousing Design
Thien Thai
CIS599
Professor Wade M. Poole
Strayer University
Feb 20, 2020
Database and Data Warehousing Design
Introduction
Technology has highly revolutionized the world of business –hence presenting more challenges and opportunities for businesses. Companies which fail to embrace and incorporate technology in their operations risks being edged out of the market due to stiff competition witnessed in the market today. On the flipside, cloud-based technology allows businesses to “easily retrieve and store valuable data about their customers, products, and employees.” Data is an important component that help to support core business decisions. In today’s highly competitive and constantly evolving business world, embracing cloud-based technology business managers an opportunity to make informed and result-oriented decisions regarding day-to-day organizational operations (Dimitriu & Matei, 2015).
Notably, business growth and competitiveness depends on its ability to transform data into information. Data warehousing and adoption of relational databases are some of cloud-based technologies which have positively impacted on businesses. The two technologies have had a strategic value to companies –helping them to have the extra edge over their competitors. Both data warehousing and relational databases help businesses to “take smart decisions in a smarter manner.” However, failure to adopt these cloud-based technologies has hindered business executives’ ability to make experienced-based and fact-based decisions which are vital to business survival. Both “databases and data warehouses are relational data systems” which serve different and equally crucial roles within an organization. For instance, data warehousing helps to support management decisions while relational databases help to perform ongoing business transactions in real-time. Basically, embracing cloud-based technologies within the organization will help to give the company a competitive advantage in the market. However, the adoption and maintenance of such technologies require full support and endorsement of the business management. Organizational management must understand the feasibility, functionality, and the importance of embracing such technologies. Movement towards relational databases and data warehousing requires a lot of funding –hence the need to convince the management to support and fund them. This paper seeks to explore the concepts of data warehousing, relational databases, their importance to the business, as whey as their design.
“Importance of Data Warehousing and Relational Databases”
Today, technology has changed the market landscape. Business are striving to adopt cloud-based technology in order to improve efficiency in business functions –among them analytical queries as well as transactional operations. Both relational databases a ...
Running head Database and Data Warehousing design1Database and.docxtodd271
Running head: Database and Data Warehousing design 1
Database and Data Warehousing Design 3
Database and Data Warehousing Design
Thien Thai
CIS599
Professor Wade M. Poole
Strayer University
Feb 20, 2020
Database and Data Warehousing Design
Introduction
Technology has highly revolutionized the world of business –hence presenting more challenges and opportunities for businesses. Companies which fail to embrace and incorporate technology in their operations risks being edged out of the market due to stiff competition witnessed in the market today. On the flipside, cloud-based technology allows businesses to “easily retrieve and store valuable data about their customers, products, and employees.” Data is an important component that help to support core business decisions. In today’s highly competitive and constantly evolving business world, embracing cloud-based technology business managers an opportunity to make informed and result-oriented decisions regarding day-to-day organizational operations (Dimitriu & Matei, 2015).
Notably, business growth and competitiveness depends on its ability to transform data into information. Data warehousing and adoption of relational databases are some of cloud-based technologies which have positively impacted on businesses. The two technologies have had a strategic value to companies –helping them to have the extra edge over their competitors. Both data warehousing and relational databases help businesses to “take smart decisions in a smarter manner.” However, failure to adopt these cloud-based technologies has hindered business executives’ ability to make experienced-based and fact-based decisions which are vital to business survival. Both “databases and data warehouses are relational data systems” which serve different and equally crucial roles within an organization. For instance, data warehousing helps to support management decisions while relational databases help to perform ongoing business transactions in real-time. Basically, embracing cloud-based technologies within the organization will help to give the company a competitive advantage in the market. However, the adoption and maintenance of such technologies require full support and endorsement of the business management. Organizational management must understand the feasibility, functionality, and the importance of embracing such technologies. Movement towards relational databases and data warehousing requires a lot of funding –hence the need to convince the management to support and fund them. This paper seeks to explore the concepts of data warehousing, relational databases, their importance to the business, as whey as their design.
“Importance of Data Warehousing and Relational Databases”
Today, technology has changed the market landscape. Business are striving to adopt cloud-based technology in order to improve efficiency in business functions –among them analytical queries as well as transactional operations. Both relational databases a.
All business sizes can benefit from better use of their data to gain insights, how the cloud can help overcome common data challenges and accelerate transformation with the cloud technology
https://www.rapyder.com/cloud-data-analytics-services/
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaCloudera, Inc.
