1603314000
After reading this article, you will be familiarized with the practicalities on how to bring your internal data get exposed to the external consumers (i.e.: partners, vendors and/or any kind of consumers) in a secured way.
We strongly believe that the approach we made is quite practical “and is simple” that solves an important scenario in relation to SFTP. It might be a big help to you, therefore, we wrote this article.
Such kind of solution is done completely in a matter of few hours, in which historically it is extremely tedious and painful to realize.
Think of these things…
You write few lines of code and had accomplished the complex job in a very short time. The solution is dynamic, adaptable to any changes (both schema, data and/or data stream). The partner is happy!
Disclaimer: The information on this article may not work and applicable in all cases, but the technique can be leveraged on how you do your own solutions. Any recommendations and suggestions can be discussed, but the contents will only be limited based on our intent.
It is important for you to know the actual scenarios and problems the solution on this article is addressing.
Also, your consumers are agreeing to any changes on the schema or data/data-type, for as long the data is arriving in a frequent manner, as agreed.
#csharp #programming #technology #data-science
1620466520
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
1602560783
In this article, we’ll discuss how to use jQuery Ajax for ASP.NET Core MVC CRUD Operations using Bootstrap Modal. With jQuery Ajax, we can make HTTP request to controller action methods without reloading the entire page, like a single page application.
To demonstrate CRUD operations – insert, update, delete and retrieve, the project will be dealing with details of a normal bank transaction. GitHub repository for this demo project : https://bit.ly/33KTJAu.
Sub-topics discussed :
In Visual Studio 2019, Go to File > New > Project (Ctrl + Shift + N).
From new project window, Select Asp.Net Core Web Application_._
Once you provide the project name and location. Select Web Application(Model-View-Controller) and uncheck HTTPS Configuration. Above steps will create a brand new ASP.NET Core MVC project.
Let’s create a database for this application using Entity Framework Core. For that we’ve to install corresponding NuGet Packages. Right click on project from solution explorer, select Manage NuGet Packages_,_ From browse tab, install following 3 packages.
Now let’s define DB model class file – /Models/TransactionModel.cs.
public class TransactionModel
{
[Key]
public int TransactionId { get; set; }
[Column(TypeName ="nvarchar(12)")]
[DisplayName("Account Number")]
[Required(ErrorMessage ="This Field is required.")]
[MaxLength(12,ErrorMessage ="Maximum 12 characters only")]
public string AccountNumber { get; set; }
[Column(TypeName ="nvarchar(100)")]
[DisplayName("Beneficiary Name")]
[Required(ErrorMessage = "This Field is required.")]
public string BeneficiaryName { get; set; }
[Column(TypeName ="nvarchar(100)")]
[DisplayName("Bank Name")]
[Required(ErrorMessage = "This Field is required.")]
public string BankName { get; set; }
[Column(TypeName ="nvarchar(11)")]
[DisplayName("SWIFT Code")]
[Required(ErrorMessage = "This Field is required.")]
[MaxLength(11)]
public string SWIFTCode { get; set; }
[DisplayName("Amount")]
[Required(ErrorMessage = "This Field is required.")]
public int Amount { get; set; }
[DisplayFormat(DataFormatString = "{0:MM/dd/yyyy}")]
public DateTime Date { get; set; }
}
C#Copy
Here we’ve defined model properties for the transaction with proper validation. Now let’s define DbContextclass for EF Core.
#asp.net core article #asp.net core #add loading spinner in asp.net core #asp.net core crud without reloading #asp.net core jquery ajax form #asp.net core modal dialog #asp.net core mvc crud using jquery ajax #asp.net core mvc with jquery and ajax #asp.net core popup window #bootstrap modal popup in asp.net core mvc. bootstrap modal popup in asp.net core #delete and viewall in asp.net core #jquery ajax - insert #jquery ajax form post #modal popup dialog in asp.net core #no direct access action method #update #validation in modal popup
1620629020
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
1618053720
Databases store data in a structured form. The structure makes it possible to find and edit data. With their structured structure, databases are used for data management, data storage, data evaluation, and targeted processing of data.
In this sense, data is all information that is to be saved and later reused in various contexts. These can be date and time values, texts, addresses, numbers, but also pictures. The data should be able to be evaluated and processed later.
The amount of data the database could store is limited, so enterprise companies tend to use data warehouses, which are versions for huge streams of data.
#data-warehouse #data-lake #cloud-data-warehouse #what-is-aws-data-lake #data-science #data-analytics #database #big-data #web-monetization
1618039260
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