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
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
In this post, we will discuss about blazor data binding in depth. This is first part of “Blazor Data Binding in Depth” article.
Blazoris a .NET framework for building single-page applications (SPA) using C#, Razor and HTML. Data binding is one the most powerful features of any single-page application through which page UI updates with model data without page reload. We will discuss about data binding feature in Blazor.
This article is part of our step-by-step Blazor series. If you have not read our previous articles on Blazor, you can check them here.
Data Binding in Blazor is a powerful feature that allows us to synchronize a variable and a html element or a component. In simple terms, whenever a variable/property data gets update in C#, UI immediately gets update with updated data and vice-versa. Let’s see data binding types in detail.
In one-way binding, data flows in one direction only from C# code to UI. That means any property or variable in C# code directly can be used inside html and whenever this property or variable value gets update in C#, this is immediately updated in UI. However vice-versa in one-way binding is not possible. This is just to display updated model data in the UI.
Let’s understand it with the help of code example. In the following example, take a look at Counter component (counter.razor) that comes with default blazor template project.
In the above code, we have a private variable _currentCount _which is initially set to 0 that displays 0 in paragraph when application runs. The _currentCount _variable works as a one-way binding here. In order to bind one-way values, we use @ symbol followed by variable or property name.
A method IncrementCount is also given in the code which works as an event handler of _onclick _blazor event of button element. When _ClickMe _button will be clicked, handler will be executed and increases the _currentCount _value by one, which will immediately update the UI. Executing event handlers in blazor triggers a re-render which updates the UI.
You are aware now with one-way binding that updates the value in one direction only. What, if you want user to update the variable or property value from UI also. This is where two-way binding comes place.
Before understanding two-way binding in blazor, let’s understand “what are components parameters in blazor?“
A component can receive data from its parent component using [Parameter] attribute. Any of following types of data can be send to a component through [Parameter] attribute. Methods can also be send thru parameters.
As we saw in our previous article A deep dive on Blazor components that blazor components can be nested. That means a blazor component can be nested inside other blazor components. As an example, let’s create a new component called CounterValueDisplay.razor and nest it inside count component.
So [Parameter] attribute is used when you want to pass any data from parent (Counter) to child (CounterValueDisplay) component.
In above example, Parent (count) component is passing value to Child (CounterValueDisplay) component using [Parameter] attribute. Follow below steps for clear understanding
#blazor #asp.net core #blazor #component parameters in blazor #one-way data binding in blazor #two-way data binding in blazor
As data mesh advocates come to suggest that the data mesh should replace the monolithic, centralized data lake, I wanted to check in with Dipti Borkar, co-founder and Chief Product Officer at Ahana. Dipti has been a tremendous resource for me over the years as she has held leadership positions at Couchbase, Kinetica, and Alluxio.
According to Dipti, while data lakes and data mesh both have use cases they work well for, data mesh can’t replace the data lake unless all data sources are created equal — and for many, that’s not the case.
All data sources are not equal. There are different dimensions of data:
Each data source has its purpose. Some are built for fast access for small amounts of data, some are meant for real transactions, some are meant for data that applications need, and some are meant for getting insights on large amounts of data.
Things changed when AWS commoditized the storage layer with the AWS S3 object-store 15 years ago. Given the ubiquity and affordability of S3 and other cloud storage, companies are moving most of this data to cloud object stores and building data lakes, where it can be analyzed in many different ways.
Because of the low cost, enterprises can store all of their data — enterprise, third-party, IoT, and streaming — into an S3 data lake. However, the data cannot be processed there. You need engines on top like Hive, Presto, and Spark to process it. Hadoop tried to do this with limited success. Presto and Spark have solved the SQL in S3 query problem.
#big data #big data analytics #data lake #data lake and data mesh #data lake #data mesh
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