• define static data and give an example
• define dynamic data and give an example
• compare the use of static information sources with dynamic information sources
• define direct and indirect data source
• understand the advantages and disadvantages of gathering data from direct and indirect data sources
Organizations today are both blessed and cursed by data. But to get insights, you need to unlock the data you have. If you are like most analysts, you are spending hours and hours struggling with “dirty data,” data that needs to be joined together, and data that is in the wrong shape for visualization. And each time the data changes, you have to redo your work. You’re stuck in the “gunk” of preparing data, and you never get out of it to do what you really need to be doing, which is analyzing and visualizing your data and realizing new, deeper insights! Learn more about alteryx tableau integration by checking out the presentation.
Organizations today are both blessed and cursed by data. But to get insights, you need to unlock the data you have. If you are like most analysts, you are spending hours and hours struggling with “dirty data,” data that needs to be joined together, and data that is in the wrong shape for visualization. And each time the data changes, you have to redo your work. You’re stuck in the “gunk” of preparing data, and you never get out of it to do what you really need to be doing, which is analyzing and visualizing your data and realizing new, deeper insights! Learn more about alteryx tableau integration by checking out the presentation.
Data Lake or Data Warehouse? Data Cleaning or Data Wrangling? How to Ensure t...Anastasija Nikiforova
This presentation was delivered as part of the Data Science Seminar titled “When, Why and How? The Importance of Business Intelligence“ organized by the Institute of Computer Science (University of Tartu) in cooperation with Swedbank.
In this presentation I talked about:
*“Data warehouse vs. data lake – what are they and what is the difference between them?” (structured vs unstructured, static vs dynamic (real-time data), schema-on-write vs schema on-read, ETL vs ELT) with further elaboration on What are their goals and purposes? What is their target audience? What are their pros and cons?
*“Is the Data warehouse the only data repository suitable for BI?” – no, (today) data lakes can also be suitable. And even more, both are considered the key to “a single version of the truth”. Although, if descriptive BI is the only purpose, it might still be better to stay within data warehouse. But, if you want to either have predictive BI or use your data for ML (or do not have a specific idea on how you want to use the data, but want to be able to explore your data effectively and efficiently), you know that a data warehouse might not be the best option.
*“So, the data lake will save my resources a lot, because I do not have to worry about how to store /allocate the data – just put it in one storage and voila?!” – no, in this case your data lake will turn into a data swamp! And you are forgetting about the data quality you should (must!) be thinking of!
*“But how do you prevent the data lake from becoming a data swamp?” – in short and simple terms – proper data governance & metadata management is the answer (but not as easy as it sounds – do not forget about your data engineer and be friendly with him [always… literally always :D) and also think about the culture in your organization.
*“So, the use of a data warehouse is the key to high quality data?” – no, it is not! Having ETL do not guarantee the quality of your data (transform&load is not data quality management). Think about data quality regardless of the repository!
*“Are data warehouses and data lakes the only options to consider or are we missing something?“– true! Data lakehouse!
*“If a data lakehouse is a combination of benefits of a data warehouse and data lake, is it a silver bullet?“– no, it is not! This is another option (relatively immature) to consider that may be the best bit for you, but not a panacea. Dealing with data is not easy (still)…
In addition, in this talk I also briefly introduced the ongoing research into the integration of the data lake as a data repository and data wrangling seeking for an increased data quality in IS. In short, this is somewhat like an improved data lakehouse, where we emphasize the need of data governance and data wrangling to be integrated to really get the benefits that the data lakehouses promise (although we still call it a data lake, since a data lakehouse is nut sufficiently mature concept with different definitions of it).
Combining Data Lake and Data Wrangling for Ensuring Data Quality in CRISAnastasija Nikiforova
This presentation is a supplementary material for the "Combining Data Lake and Data Wrangling for Ensuring Data Quality in CRIS" presented at 15th International Conference on Current Research Information Systems (CRIS2022) - Linking Research Information across data spaces. It provides an insight on the ongoing study of combining data lake as a data repository and data wrangling seeking for an increased data quality in CRIS systems, although the proposed approach is domain-agnostic and can be used not only within CRIS.
