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Types of Edge ML and Enterprise Use Cases

In the ever-evolving landscape of artificial intelligence (AI), one of the most exciting advancements is the integration of Edge Machine Learning (Edge ML). This revolutionary technology empowers devices to perform AI-driven tasks locally, on the edge, rather than relying solely on centralized cloud servers. In this blog, we'll explore the world of Machine Learning at the Edge, its significance, and enterprise edge computing use cases. So, fasten your seatbelts as we delve into the future of AI at the edge!

What Is Edge Machine Learning?

Edge ML is the practice of deploying machine learning algorithms on edge devices, such as smartphones, IoT devices, and embedded systems. Unlike traditional cloud-based AI, which relies on centralized data centers, Edge ML processes data locally, directly on the device where it's generated.

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8 Ways to Improve Application Performance

Application performance is critical for delivering a fast and responsive user experience. Slow performance, or high latency, can lead to frustrated users and lost revenue for the organization.

From a high level, application latency refers to the delay between the user's request and the application's response. Latency can not only impact the overall user experience and decrease engagement, but it can be a costly and complicated problem as well. App performance also impacts overall efficiency. Slow applications can slow down business processes, leading to decreased productivity and increased costs. All of these factors can reduce a company’s ability to compete in the market. In today’s world, customers expect fast and responsive applications. Businesses that are unable to deliver on this expectation risk losing customers to competitors who offer a better user experience.

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Edge Data Platforms, Real-Time Services, and Modern Data Trends

We all know that data is being generated at an unprecedented rate. You may also know that this has led to an increase in the demand for efficient and secure data storage solutions that won’t break the bank. Edge data platforms and real-time services are two solutions focused on solving the challenges of modern data management, and they are quickly gaining popularity among businesses. This article will explore what edge data platforms and real-time services are, why they are important, and how they can be used.

What Are Edge Data Platforms?

Edge data platforms are software solutions that enable businesses to collect, process, and analyze data at the edge of the network. These platforms offer several advantages over traditional cloud computing. By processing data at the edge of the network, latency can be minimized, which means that data can be processed and analyzed faster. This is especially important for applications that require real-time responses, such as autonomous vehicles, industrial IoT applications, or streaming media.

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HarperDB: More Than a Database

Introduction

I recently had a very interesting conversation on our podcast with Ron Lewis, the Director of Innovation and Engineering at Lumen Technologies. Ron brought up the notion that HarperDB is more than just a database, and for certain users or projects, HarperDB is not serving as a database at all. How can this be possible?

Database, Explained

Well, what really is a database? Wikipedia states “In computing, a database is an organized collection of data stored and accessed electronically from a computer system.” Another site simply states that “A database is a systematic collection of data. They support electronic storage and manipulation of data. Databases make data management easy.”

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Improving Mobile App Performance With a Powerful Database

You are probably aware that there are seemingly endless options to consider when it comes to selecting a database and other technologies for your mobile app. With so many choices, it can be difficult and confusing to determine what really matters when it comes to your tech stack. A little while back, I wrote an extensive article on the different database architectures and use cases available to provide guidance on picking the right technology for the right project. While that is still an accurate and solid resource, this article takes a deeper dive into considerations for improving the performance of, specifically, mobile apps.

Mobile vs. Web Apps

First and foremost, perhaps we should take a quick look at the difference between mobile and web apps. Mobile apps live and run on a mobile device itself, whereas web apps are accessed through a web browser and will adapt to whichever device you're viewing them on. Native mobile apps are built for a specific platform, such as iOS for Apple or Android for, well, pretty much everything else. They are downloaded and installed through an app store and have access to system resources, such as GPS and the camera function. Web apps, however, are not native to a particular system and do not need to be downloaded or installed. Due to their responsive nature, they may look and function a lot like mobile apps, which is where some confusion arises.

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Industries That Need a High Performing Low Latency Distributed Database

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.

Latency

Before jumping into specific industries that benefit from high-performing, low-latency, geo-distributed databases (it’s a mouthful, I know), let’s define a few terms here. High-performing is pretty self-explanatory so I’ll skip over that one. For the next term, I’ll refer to my colleague Jacob Cohen’s blog on Geo-Distributed Databases. Latency generally measures the duration between an action and a response. In user-facing applications, that can be narrowed down to the delay between when a user makes a request and when the application responds to a request. So, technologies that enable low latency usually improve performance and response times, leading to improved user experience and cost savings.

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HarperDB vs MongoDB vs PostgreSQL

Many people learn or understand new things relative to things they already know. This makes sense, it’s probably a natural instinct. When it comes to products and technology, a lot of people ask “how are you different,” but different from what? You need some sort of baseline to start from, so you can say, “Similar to X, but different because of Y.” Because of this, comparisons, competitive analysis, and feature matrices are a great way to understand which technology solutions are right for you. So today let’s do a comparison of three different database systems.

As stated in my Database Architectures and Use Cases article: In most cases, it’s not that one database is better than the other, it’s that one is a better fit for a specific use case due to numerous factors. The point of this article is not to determine which database is the best, but to help uncover the factors to consider when selecting a database for your specific project. With MongoDB and PostgreSQL being two of the most popular tools out there, you may already know that there are tons of resources comparing the two. However, with HarperDB being a net new database, I thought it might be helpful to throw it in the mix to provide further clarity.

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Database Drivers: Chauffeuring Your Data to Where it Needs to Go

Most, if not all, companies deal with complications and integration headaches somewhere in their data pipeline due to an inability or difficulty of connecting certain systems. Sometimes you have to add yet another technology to the lineup just to connect different systems and get your data to where it needs to go. However, in this modern-day, less is more. Most technologies that emerge are all about being more efficient and providing more functionality in a smaller package. If you can meet your data management needs with fewer tools, then it’s a win-win for cost-effectiveness, efficiency, and ease of use. Enter database drivers.

The Magical Adaptors

To put it plainly, each computer system needs some sort of adaptor or tool to be able to connect to other computer systems that are not the same. You can think about this in the physical or interior (computer system) sense. For a physical example, we all know the major frustration that comes along with upgrading your phone, when your chargers and headphones no longer fit in the ever-shrinking port on the bottom of the phone. However, you can buy an adapter to serve as a “middle man” to enable the connection. With computer systems, it’s the same but different. A database driver works like that physical phone adaptor, but instead of having to invest in an additional product to add to the tech stack, you can develop an adaptor/connector to extend the database functionality. Like an extension to a software package.

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Building a Database Written in Node.js From the Ground Up

The founding team at HarperDB built the first and only database written in Node.js. A few months back, our CEO Stephen Goldberg was invited to speak at a Women Who Code meetup to share the story of this (what some called crazy) endeavor. Stephen discussed the architectural layers of the database, demonstrated how to build a highly scalable and distributed product in Node.js, and demoed the inner workings of HarperDB. You can watch his talk at the link above, and even read a post from back in 2017, but since we all love Node.js and it’s an interesting topic, I’ll summarize here.

The main (and simplest) reason we chose to build a database in Node is that we knew it really well. We got flak for not choosing to Go, but people now accept that Go and Node are essentially head to head (in popularity and community support). Zach, one of our co-founders, recognized that with the time it would have taken to learn a new language, it would never be worth it.

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Database Architectures and Use Cases – Explained

With over 300 databases on the market, how do you determine which is right for your specific use case or skill set?

We continue to see the common debate of SQL vs. NoSQL and other database comparisons all over social media and platforms like dev.to. In most cases, it’s not that one database is better than the other, it’s that one is a better fit for a specific use case due to numerous factors.