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Contribute to documentation
13 avril 2011Documentation is vital to the development of improved technical capabilities.
MediaSPIP welcomes documentation by users as well as developers - including : critique of existing features and functions articles contributed by developers, administrators, content producers and editors screenshots to illustrate the above translations of existing documentation into other languages
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Submit bugs and patches
13 avril 2011Unfortunately a software is never perfect.
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Selection of projects using MediaSPIP
2 mai 2011, parThe examples below are representative elements of MediaSPIP specific uses for specific projects.
MediaSPIP farm @ Infini
The non profit organizationInfini develops hospitality activities, internet access point, training, realizing innovative projects in the field of information and communication technologies and Communication, and hosting of websites. It plays a unique and prominent role in the Brest (France) area, at the national level, among the half-dozen such association. Its members (...)
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What is Multi-Touch Attribution ? (And How To Get Started)
2 février 2023, par Erin — Analytics TipsGood marketing thrives on data. Or more precisely — its interpretation. Using modern analytics software, we can determine which marketing actions steer prospects towards the desired action (a conversion event).
An attribution model in marketing is a set of rules that determine how various marketing tactics and channels impact the visitor’s progress towards a conversion.
Yet, as customer journeys become more complicated and involve multiple “touches”, standard marketing reports no longer tell the full picture.
That’s when multi-touch attribution analysis comes to the fore.
What is Multi-Touch Attribution ?
Multi-touch attribution (also known as multi-channel attribution or cross-channel attribution) measures the impact of all touchpoints on the consumer journey on conversion.
Unlike single-touch reporting, multi-touch attribution models give credit to each marketing element — a social media ad, an on-site banner, an email link click, etc. By seeing impacts from every touchpoint and channel, marketers can avoid false assumptions or subpar budget allocations.
To better understand the concept, let’s interpret the same customer journey using a standard single-touch report vs a multi-touch attribution model.
Picture this : Jammie is shopping around for a privacy-centred web analytics solution. She saw a recommendation on Twitter and ended up on the Matomo website. After browsing a few product pages and checking comparisons with other web analytics tools, she signs up for a webinar. One week after attending, Jammie is convinced that Matomo is the right tool for her business and goes directly to the Matomo website a starts a free trial.
- A standard single-touch report would attribute 100% of the conversion to direct traffic, which doesn’t give an accurate view of the multiple touchpoints that led Jammie to start a free trial.
- A multi-channel attribution report would showcase all the channels involved in the free trial conversion — social media, website content, the webinar, and then the direct traffic source.
In other words : Multi-touch attribution helps you understand how prospects move through the sales funnel and which elements tinder them towards the desired outcome.
Types of Attribution Models
As marketers, we know that multiple factors play into a conversion — channel type, timing, user’s stage on the buyer journey and so on. Various attribution models exist to reflect this variability.
First Interaction attribution model (otherwise known as first touch) gives all credit for the conversion to the first channel (for example — a referral link) and doesn’t report on all the other interactions a user had with your company (e.g., clicked a newsletter link, engaged with a landing page, or browsed the blog campaign).
First-touch helps optimise the top of your funnel and establish which channels bring the best leads. However, it doesn’t offer any insight into other factors that persuaded a user to convert.
Last Interaction attribution model (also known as last touch) allocates 100% credit to the last channel before conversion — be it direct traffic, paid ad, or an internal product page.
The data is useful for optimising the bottom-of-the-funnel (BoFU) elements. But you have no visibility into assisted conversions — interactions a user had prior to conversion.
Last Non-Direct attribution model model excludes direct traffic and assigns 100% credit for a conversion to the last channel a user interacted with before converting. For instance, a social media post will receive 100% of credit if a shopper buys a product three days later.
This model is more telling about the other channels, involved in the sales process. Yet, you’re seeing only one step backwards, which may not be sufficient for companies with longer sales cycles.
Linear attribution model distributes an equal credit for a conversion between all tracked touchpoints.
For instance, with a four touchpoint conversion (e.g., an organic visit, then a direct visit, then a social visit, then a visit and conversion from an ad campaign) each touchpoint would receive 25% credit for that single conversion.
