Ruth  Nabimanya

Ruth Nabimanya

1621275120

Implementing a Large-scale Relational Database in a Distributed Database System.

First we need to understand the difference between the centralized database and the distributed database. So basically, with the centralized database, there is one single database and located at one side of our network. In the distributed database there are two or more database files located on the network (in different departments and different sections) and they need to be synced at the end of the day.

The advantage of the centralized database is since there is only one database file you always are getting a complete view of the system. you’re seeing as it’s being updated. you’re seeing the changes that are being made when you update and change it. everyone has access to that.

The advantage of a distributed database is because there are multiple database files you won’t interfere with each other if someone’s accessing the specific record. After all, you’re working on a different copy. there might be faster if you’re on a larger network because the actual file might be saved in a location that is more localized to where your workspace is. also, if one site fails the complete system won’t go down because you still got other database files to work from maybe not your specific department but still there are existing files that can be used by the company.

The disadvantages of a centralized database are can led to bottlenecking with many people accessing one file it could slow down the bandwidth that can slow down the function of the actual program because everyone trying to access data you might have to wait till someone’s finished using the actual database so you can fill in your entry can slow down productivity.

The disadvantages of the distributed database are that you’ve got to synchronize, and synchronizing takes time. because all the databases have to be synchronizing the data that they all have matching data at the end of the day. The other issue can be data and needs to be replicated so you need more than one file so which means more file sizes are being used up. It could also mean people entering the same data getting in the same day because they’re not knowing that it’s being put in on different sites.

The advantage and disadvantage of DDBMS are discussed here clearly.

#data #data-science #distributed-systems #database #data-engineering

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Implementing a Large-scale Relational Database in a Distributed Database System.
Ruth  Nabimanya

Ruth Nabimanya

1620633584

System Databases in SQL Server

Introduction

In SSMS, we many of may noticed System Databases under the Database Folder. But how many of us knows its purpose?. In this article lets discuss about the System Databases in SQL Server.

System Database

Fig. 1 System Databases

There are five system databases, these databases are created while installing SQL Server.

  • Master
  • Model
  • MSDB
  • Tempdb
  • Resource
Master
  • This database contains all the System level Information in SQL Server. The Information in form of Meta data.
  • Because of this master database, we are able to access the SQL Server (On premise SQL Server)
Model
  • This database is used as a template for new databases.
  • Whenever a new database is created, initially a copy of model database is what created as new database.
MSDB
  • This database is where a service called SQL Server Agent stores its data.
  • SQL server Agent is in charge of automation, which includes entities such as jobs, schedules, and alerts.
TempDB
  • The Tempdb is where SQL Server stores temporary data such as work tables, sort space, row versioning information and etc.
  • User can create their own version of temporary tables and those are stored in Tempdb.
  • But this database is destroyed and recreated every time when we restart the instance of SQL Server.
Resource
  • The resource database is a hidden, read only database that holds the definitions of all system objects.
  • When we query system object in a database, they appear to reside in the sys schema of the local database, but in actually their definitions reside in the resource db.

#sql server #master system database #model system database #msdb system database #sql server system databases #ssms #system database #system databases in sql server #tempdb system database

Ruth  Nabimanya

Ruth Nabimanya

1621275120

Implementing a Large-scale Relational Database in a Distributed Database System.

First we need to understand the difference between the centralized database and the distributed database. So basically, with the centralized database, there is one single database and located at one side of our network. In the distributed database there are two or more database files located on the network (in different departments and different sections) and they need to be synced at the end of the day.

The advantage of the centralized database is since there is only one database file you always are getting a complete view of the system. you’re seeing as it’s being updated. you’re seeing the changes that are being made when you update and change it. everyone has access to that.

The advantage of a distributed database is because there are multiple database files you won’t interfere with each other if someone’s accessing the specific record. After all, you’re working on a different copy. there might be faster if you’re on a larger network because the actual file might be saved in a location that is more localized to where your workspace is. also, if one site fails the complete system won’t go down because you still got other database files to work from maybe not your specific department but still there are existing files that can be used by the company.

The disadvantages of a centralized database are can led to bottlenecking with many people accessing one file it could slow down the bandwidth that can slow down the function of the actual program because everyone trying to access data you might have to wait till someone’s finished using the actual database so you can fill in your entry can slow down productivity.

The disadvantages of the distributed database are that you’ve got to synchronize, and synchronizing takes time. because all the databases have to be synchronizing the data that they all have matching data at the end of the day. The other issue can be data and needs to be replicated so you need more than one file so which means more file sizes are being used up. It could also mean people entering the same data getting in the same day because they’re not knowing that it’s being put in on different sites.

The advantage and disadvantage of DDBMS are discussed here clearly.

#data #data-science #distributed-systems #database #data-engineering

Ruth  Nabimanya

Ruth Nabimanya

1620728160

Decentralized Databases Reduce Data Latency With Geographically Distributed Data Centers

More often than not, that external server is a monolithic database residing in a single cloud region. This article will dig into some of the existing architectures that cause this issue and provide solutions on how to resolve them.

Latency Defined

The Problem: A Centralized Database

Real-World Examples

IoT-based Smart Homes

Autonomous Vehicles

#database #cloud #architecture #distributed-systems #what-cause-latency #distributed-computing #databases #cloud-computing

Ruth  Nabimanya

Ruth Nabimanya

1622359260

#NoBrainers: You Need A High Performing Low Latency Distributed Database | Hacker Noon

…but which industries benefit the most from it?

There are certain industries that greatly benefit from high-performing, low-latency, geo-distributed technologies, while other organizations might be more focused on vertically scaling architectures.

This is dependent on numerous factors including the data pipeline, network, data structure, type of product or solution, short and long term goals, etc.

While there are currently many databases and tools that provide vertical scaling capabilities, there are not many that focus on horizontal scaling – but there’s still a need for both.

#database #distributed-systems #performance #distributed-computing #data-management #scaling #edge-computing #cloud-computing

Ruth  Nabimanya

Ruth Nabimanya

1620663480

Which Database Is Right For You?Graph Database vs. Relational Database

At the very beginning of most development endeavors lies an important question: What database do I choose? There is such an abundance of database technologies at this moment, it’s no wonder many developers don’t have the time or energy to research new ones. If you are one of those developers and you aren’t very familiar with graph databases in general, you’ve come to the right place!

In this article, you will learn about the main differences between a graph database and a relational database, what kind of use-cases are best suited for each database type, and what are their strengths and weaknesses.

How Does a Graph Database Differ from a Relational Database?

The Graph Data Model

The Relational Data Model

When to use a Graph Database?

When not to use a Graph Database

Is a Graph Database Worth it?

#graph-database #relational-database #graph-theory #graph-analysis #data-analytics #networks #data #database