Upcoming digital transformation trends in real estate

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5-Step Mailpit Configuration for Offline Email Testing

There are several ways to test and debug SMTP emails. The most common method is to use an online service like Mailtrap or Mailgun. While these tools are incredibly useful, they often come with a cost and require an internet connection, leading to resource constraints, especially with the free tiers.Developers often find themselves sending email notifications frequently to test different functionalities. In the search for efficient and cost-effective solutions, professionals often look for alternatives that could be used offline. Among these are two offline-based software options for testing emails locally: Helo (Paid) and Mailpit (Free | Open Source). Given that Mailpit is free and open-source, it is a go-to tool for many. In this blog, get familiar with how to install and configure Mailpit, making email testing both accessible and efficient.How to Use Mailpit for Offline Email Testing?If you’re wondering how to set up Mailpit, the following steps can help you get started.Step 1 - Download MailpitFollow the install instructions here:GitHub - axllent/mailpit: An email and SMTP testing tool with API for developersFor windows you can check the releases page. There can be multiple variants, namely AMD, ARM and x386. You would need to download the file based on your OS and processor type.AMD/AMD64 - For Intel/AMD processorsARM - ARM processor (mostly found in mobile devices)x386 - 32-bit Intel processorsStep 2 - Install MailpitWhile installing on Linux is easy, there are a few more steps to follow on Windows.Once the zip file is downloaded, extract it to a location as per your choice. The extracted contents will look like this:Step 3 - Add mailpit path to environmentTo add the mailpit path to environment $PATH variable, follow these steps:3.1. Search for env3.2. Click on the control panel item. It will open this window. Then click on the highlighted button. 3.3. You may choose to modify either the user or system variables. Select path and then click on edit according to your selection.3.4. In the newly opened window, click on New and add mailpit installation path like the following.3.5. Click OK → OK → Apply → OK.Step 4 - Using persistent storageAll email data will be lost once mailpit closes. To mitigate this, we can use a sqlite database.Just create a blank file but make sure to keep its extension as .sqliteYou can keep the file anywhere on your system but it is suggested to create the file in the installation folder.Step 5 - Launch MailpitRun the following command in your terminal to launch Mailpit with persistent storage.mailpit -d "" -vReplace  with the actual path to your sqlite DB file. -v is used to enable verbose loggingOnce the mailpit server starts, you can access it via 127.0.0.1:8025.Other customization options can be found in their official documentation.Bonus: Create alias to launch mailpitalias mpit=mailpit -d "C:\\mailpit\\maildb.sqlite" -v $*Wrapping UpThank you for reading! Hopefully, this guide on Mailpit configuration and use for offline email testing has been helpful. If you have any questions or need further assistance, please don't hesitate to contact us. We’d love to hear about your experiences with Mailpit or any other tools you use for email testing.Stay connected for more tips and tutorials!Happy coding!

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How Title Tags Optimization Can Help Your Website Get Better Traction?

It takes plenty of time and effort in getting your business seen for the people who matter the most. There are so many different components to consider when it comes to attracting your target audience. As title tags typically show up in the SERPs or when a post is shared on social media, it’s essential that a title tag should be impactful to make the user click on the link or post.In the past, Sun Dew Solutions have done a lot of correlation studies which shows a little alteration in title tag has resulted in a significant increase of CTAs in SERPs.So, let’s simplify what are Title Tags?Title tags are the HTML elements that are used to specify the theme of a webpage. Its primary job is to tell visitors and search engines in the most concise way what they can expect from the web page. Also, the web crawlers prioritize your page and its content through a ton of factors, with one very important SEO ranking metrics being the title tag. Let’s take a look at the best practices to ensure better traction to your website.Optimize the title tag on every web pageFor multiple web pages, it can be puzzling to come up with tags that are different from each other. That’s why mapping out your website and knowing where each page fits into the context of your subject matter is important. The title tag of a home page is going to be much different than a blog post or product page.Topical Relevance within the PageThe title tag is important for search engines and users to understand the topic of the page and the important keywords are evenly used in body copy, image alt, Meta description and URL, to rank the page organically in the search results. One should never ignore the content of the page when writing the title tag as it contributes to a higher bounce rate and also loses relevance to the topic of the page. You should always use words in the title tag that are also used in other parts of the web page to create a connection with the topic.Unique TagsUnique titles help search engines understand that your content is valued and drives higher click-through rates. While it may seem difficult to craft a unique title for every page when dealing with hundreds of such pages, modern CMS and code-based templates allow creating data-driven, unique titles for almost every page of your website.Don’t stuff keywords in your title tagRepeating or adding too many keywords in your title tag won’t improve your rankings. The search engines are focused on context – not on the frequency, and density of keywords. Choose words that are clear to the topic of the page and intent and stick with them in the title tag.Call to Action in SERPSearch engines typically display only the first 50–60 characters of a title tag. Hence, titles fewer than 60 characters are expected to have about 90 per cent display accuracy. Get your important message in there with concise and direct words to match the intent of the searcher to earn their click.The title tag is just one of many elements that are part of a detailed SEO plan. So make sure that they always have a foremost place in your SEO strategy. If you would like to know more about the optimization of title tags to get Better Traction for your website, feel free to reach out to our experts at sales@sundewsolutions.com or call us at +91-98367-81929. 