This is the simplest multi-channel attribution modelling technique many tools support. The nuance is that linear models don’t reflect the true impact of various events. After all, a paid ad that introduced your brand to the shopper and a time-sensitive discount code at the checkout page probably did more than the blog content a shopper browsed in between.
Position Based attribution model allocates a 40% credit to the first and the last touchpoints and then spreads the remaining 20% across the touchpoints between the first and last.
This attribution model comes in handy for optimising conversions across the top and the bottom of the funnel. But it doesn’t provide much insight into the middle, which can skew your decision-making. For instance, you may overlook cases when a shopper landed via a social media post, then was re-engaged via email, and proceeded to checkout after an organic visit. Without email marketing, that sale may not have happened.
Time decay attribution model adjusts the credit, based on the timing of the interactions. Touchpoints that preceded the conversion get the highest score, while the first ones get less weight (e.g., 5%-5%-10%-15%-25%-30%).
This multi-channel attribution model works great for tracking the bottom of the funnel, but it underestimates the impact of brand awareness campaigns or assisted conversions at mid-stage.
Why Use Multi-Touch Attribution Modelling
Multi-touch attribution provides you with the full picture of your funnel. With accurate data across all touchpoints, you can employ targeted conversion rate optimisation (CRO) strategies to maximise the impact of each campaign.
Most marketers and analysts prefer using multi-touch attribution modelling — and for some good reasons.
Issues multi-touch attribution solves
- Funnel visibility. Understand which tactics play an important role at the top, middle and bottom of your funnel, instead of second-guessing what’s working or not.
- Budget allocations. Spend money on channels and tactics that bring a positive return on investment (ROI).
- Assisted conversions. Learn how different elements and touchpoints cumulatively contribute to the ultimate goal — a conversion event — to optimise accordingly.
- Channel segmentation. Determine which assets drive the most qualified and engaged leads to replicate them at scale.
- Campaign benchmarking. Compare how different marketing activities from affiliate marketing to social media perform against the same metrics.
How To Get Started With Multi-Touch Attribution
To make multi-touch attribution part of your analytics setup, follow the next steps :
1. Define Your Marketing Objectives
Multi-touch attribution helps you better understand what led people to convert on your site. But to capture that, you need to first map the standard purchase journeys, which include a series of touchpoints — instances, when a prospect forms an opinion about your business.
Touchpoints include :
- On-site interactions (e.g., reading a blog post, browsing product pages, using an on-site calculator, etc.)
- Off-site interactions (e.g., reading a review, clicking a social media link, interacting with an ad, etc.)
Combined these interactions make up your sales funnel — a designated path you’ve set up to lead people toward the desired action (aka a conversion).
Depending on your business model, you can count any of the following as a conversion :
- Purchase
- Account registration
- Free trial request
- Contact form submission
- Online reservation
- Demo call request
- Newsletter subscription
So your first task is to create a set of conversion objectives for your business and add them as Goals or Conversions in your web analytics solution. Then brainstorm how various touchpoints contribute to these objectives.
Web analytics tools with multi-channel attribution, like Matomo, allow you to obtain an extra dimension of data on touchpoints via Tracked Events. Using Event Tracking, you can analyse how many people started doing a desired action (e.g., typing details into the form) but never completed the task. This way you can quickly identify “leaking” touchpoints in your funnel and fix them.
2. Select an Attribution Model
Multi-attribution models have inherent tradeoffs. Linear attribution model doesn’t always represent the role and importance of each channel. Position-based attribution model emphasises the role of the last and first channel while diminishing the importance of assisted conversions. Time-decay model, on the contrary, downplays the role awareness-related campaigns played.
To select the right attribution model for your business consider your objectives. Is it more important for you to understand your best top of funnel channels to optimise customer acquisition costs (CAC) ? Or would you rather maximise your on-site conversion rates ?
Your industry and the average cycle length should also guide your choice. Position-based models can work best for eCommerce and SaaS businesses where both CAC and on-site conversion rates play an important role. Manufacturing companies or educational services providers, on the contrary, will benefit more from a time-decay model as it better represents the lengthy sales cycles.