Transitioning to a Big Data architecture is a big step; and the complexity of moving existing analytical services onto modern platforms like Cloudera, can seem overwhelming.
No Code is the Future of Digital Transformation - UDPaaS White Paper - Nov 20...KarinaArzubiaga
White paper about no code / low code capable platforms and their benefits. Specifically, our Universal Design Platform as Service (UDPaaS) cloud-based product.
IDC Study on Enterprise Hybrid Cloud StrategiesEMC
White Paper discussing IDC Survey of over 650 enterprise IT decision makers that was designed to understand the evolution of the cloud across world’s largest IT organizations.
Data Systems Integration & Business Value Pt. 2: CloudDATAVERSITY
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Data Systems Integration & Business Value Pt. 2: CloudData Blueprint
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Many organizations are modifying their IT portfolios to fully take advantage of the benefits of cloud computing. While the motivation is specific and focuses on broad-based challenges, all organizations are prepared to benefit from aspects of the cloud. This is accomplished by ensuring that cloud-hosted data share three attributes. Cloud-hosted datasets must be of:
Higher quality data than those data residing outside of the cloud;
Lower volume (1/5 the size of data collections) than similar collections residing outside of the cloud; and
Increased share-ability than data residing outside the cloud.
Increases in capacity utilization, improved IT flexibility and responsiveness, as well as the forecast decreases in cost accruing to cloud-based computing are all possible after these first three conditions have been met. Necessary investments in data engineering can help organizations to save even more money by reducing the amount of resources required to perform their duties and increasing the effectiveness of their duties and decision-making. This webinar will show you how to recognize the opportunities, ‘size up’ the required investment, and properly supervise your efforts to take advantage of the opportunities presented by the cloud.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
NFRASTRUCTURE MODERNIZATION REVIEW
Analyze the issues
Hardware
Over-running volume of data is a problem that should be addressed by data management and storage management. Data is being constantly collected but poorly analyzed which leads to excessive amounts of data occupying storage and delay in operations which inevitably affect production, sales and profits. If this remains unresolved, current data may have to be moved to external storage and recovered if needed. There is also the risk of data not being encoded into computers and thus will remain in manual state. This can be a case of redundant or extraneous data that is not yet cleaned and normalized by operations managers with the guidance of IT. This situation is known as data overload where companies actually use only a fraction of the data they capture and store. Many companies simply hoard data to make sure that they are readily available when they are needed. This negatively impacts the Corporation when assessing data relevance, accuracies and timeliness (Marr, 2016).
Software
The Largo Corporation (LC) seems to running on an enterprise resource planning system that is probably as long as 20 years old. Initially, LC has had success with the old system because they were able to establish themselves in various industries such as healthcare, media, government, etc. But due to various concerns, the Corporation is currently running on an outdated system because it is unable to provide services that keeps the Corporation a float. The LC is losing revenue and customers. Complete data without analysis is invaluable because, no information and insights can be produced that will support decisions. Customer data should lead to the best marketing and sales campaigns. The Corporation needs to recognize its weaknesses and implement changes to their software by incorporating funding for a new system that is reliable, secure, and has the ability to run on integrated systems; all of which will streamline data organization and analysis for the enterprise. (Rouse, n.d).
Network/Telecommunications
The network that was built in the 1980’s has become slow and unreliable affecting business operations. The problems caused by the old network are; lack of integration and communication between departments affecting the work flow, supply vs. demand, and inability to analyze data to carry out these operations. The Corporation should have taken into consideration the growth of the company by expanding and upgrading their networks along with their services. They should also take into consideration the number of departments, the number of users and their skill level, storage and bandwidth, and budget (Rasmussen, 2011). The current network does not allow employees to connect on their mobile devices which restricts flexibility and places limitations on productivity and portability.
Management
The responses of both IT and the business group are both juxtaposed against e ...
