Evolving Big Data Strategies: Bringing Data Lake and Data Mesh Vision to LifeSG Analytics
The new data technologies, along with legacy infrastructure, are driving market-driven innovations like personalized offers, real-time alerts, and predictive maintenance. However, these technical additions - ranging from data lakes to analytics platforms to stream processing and data mesh —have increased the complexity of data architectures. They are significantly hampering the ongoing ability of an organization to deliver new capabilities while ensuring the integrity of artificial intelligence (AI) models. https://us.sganalytics.com/blog/evolving-big-data-strategies-with-data-lakehouses-and-data-mesh/
Top 60+ Data Warehouse Interview Questions and Answers.pdfDatacademy.ai
This is a comprehensive guide to the most frequently asked data warehouse interview questions and answers. It covers a wide range of topics including data warehousing concepts, ETL processes, dimensional modeling, data storage, and more. The guide aims to assist job seekers, students, and professionals in preparing for data warehouse job interviews and exams.
This article useful for anyone who want to introduce with Big Data and how oracle architecture Big Data solution using Oracle Big Data Cloud solutions .
Evolving Big Data Strategies: Bringing Data Lake and Data Mesh Vision to LifeSG Analytics
The new data technologies, along with legacy infrastructure, are driving market-driven innovations like personalized offers, real-time alerts, and predictive maintenance. However, these technical additions - ranging from data lakes to analytics platforms to stream processing and data mesh —have increased the complexity of data architectures. They are significantly hampering the ongoing ability of an organization to deliver new capabilities while ensuring the integrity of artificial intelligence (AI) models. https://us.sganalytics.com/blog/evolving-big-data-strategies-with-data-lakehouses-and-data-mesh/
Top 60+ Data Warehouse Interview Questions and Answers.pdfDatacademy.ai
This is a comprehensive guide to the most frequently asked data warehouse interview questions and answers. It covers a wide range of topics including data warehousing concepts, ETL processes, dimensional modeling, data storage, and more. The guide aims to assist job seekers, students, and professionals in preparing for data warehouse job interviews and exams.
This article useful for anyone who want to introduce with Big Data and how oracle architecture Big Data solution using Oracle Big Data Cloud solutions .
Detailed slides of data resource management. The relationships among the many individual data elements stored in databases are based on one of several logical data structures, or models.
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
Unlock Your Data for ML & AI using Data VirtualizationDenodo
How Denodo Complement’s Logical Data Lake in Cloud
● Denodo does not substitute data warehouses, data lakes,
ETLs...
● Denodo enables the use of all together plus other data
sources
○ In a logical data warehouse
○ In a logical data lake
○ They are very similar, the only difference is in the main
objective
● There are also use cases where Denodo can be used as data
source in a ETL flow
Top Big data Analytics tools: Emerging trends and Best practicesSpringPeople
For many IT experts, big data analytics tools and technologies are now a top priority. Let's find out the top big data analytics tools in this slide to initialize and advance the process of big data analysis.
Myth Busters VII: I’m building a data mesh, so I don’t need data virtualizationDenodo
Watch full webinar here: https://bit.ly/3DBA4EP
A data mesh architecture offers a lot of promise to change the way we manage data – and for the better. But there’s a lot of confusion about a data mesh. People will tell you that you can build a data mesh on top of a data lake or on top of a data warehouse, and that you don’t need data virtualization to build a data mesh.
Many vendors are jumping on to the data mesh bandwagon and are claiming that they inherently support a data mesh architecture. But do they? How much of this is hype versus reality? Is it true that you don’t need data virtualization to build a scalable, enterprise-grade data mesh?
This is the myth we will attempt to bust in this next Myth Busters webinar.
Watch this session on-demand to learn about the concepts and components of a data mesh, and hear how the logical approach to data management and integration – powered by data virtualization - is critical for a data mesh.
Data protection and privacy regulations such as the EU’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Singapore’s Personal Data Protection Act (PDPA) have been major drivers for data governance initiatives and the emergence of data catalog solutions. Organizations have an ever-increasing appetite to leverage their data for business advantage, either through internal collaboration, data sharing across ecosystems, direct commercialization, or as the basis for AI-driven business decision-making. This requires data governance and especially data asset catalog solutions to step up once again and enable data-driven businesses to leverage their data responsibly, ethically, compliantly, and accountably.
This presentation explores how data catalog has become a key technology enabler in overcoming these challenges.
The project is to ask college related queries and get the responses through a chatbot an Artificial Conversational Entity. This System is a web application which provides answer to the query of the student. Students just have to query through the bot which is used for chatting. Students can chat using any format there is no specific format the user has to follow. This system helps the student to be updated about the college activities.
Discussion post· The proper implementation of a database is es.docxmadlynplamondon
Discussion post
· The proper implementation of a database is essential to the success of the data performance functions of an organization. Identify and evaluate at least three considerations that one must plan for when designing a database.
· Suggest at least two types of databases that would be useful for small businesses, two types for regional level organizations and two types for international companies. Include your rationale for each suggestion.