3. Collect and Organise Data From All Touchpoints
Multi-touch attribution models are based on available funnel data. So to get started, you will need to determine which data sources you have and how to best leverage them for attribution modelling.
Types of data you should collect :
- General web analytics data : Insights on visitors’ on-site actions — visited pages, clicked links, form submissions and more.
- Goals (Conversions) : Reports on successful conversions across different types of assets.
- Behavioural user data : Some tools also offer advanced features such as heatmaps, session recording and A/B tests. These too provide ample data into user behaviours, which you can use to map and optimise various touchpoints.
You can also implement extra tracking, for instance for contact form submissions, live chat contacts or email marketing campaigns to identify repeat users in your system. Just remember to stay on the good side of data protection laws and respect your visitors’ privacy.
Separately, you can obtain top-of-the-funnel data by analysing referral traffic sources (channel, campaign type, used keyword, etc). A Tag Manager comes in handy as it allows you to zoom in on particular assets (e.g., a newsletter, an affiliate, a social campaign, etc).
Combined, these data points can be parsed by an app, supporting multi-touch attribution (or a custom algorithm) and reported back to you as specific findings.
Sounds easy, right ? Well, the devil is in the details. Getting ample, accurate data for multi-touch attribution modelling isn’t easy.
Marketing analytics has an accuracy problem, mainly for two reasons :
- Cookie consent banner rejection
- Data sampling application
Please note that we are not able to provide legal advice, so it’s important that you consult with your own DPO to ensure compliance with all relevant laws and regulations.
If you’re collecting web analytics in the EU, you know that showing a cookie consent banner is a GDPR must-do. But many consumers don’t often rush to accept cookie consent banners. The average consent rate for cookies in 2021 stood at 54% in Italy, 45% in France, and 44% in Germany. The consent rates are likely lower in 2023, as Google was forced to roll out a “reject all” button for cookie tracking in Europe, while privacy organisations lodge complaints against individual businesses for deceptive banners.
For marketers, cookie rejection means substantial gaps in analytics data. The good news is that you can fill in those gaps by using a privacy-centred web analytics tool like Matomo.
Matomo takes extra safeguards to protect user privacy and supports fully cookieless tracking. Because of that, Matomo is legally exempt from tracking consent in France. Plus, you can configure to use our analytics tool without consent banners in other markets outside of Germany and the UK. This way you get to retain the data you need for audience modelling without breaching any privacy regulations.
Data sampling application partially stems from the above. When a web analytics or multi-channel attribution tool cannot secure first-hand data, the “guessing game” begins. Google Analytics, as well as other tools, often rely on synthetic AI-generated data to fill in the reporting gaps. Respectively, your multi-attribution model doesn’t depict the real state of affairs. Instead, it shows AI-produced guesstimates of what transpired whenever not enough real-world evidence is available.
4. Evaluate and Select an Attribution Tool
Google Analytics (GA) offers several multi-touch attribution models for free (linear, time-decay and position-based). The disadvantage of GA multi-touch attribution is its lower accuracy due to cookie rejection and data sampling application.
At the same time, you cannot create custom credit allocations for the proposed models, unless you have the paid version of GA, Google Analytics 360. This version of GA comes with a custom Attribution Modeling Tool (AMT). The price tag, however, starts at USD $50,000 per year.
Matomo Cloud offers multi-channel conversion attribution as a feature and it is available as a plug-in on the marketplace for Matomo On-Premise. We support linear, position-based, first-interaction, last-interaction, last non-direct and time-decay modelling, based fully on first-hand data. You also get more precise insights because cookie consent isn’t an issue with us.
Most multi-channel attribution tools, like Google Analytics and Matomo, provide out-of-the-box multi-touch attribution models. But other tools, like Matomo On-Premise, also provide full access to raw data so you can develop your own multi-touch attribution models and do custom attribution analysis. The ability to create custom attribution analysis is particularly beneficial for data analysts or organisations with complex and unique buyer journeys.
