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• ffmpeg remote url not working in lambda function

  9 septembre 2024, par Komal

  I have created lambda function to trim video.

  


  I run this command :

  


  /opt/bin/ffmpeg -protocol_whitelist file,http,https,tcp,tls -i 'https://source-bucket.s3.amazonaws.com/1.mp4' -ss 5 -t 10 -loglevel 48 -y -avoid_negative_ts 1 -acodec copy /tmp/output.mp4


  


  It gives following output :

  


  "ffmpeg version 7.0.2-static https://johnvansickle.com/ffmpeg/  Copyright (c) 2000-2024 the FFmpeg developers\n  built with gcc 8 (Debian 8.3.0-6)\n  configuration: --enable-gpl --enable-version3
--enable-static --disable-debug --disable-ffplay --disable-indev=sndio --disable-outdev=sndio --cc=gcc --enable-fontconfig --enable-frei0r --enable-gnutls --enable-gmp --enable-libgme --enable-gray --enable-libaom --enable-libfribidi --enable-libass --enable-libvmaf --enable-libfreetype --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-librubberband --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libvorbis --enable-libopus --enable-libtheora --enable-libvidstab --enable-libvo-amrwbenc --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libdav1d --enable-libxvid --enable-libzvbi --enable-libzimg\n  libavutil      59.  8.100 / 59.  8.100\n  libavcodec     61.  3.100 / 61.  3.100\n  libavformat    61.  1.100 / 61.  1.100\n  libavdevice    61.  1.100 /
61.  1.100\n  libavfilter    10.  1.100 / 10.  1.100\n  libswscale      8.  1.100 /  8.  1.100\n  libswresample   5.  1.100 /  5.  1.100\n  libpostproc    58.  1.100 / 58.  1.100\nSplitting the commandline.\nReading option '-protocol_whitelist' ... matched as AVOption 'protocol_whitelist' with argument 'file,http,https,tcp,tls'.\nReading option '-i' ... matched as input url with argument 'https://source-bucket.s3.amazonaws.com/1.mp4'.\nReading option '-ss' ... matched as option 'ss' (start transcoding at specified time) with argument '50'.\nReading option '-t' ... matched as option 't' (stop transcoding after specified duration) with argument '100'.\nReading option '-loglevel' ... matched as option 'loglevel' (set logging level) with argument '48'.\nReading option '-y' ... matched as option 'y' (overwrite output files) with argument '1'.\nReading option '-avoid_negative_ts' ... matched as AVOption 'avoid_negative_ts' with argument '1'.\nReading option '-acodec' ... matched as option 'acodec' (alias for -c:a (select encoder/decoder for audio streams)) with argument 'copy'.\nReading option '/tmp/output.mp4' ... matched as output url.\nFinished splitting the commandline.\nParsing a group of options: global .\nApplying option loglevel (set logging level) with argument 48.\nApplying option y (overwrite output files) with argument
1.\nSuccessfully parsed a group of options.\nParsing a group of options: input url https://source-bucket.s3.amazonaws.com/1.mp4.\nSuccessfully parsed a group of options.\nOpening an input file: https://source-bucket.s3.amazonaws.com/1.mp4.\n[AVFormatContext @ 0x70ea0c0] Opening 'https://source-bucket.s3.amazonaws.com/1.mp4' for reading\n"


  


  So, its not going beyond this : 'Opening 'https://source-bucket.s3.amazonaws.com/1.mp4' ; for reading\n'

  


  And after this, it immediately gets out. Need suggestions, where its going wrong. Same command working fine on local computer.

  


  I tried cloudfront and signed url as well, as mentioned in this post : How to read remote video on Amazon S3 using ffmpeg. But it doesnt work with url. Only working when downloaded into tmp folder.

  


  Following is the final log, which you can see is no timeout. It actually finish function execution within 2-3 seconds. I have timeout set for 15 mins to the function which is maximum :


  


  REPORT RequestId: 4acd7b38-017c-4dce-bb65-8f6fd3cf37e0  Duration: 1297.93 ms    Billed Duration: 1298 ms    Memory Size: 1024 MB    Max Memory Used: 103 MB Init Duration: 328.76 ms    


  


• Lean Analytics in a Privacy-First Environment – Bootcamp with Timo Dechau

  29 novembre 2024, par Daniel Crough — Banking and Financial Services, GDPR, Marketing, Privacy, Videos, Featured Banking Content

  In a recent bootcamp, Timo Dechau walked attendees through his approach to data and measurement in privacy-focused analytics environments. He demonstrates how to shift from a chaotic, ‘track-it-all’ mentality to a focused method that prioritizes quality over quantity. This post will summarize some of his key privacy-first analytics ideas, but be sure to check out the on-demand video for more detail.