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Data Engineering Vs. Data Science - Key differences

Business Intelligence(BI) and Data Analytics are no longer buzzwords. Instead, enterprises are rapidly gravitating towards them to improve business performance. With adequate focus on data literacy, its collection, and data infrastructure, it is possible to accomplish results capable of enhancing revenue generation. Businesses generate a humongous amount of data today. This necessitates adopting intelligent and result-oriented data products to process the generated data and enhance its utility. The model "Data Science Hierarchy of Needs," suggested by Monica Rogati, further corroborates this. According to this model, Data Acquisition occupies the lowest level. It is succeeded by Data Engineering, Data Analytics, Business Analytics, Data Science, and AI (deployment and observability). Data engineering helps to connect data gathering with Data Science. Raw data cannot form the basis for building predictive models that establish trends and patterns. It needs to be converted into a usable or accessible form. This transformation is achieved using well-designed systems and pipelines. The designing, developing, testing, and maintenance of these pipelines and architectures fall under the purview of Data Engineering.Data Science deals with the extrapolation of knowledge and insights from transformed but noisy raw data, both structured and unstructured, and applying that knowledge to answer business-related queries for better decision-making and formulate metrics to improve implemented business processes.Data scientists achieve the above by using different scientific methods, algorithms, processes, and systems. Data engineers complement Data Scientists by providing them with the necessary framework and architecture. Data Engineering: Defining its scope and criticalityAnalysis of Big Data has completely changed the way of doing business. The collection and management of such a large volume of data require the development of an architecture that can handle structured and unstructured primary data and appropriately cleanse and transform it. The development and management of this data architecture is done by Data Engineers. They use different intricate methodologies to achieve this. Tools associated with the implemented technique range from AI to Data Integration. By choosing and employing the correct tools and techniques, Data engineers gather, clean and authenticate data to make it comprehensive and coherent for analysis by Data Scientists. Data Engineering is also important because it helps to refine SDLC(Software Development Life cycle), enhances data security, protect businesses from cyber attacks and cyber frauds and increase business domain knowledge. Its contribution to elongating the shelf-life of a business is indisputable. By converting unreadable data into readable form, Data Engineering empowers Data Scientists with secure data to generate accurate business insights. Data Science: Its meaning and definitionModern businesses are awash with data. With the expertise of professionals, it is possible to use available cutting-edge technology and tease actionable insights from the gigabytes of transformed data generated. These experts are Data Scientists. They add value to a business by providing enterprises with accurate analytics and insights for precise decision-making, deciphering trends to realign goals, improve workflows by focussing on its best practices and identifying growth and revenue-earning opportunities. Data Science is also used to provide quantifiable data-driven evidence, refine target audience and influence insightful talent acquisition. Data scientists are invaluable assets who analyze disparate data sources to generate meaningful insights that help businesses to grow, become profitable, and attain sustainability.Data Engineering vs. Data ScienceOften confused and thought to refer to the same thing, Data Engineering and Data Science are interwoven processes with distinct fundamental differences. Data engineering is the bridge that straddles the divide between data gathering and gaining value from data. It plays a critical role in the success of data science. Differences between the two primarily relate to:Data handling: Big Data can benefit businesses by creating multiple possibilities for improvement. An organization employs people skilled in Big Data management to maximize this advantage. Data engineers and Data scientists play a crucial role in this management.In the "Data Science Hierarchy of needs" pyramid, there is a clear distinction between the job roles essayed by Data engineers and Data scientists. Data engineers collect relevant data, transform it, and move it into pipelines so Data scientists can aggregate, optimize, test, and analyze it to generate real-time insights.  Data task classification: The work of a Data engineer is technically oriented as it involves three critical data actions, namely designing, building, and arranging Data "pipelines." They are Data Architects who design Big Data architecture and prepare it for analysis.Alternatively, Data scientists analyze, test, create and present data so enterprises can improve business decision-making and make it data-driven. Data engineers do technical work, while Data scientists are more business-oriented. Tools involved: Machine Learning(ML) and Deep Learning(DL) are to Data Science what ETL(Extract Transform Load) and ELT(Extract Load Transform) are to Data Engineering. ETL is the process of extracting, transforming, and loading the transformed data onto the original database.ML, a subset of Artificial Intelligence or AI, enables computers to forecast future scenarios automatically by using specific algorithms and existing information. DL uses artificial neural networks built on ML algorithms to allow the automatic learning of computers. Of algorithms and statistics: Data Engineering uses algorithms, but Data Science uses statistics. Algorithms comprise rules and processes that guide computers to carry out specific tasks. They deal with information retrieval, logical reasoning, and mathematical problems like calculus and linear algebra.Statistics involve the study and interpretation of numerical data. Other than using statistics to group, review, and analyze information, Data scientists also use it to apply quantifiable mathematical models to specific variables.ConclusionTo sum up, Data engineering plays a critical role in Data Science. But while they might occur together in almost all business applications, they are fundamentally different and require separate tools and skill sets for successful application.Data Engineering deals with data management, understanding, and extraction from big datasets. At the same time, Data Science is concerned with analyzing the cleaned and extracted data and using analytics to generate intelligent business insights. Together, they help businesses transition from average to excellent.