Conclusion
Ultimately, multi-channel attribution gives marketers greater visibility into the customer journey. By analysing multiple touchpoints, you can establish how various marketing efforts contribute to conversions. Then use this information to inform your promotional strategy, budget allocations and CRO efforts.
The key to benefiting the most from multi-touch attribution is accurate data. If your analytics solution isn’t telling you the full story, your multi-touch model won’t either.
Collect accurate visitor data for multi-touch attribution modelling with Matomo. Start your free 21-day trial now.
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cannot convert FLV to MP4 despite compiling ffmpeg with all codecs
1er novembre 2013, par RubytasticTry to convert FLV to MP4 with below params, but it always fails. I included also list of codeces that are compiled in. Why It will not convert the FLV to MP4, who knows ?
ffmpeg -y -i stream2.flv -acodec libmp3lame -ar 44100 -ac 1 -vcodec libx264 stream2.mp4;
ffmpeg version git-2013-11-01-64a0ed1 Copyright (c) 2000-2013 the FFmpeg developers
built on Nov 1 2013 14:44:29 with gcc 4.4.7 (GCC) 20120313 (Red Hat 4.4.7-3)
configuration: --prefix=/root/ffmpeg_build --extra-cflags=-I/root/ffmpeg_build/include --extra-ldflags=-L/root/ffmpeg_build/lib --bindir=/root/bin --extra-libs=-ldl --enable-gpl --enable-nonfree --enable-libfdk_aac --enable-libmp3lame --enable-libopus --enable-libvorbis --enable-libvpx --enable-libx264
libavutil 52. 49.100 / 52. 49.100
libavcodec 55. 40.100 / 55. 40.100
libavformat 55. 20.100 / 55. 20.100
libavdevice 55. 5.100 / 55. 5.100
libavfilter 3. 90.100 / 3. 90.100
libswscale 2. 5.101 / 2. 5.101
libswresample 0. 17.104 / 0. 17.104
libpostproc 52. 3.100 / 52. 3.100
Input #0, flv, from 'stream2.flv':
Duration: 00:00:01.60, start: 0.000000, bitrate: 636 kb/s
Stream #0:0: Video: h264 (Baseline), yuv420p(tv), 640x480 [SAR 1:1 DAR 4:3], 11.92 tbr, 1k tbn, 60 tbc
Stream #0:1: Audio: speex, 16000 Hz, mono
[graph 1 input from stream 0:1 @ 0xb000d40] Invalid sample format (null)
Error opening filters!i followed the official compile documentation with all the codes, this is my full codec list :
ffmpeg version git-2013-11-01-64a0ed1 Copyright (c) 2000-2013 the FFmpeg developers
built on Nov 1 2013 14:44:29 with gcc 4.4.7 (GCC) 20120313 (Red Hat 4.4.7-3)
configuration: --prefix=/root/ffmpeg_build --extra-cflags=-I/root/ffmpeg_build/include --extra-ldflags=-L/root/ffmpeg_build/lib --bindir=/root/bin --extra-libs=-ldl --enable-gpl --enable-nonfree --enable-libfdk_aac --enable-libmp3lame --enable-libopus --enable-libvorbis --enable-libvpx --enable-libx264
libavutil 52. 49.100 / 52. 49.100
libavcodec 55. 40.100 / 55. 40.100
libavformat 55. 20.100 / 55. 20.100
libavdevice 55. 5.100 / 55. 5.100