  Watch the bootcamp on demand

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&lt;/script&gt;

  

  Unrestrained data collection leads to data bloat

  Marketing and the business world are experiencing a data problem. Analysts and business intelligence teams grapple with large amounts of data that aren’t always useful and are often incomplete. The idea that “more data is better” became a guiding principle in the early 2000s, encouraging companies to gather everything possible using all available data collection methods. This unrestrained pursuit often led to an unexpected problem : data bloat. Too much data, too little clarity. Digital marketers, analysts, and business leaders now try to navigate vast amounts of information that create more confusion than insight, especially when the data is incomplete due to privacy regulations.

  

  Cutting through the noise, focusing on what matters

  The “more data is better” mindset emerged when digital marketers were beginning to understand data’s potential. It seemed logical : more data should mean more opportunities to optimise, personalise, and drive results. But in practice, gathering every possible piece of data often leads to a cluttered, confusing pile of metrics that can mislead more than guide.

  This approach carries hidden costs. Excessive data collection burns resources, increases privacy concerns, and leaves teams unfocused. It’s easy to get lost trying to make sense of endless dashboards, metrics, and reports. More data doesn’t necessarily lead to better decisions ; it often just leads to more noise, hindering effective data management.

  Rethinking data management : From data overload to data mindfulness

  Data management has often prioritised comprehensive data gathering without considering the specific value of each data point. This approach has created more information, but not necessarily better insights.

  Data mindfulness is about taking a deliberate, focused approach to data collection and analysis. Instead of trying to collect everything, it emphasises gathering only what truly adds value. It’s about ensuring the data you collect serves a purpose and directly contributes to better insights and data-driven decision-making.

  Think of it like applying a “lean” methodology to data—trimming away the unnecessary and keeping only what is essential. Or consider embracing data minimalism to declutter your data warehouse, keeping only what truly sparks insight.

  

  Mindful data is ethical data

  Adopting a mindful approach to data can pay off in several ways :

  • Reduces overwhelm : When you reduce the clutter, you’re left with fewer, clearer metrics that lead to stronger decisions and actionable data insights.

  • Mitigates compliance risks : By collecting less, companies align better with privacy regulations and build trust with their customers. Privacy-first analytics and privacy-compliant analytics practices mean there’s no need for invasive tracking if it doesn’t add value—and customers will appreciate that.

  • Enhances data ethics : Focusing on the quality rather than the quantity of data collected ensures ethical data collection and management. Companies use data responsibly, respect user privacy, and minimise unnecessary data handling, strengthening customer relationships and brand integrity.

  • Improves data efficiency : Focused analytics means better use of resources. You’re spending less time managing meaningless metrics and more time working on meaningful insights. Many companies have found success by switching to a leaner, quality-first data approach, reporting sharper, more impactful results.

  Shifting towards simplicity and lean analytics

  If data mindfulness sounds appealing, here’s how you can get started :

  1. Ask the right questions. Before collecting any data, ask yourself : Why are we collecting this ? How will it drive value ? If you can’t answer these questions clearly, that data probably isn’t worth collecting. This is a key step in smart data management.

  2. Simplify metrics. Focus on the KPIs that truly matter for your business. Choose a handful of key metrics that reflect your goals rather than a sprawling list of nice-to-haves. Embracing data simplicity helps in targeting data collection effectively.

  3. Audit your current data. Review your existing data collection processes. Which metrics are you actively using to make decisions ? Eliminate any redundant or low-value metrics that create noise. Use ethical data management practices to ensure data efficiency and compliance. Understanding what is data management in this context is crucial.

  4. Implement lean analytics practices. Shift towards lean analytics by cutting down on unnecessary tracking. This can involve reducing reliance on multiple tracking scripts, simplifying your reporting, and setting up a streamlined dashboard focused on key outcomes. Embrace data reduction strategies to eliminate waste and boost effectiveness.

  Who should watch this bootcamp

  This bootcamp is perfect for data analysts, product managers, digital marketers and business leaders who are seeking a more streamlined approach to data measurement. If you’re interested in moving away from a chaotic “track-it-all” mentality and towards a focused, lean, and privacy-first analytics strategy, this workshop is for you.

  What you’ll discover

  • Practical steps : Learn actionable strategies to reduce data bloat and implement lean, privacy-first analytics in your organisation.

  • Real-life examples : Explore case studies of companies that have successfully adopted focused and privacy-first analytics.

  • Deep insights : Gain a deeper understanding of how to prioritise quality over quantity without sacrificing valuable insights.

  Watch the bootcamp on-demand

  For a comprehensive dive into these topics, watch the full workshop video or download the detailed transcript. Equip yourself with the knowledge and tools to transform your data management approach today.

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• Revision 3130 : La page d’inscription ne doit être visible que si elle est autorisée dans ...

  24 mars 2010, par kent1 — Log

  La page d’inscription ne doit être visible que si elle est autorisée dans la configuration de SPIP