Agnes  Sauer

Agnes Sauer


Data Modeling : An Overview

If you look at any software application in the world, you will see that at the very fundamental level, it will deal with organization, manipulation and presentation of data to fulfill the business requirements.

A** data model** is a conceptual representation to express and communicate business requirements. It visually represents the nature of data, business rules governing the data, and how the data will be organized in the database.

The process of data modeling can be compared to the process of construction of a house. Assume that a company ABC needs to build a guest house (data base). It calls a building architect (data modeler) and explains its building requirements (business requirements). Building Architect (data modeler) develops the plan (data model) and gives it to company ABC. Finally company ABC calls civil engineers (DBAs and database developers) to construct the guest house (database)

Key Terms in Data Modeling:

**_Entity and Attributes: _**The entities are the “things” in the business environment about which we want to store data e.g Products, Customers, Orders etc. Attributes provide a means of organizing and structuring data. E.g we need to store certain information about the products we sell, such as selling price or quantity available. These pieces of data are the attributes of the Product entity. Entities generally represent tables of database while attributes are columns of those tables.

**_Relationship: _**The relationship between entities describes how one entity is linked to another. In a data model, entities can be related as any of: one-to-one, many-to-one or many-to-many. This is said to be the cardinality of a given entity in relation to another.

Intersection Entity(Reference Table): In case of many-to-many relationship among entities, an intersection entity can be used to resolve it to many to one and one to many relationships. A simple example is: There are 2 entities, TV Show and Person. Each TV show may be watched by one or more persons while a person can watch one or more TV shows:

This can be resolved by introducing a new intersecting entity ‘Viewing record” as follows:

ER Diagram: A diagram that shows the entities and the relationships between them is called ER diagram. An ER diagram can take the form of Conceptual Data Model, Logical Data Model or Physical Data Model.

Conceptual Data Model: Conceptual data model includes all major entities and relationships and does not contain much detailed level of information about attributes and is often used in the initial planning phase. An example:

Logical Data Model: It is an extension of the conceptual data model. It includes all entities, attributes, key groups and relationships that represent business information and define business rules. An example:

Physical Data Model: It includes all required tables, columns, relationships, database properties, for the physical implementation of databases. Database performance, indexing strategy, physical storage and denormalization are important parameters of a physical model. An example:

Data Modeling Development Cycle:

Relational vs Dimensional Modeling:

#data-modeling #star-schema #database #dimensional-model #data-warehousing #data analysis

What is GEEK

Buddha Community

Data Modeling : An Overview
 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Enterprise Data Management: Stick to the Basics

Lots of people have increasing volumes of data and are trying to run data management programs to better sort it. Interestingly, people’s problems are pretty much the same throughout different sectors of any industry, and data management helps them configure solutions.

The fundamentals of enterprise data management (EDM), which one uses to tackle these kinds of initiatives, are the same whether one is in the health sector, a telco travel company, or a government agency, and more! Therefore, the fundamental practices that one needs to follow to manage data are similar from one industry to another.

For example, suppose you’re about to set off and design a program. In this case, it may be your integration platform project or your big warehouse project; however, the principles for designing that program of work is pretty much the same regardless of the actual details of the project.

#big data #bigdata #big data analytics #data management #data modeling #data governance #enterprise data #enterprise data management #edm

Cyrus  Kreiger

Cyrus Kreiger


How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Macey  Kling

Macey Kling


Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

  • Industrial metrology for quality assurance.
  • 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data