libavfilter 3. 90.100 / 3. 90.100
libswscale 2. 5.101 / 2. 5.101
libswresample 0. 17.104 / 0. 17.104
libpostproc 52. 3.100 / 52. 3.100
Codecs:
D..... = Decoding supported
.E.... = Encoding supported
..V... = Video codec
..A... = Audio codec
..S... = Subtitle codec
...I.. = Intra frame-only codec
....L. = Lossy compression
.....S = Lossless compression
-------
D.VI.. 012v Uncompressed 4:2:2 10-bit
D.V.L. 4xm 4X Movie
D.VI.S 8bps QuickTime 8BPS video
.EVIL. a64_multi Multicolor charset for Commodore 64 (encoders: a64multi )
.EVIL. a64_multi5 Multicolor charset for Commodore 64, extended with 5th color (colram) (encoders: a64multi5 )
D.V..S aasc Autodesk RLE
D.VIL. aic Apple Intermediate Codec
DEVIL. amv AMV Video
D.V.L. anm Deluxe Paint Animation
D.V.L. ansi ASCII/ANSI art
DEVIL. asv1 ASUS V1
DEVIL. asv2 ASUS V2
D.VIL. aura Auravision AURA
D.VIL. aura2 Auravision Aura 2
D.V... avrn Avid AVI Codec
DEVI.. avrp Avid 1:1 10-bit RGB Packer
D.V.L. avs AVS (Audio Video Standard) video
DEVI.. avui Avid Meridien Uncompressed
DEVI.. ayuv Uncompressed packed MS 4:4:4:4
D.V.L. bethsoftvid Bethesda VID video
D.V.L. bfi Brute Force & Ignorance
D.V.L. binkvideo Bink video
D.VI.. bintext Binary text
DEVI.S bmp BMP (Windows and OS/2 bitmap)
D.V..S bmv_video Discworld II BMV video
D.VI.S brender_pix BRender PIX image
D.V.L. c93 Interplay C93
D.V.L. cavs Chinese AVS (Audio Video Standard) (AVS1-P2, JiZhun profile)
D.V.L. cdgraphics CD Graphics video
D.VIL. cdxl Commodore CDXL video
D.V.L. cinepak Cinepak
DEVIL. cljr Cirrus Logic AccuPak
D.VI.S cllc Canopus Lossless Codec
D.V.L. cmv Electronic Arts CMV video (decoders: eacmv )
D.V... cpia CPiA video format
D.V..S cscd CamStudio (decoders: camstudio )
D.VIL. cyuv Creative YUV (CYUV)
D.V.L. dfa Chronomaster DFA
D.V.LS dirac Dirac
DEVIL. dnxhd VC3/DNxHD
DEVI.S dpx DPX (Digital Picture Exchange) image
D.V.L. dsicinvideo Delphine Software International CIN video
DEVIL. dvvideo DV (Digital Video)
D.V..S dxa Feeble Files/ScummVM DXA
D.VI.S dxtory Dxtory
D.V.L. escape124 Escape 124
D.V.L. escape130 Escape 130
D.VILS exr OpenEXR image
DEV..S ffv1 FFmpeg video codec #1
DEVI.S ffvhuff Huffyuv FFmpeg variant
DEV..S flashsv Flash Screen Video v1
DEV.L. flashsv2 Flash Screen Video v2
D.V..S flic Autodesk Animator Flic video
DEV.L. flv1 FLV / Sorenson Spark / Sorenson H.263 (Flash Video) (decoders: flv ) (encoders: flv )
D.V..S fraps Fraps
D.VI.S frwu Forward Uncompressed
D.V.L. g2m Go2Meeting
DEV..S gif GIF (Graphics Interchange Format)
DEV.L. h261 H.261
DEV.L. h263 H.263 / H.263-1996, H.263+ / H.263-1998 / H.263 version 2
D.V.L. h263i Intel H.263
DEV.L. h263p H.263+ / H.263-1998 / H.263 version 2
DEV.LS h264 H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10 (encoders: libx264 libx264rgb )
D.V.LS hevc H.265 / HEVC
