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If you’ve ever worked on a personal data science project, you’ve probably spent a lot of time scouring the internet for interesting datasets to analyze.
It can be fun to sift through dozens of datasets to find the best fit, but it can also be frustrating to download and import multiple CSV files, only to find that the data is just missing, not so interesting. Fortunately, there are online repositories that keep sets of data and (mostly) remove uninteresting ones.
MQTT is a communication protocol that has taken off in the IIoT community. It’s a lightweight, efficient protocol that works through a publisher/broker/subscriber model. It creates an easy way for field devices to communicate and retrieve data from a single location. In this tutorial, we’ll go over MQTT and dive into an example of how you can publish data using a groov EPIC PAC and retrieve the data using an MQTT client.
The Open Systems Interconnection (OSI) model is used to describe the way machines and applications communicate between themselves. The model was developed with the idea that a provider can utilize different protocols or software components at every layer without having to re-architect the entire structure. The MQTT protocol will come into play at Layer 7 of the model; on top of TCP/IP (Layer 4). The reason that this is important is that this keeps a lot of infrastructure in place while redefining how the packets are being sent between devices.
This blog is the first in a series introducing Alluxio as the data platform to unify data silos across heterogeneous environments. The next blog will include insights from PrestoDB committer Beinan Wang to uncover the value for analytics use cases, specifically with PrestoDB as the compute engine.
The ability to quickly and easily access data and extract insights is increasingly important to any organization. With the explosion of data sources, the trends of cloud migration, and the fragmentation of technology stacks and vendors, there has been a huge demand for data infrastructure to achieve agility, cost-effectiveness, and desired performance.
In this article, we will talk about the Repository Pattern and how we implemented it in our Laravel application to solve a scalability problem. The Repository Pattern is one of the most discussed patterns due to a lot of conflict with ORMs. Many developers think that not all ORMs are suitable for this type of design.
We discuss this topic in details below explaining why and how we implemented it in our application.
Over 25 percent of employees don’t trust their employers, and an even greater 50 percent think that their employers aren’t open or upfront with them. The lack of trust among employees is due to the lack of transparency in the workplace.
In digitally transformed organizations with digitized workflows, decentralized teams, and remote employees, embracing and maintaining transparency across different workplace tools can become even more difficult.
Vaadin Dev Day is an event that provides in-depth technical topics and insights to the Vaadin community. In the latest edition of the event, we learned about data providers for high-performance data access, advanced JavaScript and TypeScript integrations, and the future of Vaadin Flow. Here's a recap of the event.
In this talk, Simon Martinelli explains how to efficiently connect Vaadin Flow applications to databases using JPA and the DataProvider implementations that are available in the framework.
App development stacks have been improving so rapidly and effectively that today there are a number of easy, straightforward paths to push code to production, on the cloud platform of your choice. But what use are applications without the data that users interact with? Persistent data is such an indispensable piece of the IT puzzle that it’s perhaps the reason the other pieces even exist.
Enter cloud and internet scale requirements, essentially mandating that back-end services must be independently scalable / modular subsystems to succeed. Traditionally, this requirement has been difficult in the extreme for stateful systems. No doubt, database as-a-service (DBaaS) has made provisioning, operations, and security easier. But as anyone who has tried to run databases on Kubernetes will tell you: auto scaling databases, especially ones that are easy for developers to use, remain out of reach for mere mortals.
In the last two decades, many businesses have had to change their models as business operations continue to complicate. The major challenge companies face today is that a large amount of data is generated from multiple data sources. So, data analytics have introduced filters to various data sources to detect this problem. They need analytics and business intelligence to access all their data sources to make better business decisions.
It is obvious that the company needs this data to make decisions based on predicted market trends, market forecasts, customer requirements, future needs, etc. But how do you get all your company data in one place to make a proper decision? Data ingestion consolidates your data and stores it in one place.
"Data and analytics for all!” — the admirable, new mantra for today’s companies. But it’s not easy to put all of an organization’s analytical data and assets into the hands of everyone that needs it. That’s why embarking on this democratization initiative requires you to be prepared to overcome the five monumental challenges you undoubtedly will face.
Join us for this interactive webcast where we will: explore the recommended components of an all-encompassing, extended analytics architecture; dive into the details of what stands between you and data democratization success, and; reveal how a new open data architecture maximizes data access with minimal data movement and no data copies.
You’d be hard-pressed to find someone in the IT security space who will argue against the importance of risk preparedness. Unfortunately, more often than not, people will talk-the-talk without walking the proverbial walk. It sounds smart: be ready for potential attacks before they happen. But we have a long way to go to put this sentiment into practice. Accidents are unplanned, and we're never quite as prepared as we should be. The "that will never happen to us" attitude is rampant among the enterprise, especially when it comes to cybersecurity.
Risk preparedness is something organizations need to start taking seriously, as seen by the recent Florida water plant hack, among others. If they don't, the outcomes could be devastating. Imagine a stadium of sick Super Bowl attendees or worse. While the focus has been largely on protecting big businesses or federal entities with lots of valuable data, no one is truly safe from bad actors — not even local municipalities. In fact, these could be even more dangerous targets when you consider something as serious as compromising a community’s water supply or information theft.
The very nature of big data integration requires an organization to become more flexible in some ways; particularly when gathering input and metrics from such varied sources as mobile apps, browser heuristics, A / V input, software logs, and more. The number of different methodologies, protocols, and formats that your organization needs to ingest while complying with both internal and government-mandated standards can be staggering.
Is there a clean and discreet way to achieve fast data integration and still reap all of the benefits of big data analytics?
Determining what data a user, application, or device can access can be one of the most important decisions an organization faces. You don’t have to be a healthcare or financial institution to be responsible for customer data. But to maintain customer trust, your organization may want to treat all customer data as such.
The data access problem is complex. Elements of policy may be driven by IT, human resources, legal, or even by finance. Policies might be enforced at different points depending on where data travels and how it is consumed. Policies might be enforced at the network layer through remote-access systems, at the database layer, within cloud infrastructure, or at endpoints like email and files. Most of these platforms inherently implement a permissive security policy.
Whether you’re a software development team lead at a prestigious financial institution assigned to redact personally identifiable information (PII) before releasing the next bankruptcy report, or you're part of a development shop that has just been contracted by a large healthcare organization to help update their systems to meet HIPAA requirements, chances are you’ve been asked to obfuscate sensitive data.
Protecting sensitive data is not an uncommon requirement when building applications. In a recent survey, 71% of companies indicated they utilized encryption for some of their data in transit; 53% utilized encryption for some of their data in storage.
To gather insights on the current and future state of API management, we asked IT professionals from 18 companies to share their thoughts. We asked them, "How does your company use APIs?" Here's what they told us:
Here's who shared their insights:
Spring Boot offers fantastic support for accessing data with JPA through the use of interfaces that extends to the repository. If we add to this the ease with which REST services are created, as explained in this entry, we can make an application that offers an API to access our preferred database very easily.
But if we want to implement HATEOAS in our project or if there are a lot of entry points in our API, we must write a lot of code. To solve this problem, Spring Boot offers the library Spring Data Rest. With it, we can write an API REST to access our database easily and without writing much code.
IT is the heart of every business. Business records, plans, employee records and so much more is handled via IT. Even the smallest companies rely on computing to handle all the important aspects of their business.
No matter how convenient IT support is, it still has a stranglehold on the budget. Purchasing and repairing servers and hard drives are increasingly expensive, which is problematic for small businesses.