Read the article here -> Azeroual, O., Schöpfel, J., Ivanovic, D., & Nikiforova, A. (2022, May). Combining Data Lake and Data Wrangling for Ensuring Data Quality in CRIS. In CRIS2022: 15th International Conference on Current Research Information Systems --> https://hal.archives-ouvertes.fr/hal-03694519/
BDW16 London - Scott Krueger, skyscanner - Does More Data Mean Better Decisio...Big Data Week
We have seen vast improvements to data collection, storage, processing and transport in recent years. An increasing number of networked devices are emitting data and all of us are preparing to handle this wave of valuable data.
Have we, as data professionals, been too focused on the technical challenges and analytical results?
What about the data quality? Are we confident about it? How can we be sure we are making good decisions?
We need to revisit methods of assessing data quality on our modernized data platforms. The quality of our decision making depends on it.
Using Data Lakes to Sail Through Your Sales GoalsIrshadKhan682442
Using Data Lakes to Sail Through Your Sales Goals Most Popular Busting 5 Common CRM Myths Fail-Proof Ways to Hire A-Lister in Sales Our Recommendations Retail Redefined - Where does the innovation takes us?
To know more visit here: https://www.denave.com/resources/ebooks/using-data-lakes-to-sail-through-your-sales-goals/
The volume, variety, velocity and veracity of big data are getting increasingly complex
each passing day. The way the data is stored, processed, managed and shared with
decision-makers is getting impacted by this complexity and to tackle the same, a
revolutionary approach to data management has come into picture. A data lake.
Busting 5 Common CRM Myths Most Read Fail-Proof Ways to Hire A-Listers in Sales Fail-Proof Ways to Use Data Lakes to Achieve Your Sales Goals Recommendations from Us Where does innovation lead us with respect to retail redefined?
WHAT IS A DATA LAKE? Know DATA LAKES & SALES ECOSYSTEMRajaraj64
As the name suggests, data lake is a large reservoir of data – structured or unstructured, fed through disparate channels. The data is fed through channels in anad-hoc manner into these data lakes, however, owing to the predefined set of rules orschema, correlation between the database is established automatically to help with the extraction of meaningful information.
For more information visit:- https://bit.ly/3lMLD1h
Why Data Virtualization? An Introduction by DenodoJusto Hidalgo
Data Virtualization means Real-time Data Access and Integration. But why do I need it? This presentation tries to answer it in a simple yet clear way.
By Alberto Pan, CTO of Denodo, and Justo Hidalgo, VP Product Management.
Jet Reports es la herramienta para construir el mejor BI y de forma mas rapida CLARA CAMPROVIN
Análisis empresariales cuando los necesite, en cualquier lugar
Jet Enterprise es una solución de inteligencia empresarial y generación de informes desarrollada específicamente para satisfacer las necesidades propias de los usuarios de Microsoft Dynamics. Ahora puede juntar toda su información en un mismo lugar y permitir que quien usted quiera de la organización realice fácilmente sofisticados análisis empresariales desde cualquier sitio. Capacite a los usuarios para tomar mejores decisiones, más rápido, prácticamente con cualquier dispositivo.
Con Jet Enterprise dispone de:
Una solución completa de inteligencia empresarial y generación de informes, lista para usar en solo 2 horas
Más de 80 paneles y plantillas de informes
7 cubos pregenerados personalizables
Un almacén de datos
Integración directa con sus datos de Microsoft Dynamics y posibilidad de conectarse a otros sistemas empresariales pertinentes
Posibilidad de crear paneles en cuestión de minutos, sin necesidad de conocer la estructura de datos subyacente
Jet Mobile opcional, para acceder a sus datos desde cualquier sitio a través de un navegador web o un dispositivo móvil
Una plataforma robusta de automatización y personalización del almacenamiento de datos
«Comenzamos con datos de Sage Pro, datos de NAV 2009 y, además, datos incorporados de la nueva empresa que habíamos adquirido, por lo que ahora estamos usando tres sistemas de datos. Las ventajas de combinar los tres sistemas en Jet Enterprise han sido enormes».