D.V.L. hnm4video HNM 4 video
DEVI.S huffyuv HuffYUV
D.V.L. idcin id Quake II CIN video (decoders: idcinvideo )
D.VI.. idf iCEDraw text
D.V.L. iff_byterun1 IFF ByteRun1 (decoders: iff )
D.V.L. iff_ilbm IFF ILBM (decoders: iff )
D.V.L. indeo2 Intel Indeo 2
D.V.L. indeo3 Intel Indeo 3
D.V.L. indeo4 Intel Indeo Video Interactive 4
D.V.L. indeo5 Intel Indeo Video Interactive 5
D.V.L. interplayvideo Interplay MVE video
DEVILS jpeg2000 JPEG 2000
DEVILS jpegls JPEG-LS
D.VIL. jv Bitmap Brothers JV video
D.V.L. kgv1 Kega Game Video
D.V.L. kmvc Karl Morton's video codec
D.VI.S lagarith Lagarith lossless
.EVI.S ljpeg Lossless JPEG
D.VI.S loco LOCO
D.V.L. mad Electronic Arts Madcow Video (decoders: eamad )
D.VIL. mdec Sony PlayStation MDEC (Motion DECoder)
D.V.L. mimic Mimic
DEVIL. mjpeg Motion JPEG
D.VIL. mjpegb Apple MJPEG-B
D.V.L. mmvideo American Laser Games MM Video
D.V.L. motionpixels Motion Pixels video
DEV.L. mpeg1video MPEG-1 video
DEV.L. mpeg2video MPEG-2 video (decoders: mpeg2video mpegvideo )
DEV.L. mpeg4 MPEG-4 part 2
..V.L. mpegvideo_xvmc MPEG-1/2 video XvMC (X-Video Motion Compensation)
D.V.L. msa1 MS ATC Screen
D.V.L. msmpeg4v1 MPEG-4 part 2 Microsoft variant version 1
DEV.L. msmpeg4v2 MPEG-4 part 2 Microsoft variant version 2
DEV.L. msmpeg4v3 MPEG-4 part 2 Microsoft variant version 3 (decoders: msmpeg4 ) (encoders: msmpeg4 )
D.V..S msrle Microsoft RLE
D.V.L. mss1 MS Screen 1
D.VIL. mss2 MS Windows Media Video V9 Screen
DEV.L. msvideo1 Microsoft Video 1
D.VI.S mszh LCL (LossLess Codec Library) MSZH
D.V.L. mts2 MS Expression Encoder Screen
D.VIL. mvc1 Silicon Graphics Motion Video Compressor 1
D.VIL. mvc2 Silicon Graphics Motion Video Compressor 2
D.V.L. mxpeg Mobotix MxPEG video
D.V.L. nuv NuppelVideo/RTJPEG
D.V.L. paf_video Amazing Studio Packed Animation File Video
DEVI.S pam PAM (Portable AnyMap) image
DEVI.S pbm PBM (Portable BitMap) image
DEVI.S pcx PC Paintbrush PCX image
DEVI.S pgm PGM (Portable GrayMap) image
DEVI.S pgmyuv PGMYUV (Portable GrayMap YUV) image
D.VIL. pictor Pictor/PC Paint
DEV..S png PNG (Portable Network Graphics) image
DEVI.S ppm PPM (Portable PixelMap) image
DEVIL. prores Apple ProRes (iCodec Pro) (decoders: prores prores_lgpl ) (encoders: prores prores_aw prores_ks )
D.VIL. ptx V.Flash PTX image
D.VI.S qdraw Apple QuickDraw
D.V.L. qpeg Q-team QPEG
DEV..S qtrle QuickTime Animation (RLE) video
DEVI.S r10k AJA Kona 10-bit RGB Codec
DEVI.S r210 Uncompressed RGB 10-bit
DEVI.S rawvideo raw video
D.VIL. rl2 RL2 video
DEV.L. roq id RoQ video (decoders: roqvideo ) (encoders: roqvideo )
D.V.L. rpza QuickTime video (RPZA)
DEV.L. rv10 RealVideo 1.0
DEV.L. rv20 RealVideo 2.0
D.V.L. rv30 RealVideo 3.0
D.V.L. rv40 RealVideo 4.0
D.V.L. sanm LucasArts SMUSH video
DEVI.S sgi SGI image
D.VI.S sgirle SGI RLE 8-bit
D.V.L. smackvideo Smacker video (decoders: smackvid )