Cloud Analytics Ability to Design, Build, Secure, and Maintain Analytics Solu...YogeshIJTSRD
Cloud Analytics is another area in the IT field where different services like Software, Infrastructure, storage etc. are offered as services online. Users of cloud services are under constant fear of data loss, security threats, and availability issues. However, the major challenge in these methods is obtaining real time and unbiased datasets. Many datasets are internal and cannot be shared due to privacy issues or may lack certain statistical characteristics. As a result of this, researchers prefer to generate datasets for training and testing purposes in simulated or closed experimental environments which may lack comprehensiveness. Advances in sensor technology, the Internet of things IoT , social networking, wireless communications, and huge collection of data from years have all contributed to a new field of study Big Data is discussed in this paper. Through this analysis and investigation, we provide recommendations for the research public on future directions on providing data based decisions for cloud supported Big Data computing and analytic solutions. This paper concentrates upon the recent trends in Big Data storage and analysing, in the clouds, and also points out the security limitations. Rajan Ramvilas Saroj "Cloud Analytics: Ability to Design, Build, Secure, and Maintain Analytics Solutions on the Cloud" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd43728.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/43728/cloud-analytics-ability-to-design-build-secure-and-maintain-analytics-solutions-on-the-cloud/rajan-ramvilas-saroj
Get ahead of the cloud or get left behindMatt Mandich
An enterprise cloud computing strategy results in:
Broad consensus on goals and expected results of moving select processes to the cloud
Standardized, consistent approach to evaluating the benefits and challenges of cloud projects
Clear requirements for the negotiation and monitoring of partnerships with cloud service providers
Understanding and consensus on the enabling and managing role IT will play in future cloud initiatives
Goals and a roadmap for transforming internal IT from asset managers to service broker
leewayhertz.com-AI in Master Data Management MDM Pioneering next-generation d...KristiLBurns
Master data refers to the critical, core data within an enterprise that is essential for conducting business operations and making informed decisions. This data encompasses vital information about the primary entities around which business transactions revolve and generally changes infrequently. Master data is not transactional but rather plays a key role in defining and guiding transactions.
Is there anything that can double the advantages of hybrid cloud hosting without requiring heavy IT investment? Yes, there is. Effective resource allocation and cost management can help improve hybrid cloud benefits. Read to know how.
Analyst Webinar: Discover how a logical data fabric helps organizations avoid...Denodo
Watch full webinar here: https://bit.ly/3zVUXWp
In this webinar, we’ll be tackling the question of where our data is and how we can avoid it falling into a black hole.
We’ll examine how data blackholes and silos come to be and the challenges these pose to organisations. We will also look at the impact of data silos as organisations adopt more complex multi-cloud setups. Finally, we will discuss the opportunities a logical data fabric poses to assist organisations to avoid data silos and manage data in a centrally governed and controlled environment.
Join us and Barc’s Jacqueline Bloemen on this webinar to get the answer and further insights on how to better avoid falling into a #datablackhole. Hope to see you connected!
Running head Database and Data Warehousing design1Database and.docxhealdkathaleen
Running head: Database and Data Warehousing design 1
Database and Data Warehousing Design 3
Database and Data Warehousing Design
Thien Thai
CIS599
Professor Wade M. Poole
Strayer University
Feb 20, 2020
Database and Data Warehousing Design
Introduction
Technology has highly revolutionized the world of business –hence presenting more challenges and opportunities for businesses. Companies which fail to embrace and incorporate technology in their operations risks being edged out of the market due to stiff competition witnessed in the market today. On the flipside, cloud-based technology allows businesses to “easily retrieve and store valuable data about their customers, products, and employees.” Data is an important component that help to support core business decisions. In today’s highly competitive and constantly evolving business world, embracing cloud-based technology business managers an opportunity to make informed and result-oriented decisions regarding day-to-day organizational operations (Dimitriu & Matei, 2015).
Notably, business growth and competitiveness depends on its ability to transform data into information. Data warehousing and adoption of relational databases are some of cloud-based technologies which have positively impacted on businesses. The two technologies have had a strategic value to companies –helping them to have the extra edge over their competitors. Both data warehousing and relational databases help businesses to “take smart decisions in a smarter manner.” However, failure to adopt these cloud-based technologies has hindered business executives’ ability to make experienced-based and fact-based decisions which are vital to business survival. Both “databases and data warehouses are relational data systems” which serve different and equally crucial roles within an organization. For instance, data warehousing helps to support management decisions while relational databases help to perform ongoing business transactions in real-time. Basically, embracing cloud-based technologies within the organization will help to give the company a competitive advantage in the market. However, the adoption and maintenance of such technologies require full support and endorsement of the business management. Organizational management must understand the feasibility, functionality, and the importance of embracing such technologies. Movement towards relational databases and data warehousing requires a lot of funding –hence the need to convince the management to support and fund them. This paper seeks to explore the concepts of data warehousing, relational databases, their importance to the business, as whey as their design.