– Davis & Shirtliff
Éxito inmediato = rápido ROI y bajo coste de propiedad
Muchas soluciones de inteligencia empresarial conllevan costes ocultos, como implementaciones prolongadas y difíciles, personalizaciones caras y precio elevado de las licencias cuando se amplían a un gran número de usuarios. Jet Enterprise se suele instalar en unas dos horas, requiere un nivel mínimo de formación de los usuarios y ofrece licencias para un número ilimitado de usuarios. Los usuarios habitualmente experimentan un incremento de los ingresos brutos en los primeros 12 meses de uso.
Data Lake or Data Warehouse? Data Cleaning or Data Wrangling? How to Ensure t...Anastasija Nikiforova
This presentation was delivered as part of the Data Science Seminar titled “When, Why and How? The Importance of Business Intelligence“ organized by the Institute of Computer Science (University of Tartu) in cooperation with Swedbank.
In this presentation I talked about:
*“Data warehouse vs. data lake – what are they and what is the difference between them?” (structured vs unstructured, static vs dynamic (real-time data), schema-on-write vs schema on-read, ETL vs ELT) with further elaboration on What are their goals and purposes? What is their target audience? What are their pros and cons?
*“Is the Data warehouse the only data repository suitable for BI?” – no, (today) data lakes can also be suitable. And even more, both are considered the key to “a single version of the truth”. Although, if descriptive BI is the only purpose, it might still be better to stay within data warehouse. But, if you want to either have predictive BI or use your data for ML (or do not have a specific idea on how you want to use the data, but want to be able to explore your data effectively and efficiently), you know that a data warehouse might not be the best option.
*“So, the data lake will save my resources a lot, because I do not have to worry about how to store /allocate the data – just put it in one storage and voila?!” – no, in this case your data lake will turn into a data swamp! And you are forgetting about the data quality you should (must!) be thinking of!
*“But how do you prevent the data lake from becoming a data swamp?” – in short and simple terms – proper data governance & metadata management is the answer (but not as easy as it sounds – do not forget about your data engineer and be friendly with him [always… literally always :D) and also think about the culture in your organization.
*“So, the use of a data warehouse is the key to high quality data?” – no, it is not! Having ETL do not guarantee the quality of your data (transform&load is not data quality management). Think about data quality regardless of the repository!
*“Are data warehouses and data lakes the only options to consider or are we missing something?“– true! Data lakehouse!
*“If a data lakehouse is a combination of benefits of a data warehouse and data lake, is it a silver bullet?“– no, it is not! This is another option (relatively immature) to consider that may be the best bit for you, but not a panacea. Dealing with data is not easy (still)…
In addition, in this talk I also briefly introduced the ongoing research into the integration of the data lake as a data repository and data wrangling seeking for an increased data quality in IS. In short, this is somewhat like an improved data lakehouse, where we emphasize the need of data governance and data wrangling to be integrated to really get the benefits that the data lakehouses promise (although we still call it a data lake, since a data lakehouse is nut sufficiently mature concept with different definitions of it).
Combining Data Lake and Data Wrangling for Ensuring Data Quality in CRISAnastasija Nikiforova
This presentation is a supplementary material for the "Combining Data Lake and Data Wrangling for Ensuring Data Quality in CRIS" presented at 15th International Conference on Current Research Information Systems (CRIS2022) - Linking Research Information across data spaces. It provides an insight on the ongoing study of combining data lake as a data repository and data wrangling seeking for an increased data quality in CRIS systems, although the proposed approach is domain-agnostic and can be used not only within CRIS.
Read the article here -> Azeroual, O., Schöpfel, J., Ivanovic, D., & Nikiforova, A. (2022, May). Combining Data Lake and Data Wrangling for Ensuring Data Quality in CRIS. In CRIS2022: 15th International Conference on Current Research Information Systems --> https://hal.archives-ouvertes.fr/hal-03694519/
BDW16 London - Scott Krueger, skyscanner - Does More Data Mean Better Decisio...Big Data Week
We have seen vast improvements to data collection, storage, processing and transport in recent years. An increasing number of networked devices are emitting data and all of us are preparing to handle this wave of valuable data.
