Upgrade : Get Unstuck from the LAMP Stack to the MEAN Stack
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Upgrade : Get Unstuck from the LAMP Stack to the MEAN Stack
Understanding the role each technology plays in the stack is crucial when moving from LAMP to MEAN for web development. Whether you’re looking for consolidation of technologies or to leverage in-house JavaScript expertise, the MEAN stack can offer a lot to a flexible web development organization. The team here at BACK& wanted to try and help sort out the pros and cons of each stack. So below I’ll detail the MEAN stack, how it compares to the LAMP stack, and offer a few tips for optimizing your choices.
The Operating System
The first choice in any tech stack is the operating system. While the LAMP stack locked the operating system to a variant of Linux, the MEAN stack has no such restrictions. Linux is still a good choice for an app built on MEAN, but it is by no means the only option; any operating system that can run Node.js is a viable alternative.
The Web Server
In the MEAN stack, the web server – provided by Apache in LAMP – is provided by Node.js. This can improve the performance of the application, as Node.js is entirely non-blocking and event-based, allowing for true concurrency among requests. Node.js is lightweight and relatively new, however, which ultimately means that your organization will be largely on its own when it comes to non-standard extensions. While there is active plug-in development for Node.js, the technology is not as matured as Apache. This usually means that you need to write your own plug-ins to cover the areas where Node.js is missing functionality. Additionally, choosing Node.js locks all code on your web server into JavaScript. For new development this isn’t a major concern, but converting a back-end of significant complexity can be time-consuming.
The Data Store
The MEAN stack replaces LAMP’s use of MySQL (or another relational database) with MongoDB (or an equivalent non-relational database). For many web apps, this will be the most significant change. Translating the data in an existing SQL database requires a lot of forethought to eliminate redundant/unnecessary object attributes, and will likely require a custom software suite to accomplish. However, once this is done the database will be much faster for data retrieval.
The Code
MEAN makes use of Express.js and AngularJS to drive web page presentation and control flow, tasks covered by PHP or Python in the LAMP stack. Express.js serves as the controller layer, directing application flow and marshaling data for AngularJS, which handles data presentation. The primary benefits offered by these scripts are a simplified back-end architecture – for example, Express.js weighs in at only 1,143 lines of code – and a purely client-side presentation layer in AngularJS that can be easily embedded into any existing web application. Furthermore, usage of Express.js and AngularJSo n top of Node.js gives your technology stack the added benefit of being entirely in one language, meaning your front-end developers now have the ability to trace all the way down the stack without having to learn another programming language.
Additional Considerations
Probably the biggest choice to be faced when converting from LAMP to MEAN is the choice of data store. While the MEAN stack is designed to work with a non-relational database, there are plug-ins for Node.js that allow the stack to run off of a relational database just as easily. The front end handles everything in JSON, so the only true consideration is how the data is stored before it is retrieved, or the difference between Relational and Non-Relational databases.
Relational databases, with their support for highly complex structured queries, lend themselves well to performing complex calculations with data. Non-relational databases excel at managing operational data, such as a list of objects in a system. The lack of a schema allows for fluid object definitions that don’t require extensive code changes, and by removing the need for extensive and complicated queries the system can often operate more efficiently than a similar architecture build over a relational database.
Conclusion
Converting to the MEAN stack gives your development team a number of benefits, the three most significant being a single language from top to bottom, flexibility in deployment platform, and enhanced speed in data retrieval. However, the switch is not without trade-offs; any existing code will either need to be rewritten in JavaScript or integrated into the new stack in a non-obvious manner. Ultimately the choice to switch to a MEAN stack from LAMP will be based in your organization and the priorities for the project under development.
Courtesy of Blog.BackAnd
Ag 3.0 : Why IoT, Big Data & Smart Farming is the Future of Agriculture
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The farming industry will become arguably more important than ever before in the next few decades.
The world will need to produce 70% more food in 2050 than it did in 2006 in order to feed the growing population of the Earth, according to the UN Food and Agriculture Organization. To meet this demand, farmers and agricultural companies are turning to the Internet of Things for analytics and greater production capabilities.
Technological innovation in farming is nothing new. Handheld tools were the standards hundreds of years ago, and then the Industrial Revolution brought about the cotton gin. The 1800s brought about grain elevators, chemical fertilizers, and the first gas-powered tractor. Fast forward to the late 1900s, when farmers start using satellites to plan their work.
The IoT is set to push the future of farming to the next level. Smart agriculture is already becoming more commonplace among farmers, and high tech farming is quickly becoming the standard thanks to agricultural drones and sensors.
Below, we've outlined IoT applications in agriculture and how "Internet of Things farming" will help farmers meet the world's food demands in the coming years.
High Tech Farming: Precision Farming & Smart Agriculture
Farmers have already begun employing some high tech farming techniques and technologies in order to improve the efficiency of their day-to-day work. For example, sensors placed in fields allow farmers to obtain detailed maps of both the topography and resources in the area, as well as variables such as acidity and temperature of the soil. They can also access climate forecasts to predict weather patterns in the coming days and weeks.
Farmers can use their smartphones to remotely monitor their equipment, crops, and livestock, as well as obtain stats on their livestock feeding and produce. They can even use this technology to run statistical predictions for their crops and livestock.
And drones have become an invaluable tool for farmers to survey their lands and generate crop data.
