Front-end Development Training by Technnovation Labs
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With the increase of mobile systems, gadgets & smart phone apps, front-end designers & developers are in great demand and simply knowing how to convert a Photoshop template to HTML is not enough - nor just do simple DOM (document object model) trickery with jQuery. Nowadays, you’re expected to be an expert in many areas. So are you ready to take your code to the next level? Understand how deep the CSS3 rabbit hole goes? Join in this jam-packed course & know everything from the Experts of Front-end Web Development. Gain in-depths of HTML5, CSS3 and JavaScript techniques directly from top thoughts forming the industry. Spend money on yourself and become a more flexible front-end designer today!
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Using the SQLExecutor for Data Quality Management: aka One man's love for the...Safe Software The SQLExecutor is one of FME’s most powerful and flexible transformers. Pivvot maintains a robust internal metadata hierarchy used to support ingestion and curation of thousands of external data sources that must be managed for quality before entering our platform. By using the SQLExecutor, Pivvot can efficiently detect problems and perform analysis before data is extracted from our staging environment, removing the need for rollbacks or cycles waisted on a failed job. This presentation will walk through three distinct examples of how Pivvot uses the SQLExecutor to engage its metadata hierarchy and integrate with its Data Quality Management workflows efficiently and within the source postgres database. Spatial Validation –Validating spatial prerequisites before entering a production environment. Reference Data Validation - Dynamically validate domain-ed columns across any table and multiple columns per table. Practical De-duplication - Removing identical or near-identical well point locations from two distinct source datasets in the same table.
From Manual to Auto Searching- FME in the Driver's Seat
From Manual to Auto Searching- FME in the Driver's SeatSafe Software Finding a specific car online can be a time-consuming task, especially when checking multiple dealer websites. A few years ago, I faced this exact problem while searching for a particular vehicle in New Zealand. The local classified platform, Trade Me (similar to eBay), wasn’t yielding any results, so I expanded my search to second-hand dealer sites—only to realise that periodically checking each one was going to be tedious. That’s when I noticed something interesting: many of these websites used the same platform to manage their inventories. Recognising this, I reverse-engineered the platform’s structure and built an FME workspace that automated the search process for me. By integrating API calls and setting up periodic checks, I received real-time email alerts when matching cars were listed. In this presentation, I’ll walk through how I used FME to save hours of manual searching by creating a custom car-finding automation system. While FME can’t buy a car for you—yet—it can certainly help you find the one you’re after!
“Key Requirements to Successfully Implement Generative AI in Edge Devices—Opt...
“Key Requirements to Successfully Implement Generative AI in Edge Devices—Opt...Edge AI and Vision Alliance
You are not excused! How to avoid security blind spots on the way to production
You are not excused! How to avoid security blind spots on the way to productionMichele Leroux Bustamante
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