LP’s post states the following:Top of Form
Question:
The proper implementation of a database is essential to the success of the data performance functions of an organization. Identify and evaluate at least three considerations that one must plan for when designing a database.
Answer:
Planning is the most significant aspect of database design, and here is where most projects for database design will fail because the database does not meet requirements, does not meet expectations, or are just unmanageable. Here you need to be forward-thinking by planning for the future. What information needs to be stored or what things or entities do we need to store information about (Knauff, 2004)? What questions will we need to ask of the database (Knauff, 2004)?
A well-designed database promotes consistent data entry and retrieval and reduces the existence of duplication among the database tables. Relational database tables work together to ensure that the correct data is available when you need it.
The first consideration should be what is the database’s intended purpose. Understanding the purpose will help define the need. Some examples might be “to keep a list of customers,” “to manage inventory,” or “to grade students (Filemaker Staff, n.d.).” All stakeholders need to be involved in this process.
Second is Data integrity. Is the data accurate, consistent, and complete? What kind of categories does the data align with? Identifying these categories is critical to designing an efficient database because different types and amounts of data in each category will be stored. Some example categories might be sales that track “customers,” “products,” and “invoices,” or grades that track “students,” “classes,” and “assignments (Filemaker Staff, n.d.).” Once the categories have been defined the relations can be determined. A good exercise to help with this is to write these out in simple sentences:
“customers order products” and “invoices record customers’ orders.”
Now the organization of the data can begin. The categories above can be used as tables so common data can be grouped.
The third is security. Is the database secure? Will the current policy and rules be sufficient going forward? Who should have access? Who should have access to which tables (Nield, 2016)? Read-only access? Write access? Is this database critical to business operations (Nield, 2016)? What are the D&R plans?
Excessive security creates excessive red tape and obstructs agility, but insufficient security will invite catastrophe (Nield, 2016 ...
Detailed slides of data resource management. The relationships among the many individual data elements stored in databases are based on one of several logical data structures, or models.
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
Unlock Your Data for ML & AI using Data VirtualizationDenodo
How Denodo Complement’s Logical Data Lake in Cloud
● Denodo does not substitute data warehouses, data lakes,
ETLs...
● Denodo enables the use of all together plus other data
sources
○ In a logical data warehouse
○ In a logical data lake
○ They are very similar, the only difference is in the main
objective
● There are also use cases where Denodo can be used as data
source in a ETL flow
Top Big data Analytics tools: Emerging trends and Best practicesSpringPeople
For many IT experts, big data analytics tools and technologies are now a top priority. Let's find out the top big data analytics tools in this slide to initialize and advance the process of big data analysis.
Myth Busters VII: I’m building a data mesh, so I don’t need data virtualizationDenodo
Watch full webinar here: https://bit.ly/3DBA4EP
A data mesh architecture offers a lot of promise to change the way we manage data – and for the better. But there’s a lot of confusion about a data mesh. People will tell you that you can build a data mesh on top of a data lake or on top of a data warehouse, and that you don’t need data virtualization to build a data mesh.
Many vendors are jumping on to the data mesh bandwagon and are claiming that they inherently support a data mesh architecture. But do they? How much of this is hype versus reality? Is it true that you don’t need data virtualization to build a scalable, enterprise-grade data mesh?
This is the myth we will attempt to bust in this next Myth Busters webinar.
Watch this session on-demand to learn about the concepts and components of a data mesh, and hear how the logical approach to data management and integration – powered by data virtualization - is critical for a data mesh.
Data protection and privacy regulations such as the EU’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Singapore’s Personal Data Protection Act (PDPA) have been major drivers for data governance initiatives and the emergence of data catalog solutions. Organizations have an ever-increasing appetite to leverage their data for business advantage, either through internal collaboration, data sharing across ecosystems, direct commercialization, or as the basis for AI-driven business decision-making. This requires data governance and especially data asset catalog solutions to step up once again and enable data-driven businesses to leverage their data responsibly, ethically, compliantly, and accountably.
This presentation explores how data catalog has become a key technology enabler in overcoming these challenges.
The project is to ask college related queries and get the responses through a chatbot an Artificial Conversational Entity. This System is a web application which provides answer to the query of the student. Students just have to query through the bot which is used for chatting. Students can chat using any format there is no specific format the user has to follow. This system helps the student to be updated about the college activities.
Discussion post· The proper implementation of a database is es.docxmadlynplamondon
Discussion post
· The proper implementation of a database is essential to the success of the data performance functions of an organization. Identify and evaluate at least three considerations that one must plan for when designing a database.
· Suggest at least two types of databases that would be useful for small businesses, two types for regional level organizations and two types for international companies. Include your rationale for each suggestion.
LP’s post states the following:Top of Form
Question:
The proper implementation of a database is essential to the success of the data performance functions of an organization. Identify and evaluate at least three considerations that one must plan for when designing a database.
Answer:
Planning is the most significant aspect of database design, and here is where most projects for database design will fail because the database does not meet requirements, does not meet expectations, or are just unmanageable. Here you need to be forward-thinking by planning for the future. What information needs to be stored or what things or entities do we need to store information about (Knauff, 2004)? What questions will we need to ask of the database (Knauff, 2004)?