“Importance of Data Warehousing and Relational Databases”
Today, technology has changed the market landscape. Business are striving to adopt cloud-based technology in order to improve efficiency in business functions –among them analytical queries as well as transactional operations. Both relational databases a ...
Running head Database and Data Warehousing design1Database and.docxtodd271
Running head: Database and Data Warehousing design 1
Database and Data Warehousing Design 3
Database and Data Warehousing Design
Thien Thai
CIS599
Professor Wade M. Poole
Strayer University
Feb 20, 2020
Database and Data Warehousing Design
Introduction
Technology has highly revolutionized the world of business –hence presenting more challenges and opportunities for businesses. Companies which fail to embrace and incorporate technology in their operations risks being edged out of the market due to stiff competition witnessed in the market today. On the flipside, cloud-based technology allows businesses to “easily retrieve and store valuable data about their customers, products, and employees.” Data is an important component that help to support core business decisions. In today’s highly competitive and constantly evolving business world, embracing cloud-based technology business managers an opportunity to make informed and result-oriented decisions regarding day-to-day organizational operations (Dimitriu & Matei, 2015).
Notably, business growth and competitiveness depends on its ability to transform data into information. Data warehousing and adoption of relational databases are some of cloud-based technologies which have positively impacted on businesses. The two technologies have had a strategic value to companies –helping them to have the extra edge over their competitors. Both data warehousing and relational databases help businesses to “take smart decisions in a smarter manner.” However, failure to adopt these cloud-based technologies has hindered business executives’ ability to make experienced-based and fact-based decisions which are vital to business survival. Both “databases and data warehouses are relational data systems” which serve different and equally crucial roles within an organization. For instance, data warehousing helps to support management decisions while relational databases help to perform ongoing business transactions in real-time. Basically, embracing cloud-based technologies within the organization will help to give the company a competitive advantage in the market. However, the adoption and maintenance of such technologies require full support and endorsement of the business management. Organizational management must understand the feasibility, functionality, and the importance of embracing such technologies. Movement towards relational databases and data warehousing requires a lot of funding –hence the need to convince the management to support and fund them. This paper seeks to explore the concepts of data warehousing, relational databases, their importance to the business, as whey as their design.
“Importance of Data Warehousing and Relational Databases”
Today, technology has changed the market landscape. Business are striving to adopt cloud-based technology in order to improve efficiency in business functions –among them analytical queries as well as transactional operations. Both relational databases a.
All business sizes can benefit from better use of their data to gain insights, how the cloud can help overcome common data challenges and accelerate transformation with the cloud technology
https://www.rapyder.com/cloud-data-analytics-services/
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaCloudera, Inc.
Transitioning to a Big Data architecture is a big step; and the complexity of moving existing analytical services onto modern platforms like Cloudera, can seem overwhelming.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. The dream of creating a
centralized “single
source of truth” has
remained just that, a
dream!
Shailendra Mruthyunjayappa
https://www.linkedin.com/in/mruthyunjayappa/
1 Implementing a stable and structured enterprise data repository,
especially in a dynamic and competitive industry, is an impossibility
due to: (a) divergence in design approach, data architecture,
ownership, security and usage patterns of the operational layer and
analytical layer; (b) continuously changing, and therefore,
continuously failing integration between the operational and
analytical layers.
On the one hand, building and maintaining a monolithic data
warehouse is infeasible since there are too many moving parts in
the system. On the other hand, creating a pure data lake requires
retaining huge volumes of raw data files and building further data
layers, making it cumbersome for data users to automate use cases.
New decentralized/ distributed/ federated concepts such as data
fabric, data mesh and data lakehouse are taking off, which can help:
(a) weave together data from multiple distributed/ interconnected
sources; (b) adopt a hybrid cloud-based architecture; (c) avoid
multiple data hops and complex integrations; (d) enable federated
data governance; and(e) stay agile and focus on effective and
efficient data management.
The new generation of data leaders are being more practical,
idealism is giving way to pragmatism, and the data management
world is moving from big data concepts to distributed-governed
data design.
3. Cloud adoption is
growing quickly, but
there are a lot of “ifs &
buts”
Shailendra Mruthyunjayappa
https://www.linkedin.com/in/mruthyunjayappa/
2 For over a decade, cloud has been touted to offer significant
benefits to enterprises, including cost savings, security, scalability,
redundancy, business continuity, maintainability, etc. These benefits
are not fully realized, and many limitations and challenges are yet to
be solved.
