Koa is a small framework that lets us create backend apps that run on the Node.js platform.
In this article, we’ll look at how to validate request data type with Koa.
Validating Request Data Type Using the request.is Method
We can call the ctx.is method with the string for the data type that we’re checking for to check if the data type from the Content-Type header’s value matches what we have in the argument.
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.
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In this tutorial, let’s discuss what data validation is and how it can be implemented in MS-Excel. Let’s start!!!
Data Validation is one of the features in MS-Excel which helps in maintaining the consistency of the data in the spreadsheet. It controls the type of data that can enter in the data validated cells.
Now, let’s have a look at how data validation works and how to implement it in the worksheet:
To apply data validation for the cells, then follow the steps.
1: Choose to which all cells the validation of data should work.
2: Click on the DATA tab.
3: Go to the Data Validation option.
4: Choose the drop down option in it and click on the Data Validation.
Once you click on the data validation menu from the ribbon, a box appears with the list of data validation criteria, Input message and error message.
Let’s first understand, what is an input message and error message?
Once, the user clicks the cell, the input message appears in a small box near the cell.
If the user violates the condition of that particular cell, then the error message pops up in a box in the spreadsheet.
The advantage of both the messages is that the input and as well as the error message guide the user about how to fill the cells. Both the messages are customizable also.
Let us have a look at how to set it up and how it works with a sample
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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.
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The impulse to cut project costs is often strong, especially in the final delivery phase of data integration and data migration projects. At this late phase of the project, a common mistake is to delegate testing responsibilities to resources with limited business and data testing skills.
Data integrations are at the core of data warehousing, data migration, data synchronization, and data consolidation projects.
In the past, most data integration projects involved data stored in databases. Today, it’s essential for organizations to also integrate their database or structured data with data from documents, e-mails, log files, websites, social media, audio, and video files.
Using data warehousing as an example, Figure 1 illustrates the primary checkpoints (testing points) in an end-to-end data quality testing process. Shown are points at which data (as it’s extracted, transformed, aggregated, consolidated, etc.) should be verified – that is, extracting source data, transforming source data for loads into target databases, aggregating data for loads into data marts, and more.
Only after data owners and all other stakeholders confirm that data integration was successful can the whole process be considered complete and ready for production.
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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.
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