A well-designed database promotes consistent data entry and retrieval and reduces the existence of duplication among the database tables. Relational database tables work together to ensure that the correct data is available when you need it.
The first consideration should be what is the database’s intended purpose. Understanding the purpose will help define the need. Some examples might be “to keep a list of customers,” “to manage inventory,” or “to grade students (Filemaker Staff, n.d.).” All stakeholders need to be involved in this process.
Second is Data integrity. Is the data accurate, consistent, and complete? What kind of categories does the data align with? Identifying these categories is critical to designing an efficient database because different types and amounts of data in each category will be stored. Some example categories might be sales that track “customers,” “products,” and “invoices,” or grades that track “students,” “classes,” and “assignments (Filemaker Staff, n.d.).” Once the categories have been defined the relations can be determined. A good exercise to help with this is to write these out in simple sentences:
“customers order products” and “invoices record customers’ orders.”
Now the organization of the data can begin. The categories above can be used as tables so common data can be grouped.
The third is security. Is the database secure? Will the current policy and rules be sufficient going forward? Who should have access? Who should have access to which tables (Nield, 2016)? Read-only access? Write access? Is this database critical to business operations (Nield, 2016)? What are the D&R plans?
Excessive security creates excessive red tape and obstructs agility, but insufficient security will invite catastrophe (Nield, 2016 ...
The Internet of Things (IoT) is a revolutionary concept that connects everyday objects and devices to the internet, enabling them to communicate, collect, and exchange data. Imagine a world where your refrigerator notifies you when you’re running low on groceries, or streetlights adjust their brightness based on traffic patterns – that’s the power of IoT. In essence, IoT transforms ordinary objects into smart, interconnected devices, creating a network of endless possibilities.
Here is a blog on the role of electrical and electronics engineers in IOT. Let's dig in!!!!
For more such content visit: https://nttftrg.com/
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Governing Equations for Fundamental Aerodynamics_Anderson2010.pdf
IOE_Individual.pptx
1. Linked Analytics Data Sets
Name:- Shivamkumar Prasad
Roll no:- 44
Guided By :- Prof. Reena Kothari
2. INTRODUCTION
• Data Organization is the practice of categorizing and classifying data to make it more usable Similar to a file
folder, where we keep important documents, you'll need to arrange your data in the most logical and orderly
fashion, so you and anyone else who accesses it can easily find what they are looking for.
• Good data organization strategy are important because your data contains the keys to managing your company's
most valuable assets.
• An analytics strategy is part of your comprehensive strategic vision to specify how data is collected and used to
inform business decision. it is meant to provide clarity on key reporting metrics by: specifying the sources and
the types of data that are collected and used for reporting.
Strategies to organize Data for Analytics:-
1. Linked Analytics DataSets
2. Analytical DataSets
3. Building Analytical Datasets
3. Linked Analytics DataSets
Linked Data is a set of design principles for sharing machine - readable interlinked data on the web. when
combined with open data(data that can be freely used and distributed), it is called linked open data(LOD)
An RDF database(resource Description Framework) such as Ontotext,s GraphDB is an Example of LOD.
It is able to handle huge datasets coming from disparate soucres and link them to Open data, which boots
knowledge discovery and efficient data driven analytics
4. Linked Data is one of the core pillars of the Semantic Web, also known as the Web of Data. The
Semantic Web is about making links between datasets that are understandable not only to humans,
but also to machines, and Linked Data provides the best practices for making these links possible. In
other words, Linked Data is a set of design principles for sharing machine-readable interlinked data
on the Web
In computing, linked data (often capitalized as Linked Data) is structured data which is interlinked
with other data so it becomes more useful through semantic queries. It builds upon
standard Web technologies such as HTTP, RDF and URIs, but rather than using them to serve web
pages only for human readers, it extends them to share information in a way that can be read
automatically by computers. Part of the vision of linked data is for the Internet to become a
global database.[1]
5. The IoT Analytics Dataset is a materialized view defined in SQL over a Datastore, multiple Datasets can be created
over a single Datastore
Analytical DataSets
Building Analytical Datasets
• Analytical datasets are semi-denormalized tables. By semi-denormalized, this means including not just the ID
code of a field but the description for it as well. You may also decide to create categories based on value ranges
and include these as separate features.
• The goal is to make life easy for your analysts more than a focus on efficiently storing values, as it would be with
purely relational database design. You are, essentially, prebuilding the transformed datasets that an analyst
would be building using SQL to preprocess a dataset in preparation to train an ML model anyway.
6. Conclusion
The data generated from IoT devices turns out to be of value only if it gets subjected to analysis,
which brings data analytics into the picture. Data Analytics (DA) is defined as a process, which is
used to examine big and small data sets with varying data properties to extract meaningful
conclusions and actionable insights. These conclusions are usually in the form of trends,
patterns, and statistics that aid business organizations in proactively engaging with data to
implement effective decision-making processes.