Current limitations and challenges include: (a) insufficient visibility
into cloud usage and performance, leading to difficulty in budgeting
and justifying cost-performance tradeoff; (b) readiness/
compatibility of applications on cloud platforms; (c) constraints in
real-time integrations across multiple environments; (d) risks of
data loss/ leakage, data theft, cybersecurity, etc.; (e) compliance to
privacy requirements and data governance; (f) increased external
dependency risk.
Some of the challenges listed above are amplified further for
enterprise data management and data driven applications.
Emergence of hybrid cloud architecture (that gives the ability to
combine applications on public cloud, private cloud and on-
premises), third-party multi-cloud monitoring tools (that makes it
possible to optimize and control cloud spending), and cloud data
management platforms (to collect, manage, govern, and use
enterprise data in a hybrid cloud environment) are helping
overcome some of these limitations, but are yet mature.
4. Data governance has
graduated from theory
to practice
Shailendra Mruthyunjayappa
https://www.linkedin.com/in/mruthyunjayappa/
3 Every aspiring data driven organization understands the importance
of data governance. Most of them embarked on enterprise data
governance programs, and for over 2 decades have realized little
benefits. The main reason for the struggle has been the
misalignment of objectives between the data governance program
leaders and end users. Data governance programs focused on
hypothetical benefits of centralized structure, control and
compliance, without delivering any value to end users.
A good data governance initiative should be built on user
empowerment and efficiency goals. Data governance should be a
practical framework based on a broad system of rules, and
processes for consistent usage of data, with clear accountability of
data ownership, quality, usage and security.
Of late, we are seeing organizations define data governance
objectives that focus on efficiency and facilitation of end use cases
with emphasis on line-of-business ownership and standardized
usage of data. The introduction of data regulations and the added
complexity of overlaying external data has pushed data governance
programs to be more agile and responsive to user, industry and
regulatory requirements.
Data governance success can be found by embracing data silos, and
tooling for distributed data management and shared data
governance.
5. The potential of AI/ML
in data management is
yet to be realized
Shailendra Mruthyunjayappa
https://www.linkedin.com/in/mruthyunjayappa/
4 When someone refers to AI/ML, you imagine very fancy use cases
such as lifting business growth, sophisticated user engagement,
advanced risk assessment and management, optimization of
productivity, etc. While these use cases get the most attention from
stakeholders and deliver noticeable impact to the business, several
potential applications of AI/ML in the data management domain are
overlooked.
AI/ML can be used to automate low level tasks and augment
complex tasks. Automation of low-level tasks could in the form of
anomaly detection, rectification in the case of recurring data issues,
monitoring and tuning for data volumes and workload balancing.
Augmentation of complex tasks can include exception management,
master data management, data cataloging, data governance, data
risks and controls.
Several of these AI/ML use cases can be built internally with small
investment and benefits can be quickly realized as part of ongoing
operations. Once a use case is proven on a particular dataset or
workflow, the same can be standardized and gradually rolled out
enterprise-wide.
There are several new age technologies such as data observability
platforms and data curation platforms that have embedded AI/ML
capabilities, which can help quickly bridge the gaps in the enterprise
data management framework.
6. DataOps is very real
and within reach of
enterprises
Shailendra Mruthyunjayappa
https://www.linkedin.com/in/mruthyunjayappa/
5 DevOps, which started as a movement to streamline the software
development and operations teams with aligned objectives, went
mainstream within the space of a few years and has become the
prevalent methodology for software development and maintenance.
The principles of DevOps and Agile methodologies applied to data
management, commonly referred to as DataOps, is fast catching up
within the community.
DataOps is intended to help organizations meet business and
regulatory requirements quickly. DataOps helps reduce the end-to-
end cycle time of analytics and insights by breaking down and
iterating on ideation, design, data sourcing, data preparation,
analysis, and deployment, ensuring high quality data and usable
insights are delivered to business users swiftly.
Whether there is a formalized DataOps setup or not, data analytics
teams have always come under pressure to deliver quick results to
the business users. By implementing DataOps, organizations can
create an environment where processing time is reduced from days
to hours and hours to minutes, and bring focus to the highest
priority requirements.
DataOps leads to a responsive data analytics team and create an
edge for the business. The benefits are real and tangible.
7. For more such insights
follow me:
Shailendra Mruthyunjayappa
https://www.linkedin.com/in/mruthyunjayappa/