Have we, as data professionals, been too focused on the technical challenges and analytical results?
What about the data quality? Are we confident about it? How can we be sure we are making good decisions?
We need to revisit methods of assessing data quality on our modernized data platforms. The quality of our decision making depends on it.
Using Data Lakes to Sail Through Your Sales GoalsIrshadKhan682442
Using Data Lakes to Sail Through Your Sales Goals Most Popular Busting 5 Common CRM Myths Fail-Proof Ways to Hire A-Lister in Sales Our Recommendations Retail Redefined - Where does the innovation takes us?
To know more visit here: https://www.denave.com/resources/ebooks/using-data-lakes-to-sail-through-your-sales-goals/
The volume, variety, velocity and veracity of big data are getting increasingly complex
each passing day. The way the data is stored, processed, managed and shared with
decision-makers is getting impacted by this complexity and to tackle the same, a
revolutionary approach to data management has come into picture. A data lake.
Busting 5 Common CRM Myths Most Read Fail-Proof Ways to Hire A-Listers in Sales Fail-Proof Ways to Use Data Lakes to Achieve Your Sales Goals Recommendations from Us Where does innovation lead us with respect to retail redefined?
WHAT IS A DATA LAKE? Know DATA LAKES & SALES ECOSYSTEMRajaraj64
As the name suggests, data lake is a large reservoir of data – structured or unstructured, fed through disparate channels. The data is fed through channels in anad-hoc manner into these data lakes, however, owing to the predefined set of rules orschema, correlation between the database is established automatically to help with the extraction of meaningful information.
For more information visit:- https://bit.ly/3lMLD1h
Why Data Virtualization? An Introduction by DenodoJusto Hidalgo
Data Virtualization means Real-time Data Access and Integration. But why do I need it? This presentation tries to answer it in a simple yet clear way.
By Alberto Pan, CTO of Denodo, and Justo Hidalgo, VP Product Management.
Jet Reports es la herramienta para construir el mejor BI y de forma mas rapida CLARA CAMPROVIN
Análisis empresariales cuando los necesite, en cualquier lugar
Jet Enterprise es una solución de inteligencia empresarial y generación de informes desarrollada específicamente para satisfacer las necesidades propias de los usuarios de Microsoft Dynamics. Ahora puede juntar toda su información en un mismo lugar y permitir que quien usted quiera de la organización realice fácilmente sofisticados análisis empresariales desde cualquier sitio. Capacite a los usuarios para tomar mejores decisiones, más rápido, prácticamente con cualquier dispositivo.
Con Jet Enterprise dispone de:
Una solución completa de inteligencia empresarial y generación de informes, lista para usar en solo 2 horas
Más de 80 paneles y plantillas de informes
7 cubos pregenerados personalizables
Un almacén de datos
Integración directa con sus datos de Microsoft Dynamics y posibilidad de conectarse a otros sistemas empresariales pertinentes
Posibilidad de crear paneles en cuestión de minutos, sin necesidad de conocer la estructura de datos subyacente
Jet Mobile opcional, para acceder a sus datos desde cualquier sitio a través de un navegador web o un dispositivo móvil
Una plataforma robusta de automatización y personalización del almacenamiento de datos
«Comenzamos con datos de Sage Pro, datos de NAV 2009 y, además, datos incorporados de la nueva empresa que habíamos adquirido, por lo que ahora estamos usando tres sistemas de datos. Las ventajas de combinar los tres sistemas en Jet Enterprise han sido enormes».
– Davis & Shirtliff
Éxito inmediato = rápido ROI y bajo coste de propiedad
Muchas soluciones de inteligencia empresarial conllevan costes ocultos, como implementaciones prolongadas y difíciles, personalizaciones caras y precio elevado de las licencias cuando se amplían a un gran número de usuarios. Jet Enterprise se suele instalar en unas dos horas, requiere un nivel mínimo de formación de los usuarios y ofrece licencias para un número ilimitado de usuarios. Los usuarios habitualmente experimentan un incremento de los ingresos brutos en los primeros 12 meses de uso.