As a concrete example, John Deere (one of the biggest names in farming equipment) has begun connecting its tractors to the Internet and has created a method to display data about farmers' crop yields. Furthermore, the company is pioneering self-driving tractors, which would free up farmers to perform other tasks and further increase efficiency.
All of these techniques help make up precision farming or precision agriculture, the process of using satellite imagery and other technology (such as sensors) to observe and record data with the goal of improving production output while minimizing cost and preserving resources.
Future of Farming: IoT, Agricultural Sensors, & Farming Drones
Smart agriculture and precision farming are taking off, but they could just be the precursors to even greater use of technology in the farming world.
BI Intelligence, Business Insider's premium research service, predicts that IoT device installations in the agriculture world will increase from 30 million in 2015 to 75 million in 2020, for a compound annual growth rate of 20%.
The U.S. currently leads the world in IoT smart agriculture, as it produces 7,340 kgs of cereal (e.g. wheat, rice, maize, barley, etc.) per hectare (2.5 acres) of farmland, compared to the global average of 3,851 kgs of cereal per hectare.
And this efficiency should only improve in the coming decades as farms become more connected. OnFarm, which makes a connected farm IoT platform, expects the average farm to generate an average of 4.1 million data points per day in 2050, up from 190,000 in 2014.
Furthermore, OnFarm ran several studies and discovered that for the average farm, yield rose by 1.75%, energy costs dropped $7 to $13 per acre, and water use for irrigation fell by 8%.
Given all of the potential benefits of these IoT applications in agriculture, it's understandable that farmers are increasingly turning to agricultural drones and satellites for the future of farming.
Courtesy of BI
Courtesy of BI
How To: 4 Ways to Make Big Data Actionable
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The oil and gas industry can be a volatile and unpredictable marketplace. However, this sector plays a fundamental role in global commerce, and if it is not running efficiently, the effects ripple throughout our economy.
There's good reason why: Demand and consumption have been growing steadily over the past few decades. Now, the United States is the third largest producer, generating more than 12 percent of the world’s oil.
So, how did oil and gas operations manage to eliminate inefficiencies, expedite production and improve processes, all the while driving profitability? Somewhat ironically, these operatons become technology companies.
According to Shiva Rajagopalan, CEO of Seven Lakes Technologies, at the end of the day, the COO lives and breathes numbers, and the numbers all need to match.
Rajagopalan is an oil and gas industry expert, who developed his enterprise software solution to give oil and gas operators better insights into the data they need to eliminate inefficiencies in their drilling and production operations.
In an interview he told me that he believes that companies as large as those in oil and gas, or as small as a five-person startup, can make their data actionable to drive down costs and increase production. Here's how.
1. Be disciplined with big data.
According to a recent survey by The Economist Intelligence Unit, companies that master the emerging discipline of big data management can reap significant rewards and separate themselves from their competitors.
Big data is a term that describes the large volume of data -- both structured and unstructured -- that inundates a business on a day-to-day basis. And, thanks to constant tech innovations, such as sensing capabilities, the volume of data produced by a drilling operation is enormous.
“But it’s not the amount of data that’s important. It’s what organizations do with the data that matters,” Rajagopalan said.
For the oil and gas industry, superior data discipline reduces the inaccurate reporting of active well counts and decreases incorrect reserves reported, and penalties assessed due to incorrect updates in the forecasting system.
While oil and gas is an enormous case study for how companies can integrate data into their operations, the benefits they receive hold true for all companies. Implementing proper workflow analytics helps to automate processes and streamline manual business operations to make every arm of the company more efficient.
“With software, organizations can shrink the time it takes to convert data into value in the hands of its operators, Rajagopalan said. "Software improves the quality of analytics and creates confidence within the organization, so those on the front lines are making informed decisions.
"Being disciplined with big data means you get accounting, production and budget systems all communicating effectively."
2. Scale successful results.
The best way for the corner office executive to be on the same page as those in the field is via access to the same information. However, this requires the organization to trust that the data is flowing smoothly across the organization without multiple sources causing errors or bad reporting.
“Repeatability provides employees with a level of trust in the data and systems with which they make decisions,” Rajagopalan said. “They reconcile information less and take accountability more. They continue to respond in this way, and the system continues to shrink their time to achieve business value.”
3. Avoid the garbage-in, garbage-out effect.
In order to balance production and expenses, companies with field operations will require a more surgical effort than in previous decades. The unstructured data, which may not have been considered in the past, becomes relevant.
Working in industries with field workers, all of this data needs to be tagged, integrated and synced together with the appropriate validation checkpoints so everyone from the COO to the asset manager has the exact same view of operations.
4. Work smarter, not harder.
Yes, it’s an age-old cliché, especially for entrepreneurs. But clichés are only repeated so much in the first place because they are so relevant. Oil and gas is an important part of the world’s energy balance. It is simply time to rethink and refocus to outwit and outmaneuver the current market forces.
“Equip your team to drive significant and lasting value. Disparate source systems, ungoverned information and unreliable data block their view to operational excellence. Give them tools to turn meaningful insights into shared action,” Rajagopalan said.
By employing and utilizing intuitive new technology that keeps the company moving in the same direction while enabling actionable insights across the organization, COOs can set aggressive goals and lead their companies to the end zone time after time in a sustainable manner.
Courtesy Of Ayodeji Onibalusi
Courtesy Of Ayodeji Onibalusi