Now that MongoDB Charts has gotten even more embeddable via the general release of our Embedding SDK, it’s time to get secure and make the most of it by utilizing one of the core new features in the release, Authenticated Embedding.
Many users of MongoDB store sensitive information. And many people use MongoDB to make incredible applications that would benefit from integrated data visualizations of that sensitive information. With that in mind, we’ve provided the ability to integrate embedded charts with a site’s existing authentication system. This ensures that only authenticated users are able to view the embedded charts.
Today, we’re the Sales Manager at Made-Up-Company-Name Inc. We’ve got a lot of data we want to show to our regional managers as charts and tables integrated into our home-grown CRM system. This data is sensitive, so we don’t want it visible to anyone outside the sales team. Furthermore, we want to contextualize it by location: for compliance reasons, it’s important our regional managers don’t have access to data from outside their region so that we can ensure our company abides with the new legislation.
What is 2FA
Two-Factor Authentication (or 2FA as it often referred to) is an extra layer of security that is used to provide users an additional level of protection when securing access to an account.
Employing a 2FA mechanism is a vast improvement in security over the Singe-Factor Authentication method of simply employing a username and password. Using this method, accounts that have 2FA enabled, require the user to enter a one-time passcode that is generated by an external application. The 2FA passcode (usually a six-digit number) is required to be input into the passcode field before access is granted. The 2FA input is usually required directly after the username and password are entered by the client.
#tutorials #2fa #access #account security #authentication #authentication method #authentication token #cli #command line #cpanel #feature manager #google authenticator #one time password #otp #otp authentication #passcode #password #passwords #qr code #security #security code #security policy #security practices #single factor authentication #time-based one-time password #totp #two factor authentication #whm
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
Using data to inform decisions is essential to product management, or anything really. And thankfully, we aren’t short of it. Any online application generates an abundance of data and it’s up to us to collect it and then make sense of it.
Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories. If it wasn’t already, data literacy is as much a fundamental skill as learning to read or write. Or it certainly will be.
Nothing is more powerful than data democracy, where anyone in your organization can regularly make decisions informed with data. As part of enabling this, we need to be able to visualize data in a way that brings it to life and makes it more accessible. I’ve recently been learning how to do this and wanted to share some of the cool ways you can do this in Google Data Studio.
#google-data-studio #blending-data #dashboard #data-visualization #creating-visualizations #how-to-visualize-data #data-analysis #data-visualisation
Data Loss Prevention is a set of tools and practices geared towards protecting your data from loss and leak. Even though the name has only the loss part, in actuality, it’s as much about the leak protection as it is about the loss protection. Basically, DLP, as a notion, encompasses all the security practices around protecting your company data.
Every company, even if never vocalized it, has or should have at least some DLP practices in place. You obviously use identity and access management that include authenticating users; you also for sure use some endpoint protection for users’ computers. Maybe (and hopefully) you do beyond that. And this all can be called data loss prevention.
#data-protection #cybersecurity #data-backup #data-security #data-breach #personal-data-security #data #cyber-security
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