• describe the coding of data (including: M for male, F for female) and more intricate codes (including: clothing type, sizes and colour of garment)
• discuss the advantages and disadvantages of the coding of data
• evaluate the need for encoding data and analyse the different methods that can be used to encode data (including: codecs)
• define encryption and describe different methods of encryption (including: symmetric, asymmetric, public key, private key)
• evaluate the need for encryption and how it can be used to protect data such as on a hard disk, email or in HTTPS websites
• discuss encryption protocols (including: the purpose of Secure Socket Layer (SSL)/Transport Layer Security (TLS) and the use of SSL/TLS in client server communication)
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
2. OBJECTIVES
The learners will be able to:
Define Static Data and give examples
Define Dynamic Data and give examples
Compare the use of static information sources with dynamic information
sources
Define direct and indirect data source
Understand the advantages and disadvantages of gathering data from direct
and indirect data sources
3. KEY TERMS
Static data: data that does not normally change
Dynamic data: data that changes automatically without user
intervention
Direct data source: data that is collected for the purpose for which
it will be used
Indirect data source: data that was collected for a different purpose
(secondary source)
4. DATA STRUCTURE
What is a data structure?
“A data structure is a collection of data items that is
implemented by various development tools.”
6. STATIC DATA STRUCTURE
Static means “still”. It is data that does not normally
change.
Static data is either fixed or has to be changed
manually by editing a document.
Static data structures are very good for storing a well-
defined number of data items.
7. For example –
Title of a web page
Magazines
CD – ROMS
Instructions on a data entry screen
8. ADVANTAGES OF STATIC DATA STRUCTURES
The development tool can allocate space during compilation
Easy to program
Easy to check for overflow
Allows random access
9. DISADVANTAGES OF STATIC DATA STRUCTURES
The developer has to estimate the maximum amount of space that is
going to be needed.
A lot of space maybe wasted.
11. DYNAMIC DATA STRUCTURE
Dynamic means “moving”.
It is data that updates as a result of the source data changing.
Dynamic data is updated automatically without user intervention
12. There are many situations where the number of items to be stored is NOT known in
advance e.g. the length of someone’s name may NOT be the same as any other’s
name.
In this case, the developer would be using a dynamic data structure. This means that
the data structure is allowed to grow and shrink as the demand for storage arises.
The developer should also set a maximum size to help avoid memory collisions.
13. EXAMPLES
Live sports result on a website (when a wicket falls or a run is
scored, e.g. cricinfo.com, the scores are updated on the
website)
News feeds on a mobile phone app (when the news is
changed in the main database, the news feed will be updated
on the phone)
Availability of tickets for a concert
Expected arrival times of train
Profit for a product in a spreadsheet (profit = price – cost so
when either the price or cost changes, then the profit changes
too)
14. ADVANTAGES OF DYNAMIC DATA STRUCTURE
Uses memory efficiently.
Can extend as far as physically possible – more flexible.
Allows for the program to be more easily written – less must be determined at
compilation time.
Inserting, merging and deleting of items is very easy and requires little
processing power.
15. DISADVANTAGES OF DYNAMIC DATA STRUCTURE
Unnecessary + inefficient for small amounts of data. In this case the size of the data
may be even smaller than the extra data needed to make it dynamic.
Data can be highly fragmented over extended use. This may cause a physical bottleneck
when the hardware needs to access this data.
16. COMPARISON OF STATIC INFORMATION SOURCES COMPARED
WITH DYNAMIC INFORMATION SOURCES
STATIC DATA DYNAMIC DATA
The information does not change on a regular basis. Information is updated automatically when the
original data changes.
The information can become out dated quickly
because it is not designed to be changed on a
regular basis.
It is most likely to be updated as it changes
automatically based on the source data.
The information can be viewed offline because live
data is not required.
An Internet or network connection to the source
data is required, which can be costly and can also be
slow in remote areas.
It is more likely to be accurate because time will
have been taken to check the information being
published, as it will be available for a long period of
time.
The data may have been produced very quickly and
so may contain errors.