D.V.L. smc QuickTime Graphics (SMC)
D.V... smv Sigmatel Motion Video (decoders: smvjpeg )
DEV.LS snow Snow
D.VIL. sp5x Sunplus JPEG (SP5X)
DEVI.S sunrast Sun Rasterfile image
DEV.L. svq1 Sorenson Vector Quantizer 1 / Sorenson Video 1 / SVQ1
D.V.L. svq3 Sorenson Vector Quantizer 3 / Sorenson Video 3 / SVQ3
DEVI.S targa Truevision Targa image
D.VI.. targa_y216 Pinnacle TARGA CineWave YUV16
D.V.L. tgq Electronic Arts TGQ video (decoders: eatgq )
D.V.L. tgv Electronic Arts TGV video (decoders: eatgv )
D.V.L. theora Theora
D.VIL. thp Nintendo Gamecube THP video
D.V.L. tiertexseqvideo Tiertex Limited SEQ video
DEVI.S tiff TIFF image
D.VIL. tmv 8088flex TMV
D.V.L. tqi Electronic Arts TQI video (decoders: eatqi )
D.V.L. truemotion1 Duck TrueMotion 1.0
D.V.L. truemotion2 Duck TrueMotion 2.0
D.V..S tscc TechSmith Screen Capture Codec (decoders: camtasia )
D.V.L. tscc2 TechSmith Screen Codec 2
D.VIL. txd Renderware TXD (TeXture Dictionary) image
D.V.L. ulti IBM UltiMotion (decoders: ultimotion )
DEVI.S utvideo Ut Video
DEVI.S v210 Uncompressed 4:2:2 10-bit
D.VI.S v210x
DEVI.. v308 Uncompressed packed 4:4:4
DEVI.. v408 Uncompressed packed QT 4:4:4:4
DEVI.S v410 Uncompressed 4:4:4 10-bit
D.V.L. vb Beam Software VB
D.VI.S vble VBLE Lossless Codec
D.V.L. vc1 SMPTE VC-1
D.V.L. vc1image Windows Media Video 9 Image v2
D.VIL. vcr1 ATI VCR1
D.VIL. vixl Miro VideoXL (decoders: xl )
D.V.L. vmdvideo Sierra VMD video
D.V..S vmnc VMware Screen Codec / VMware Video
D.V.L. vp3 On2 VP3
D.V.L. vp5 On2 VP5
D.V.L. vp6 On2 VP6
D.V.L. vp6a On2 VP6 (Flash version, with alpha channel)
D.V.L. vp6f On2 VP6 (Flash version)
DEV.L. vp8 On2 VP8 (decoders: vp8 libvpx ) (encoders: libvpx )
DEV.L. vp9 Google VP9 (decoders: vp9 libvpx-vp9 ) (encoders: libvpx-vp9 )
D.VILS webp WebP
DEV.L. wmv1 Windows Media Video 7
DEV.L. wmv2 Windows Media Video 8
D.V.L. wmv3 Windows Media Video 9
D.V.L. wmv3image Windows Media Video 9 Image
D.VIL. wnv1 Winnov WNV1
D.V.L. ws_vqa Westwood Studios VQA (Vector Quantized Animation) video (decoders: vqavideo )
D.V.L. xan_wc3 Wing Commander III / Xan
D.V.L. xan_wc4 Wing Commander IV / Xxan
D.VI.. xbin eXtended BINary text
DEVI.S xbm XBM (X BitMap) image
DEVIL. xface X-face image
DEVI.S xwd XWD (X Window Dump) image
DEVI.. y41p Uncompressed YUV 4:1:1 12-bit
D.V.L. yop Psygnosis YOP Video
DEVI.. yuv4 Uncompressed packed 4:2:0
D.V..S zerocodec ZeroCodec Lossless Video
DEVI.S zlib LCL (LossLess Codec Library) ZLIB
DEV..S zmbv Zip Motion Blocks Video
D.A.L. 8svx_exp 8SVX exponential
D.A.L. 8svx_fib 8SVX fibonacci
DEA.L. aac AAC (Advanced Audio Coding) (decoders: aac libfdk_aac ) (encoders: aac libfdk_aac )
D.A.L. aac_latm AAC LATM (Advanced Audio Coding LATM syntax)
DEA.L. ac3 ATSC A/52A (AC-3) (encoders: ac3 ac3_fixed )
D.A.L. adpcm_4xm ADPCM 4X Movie
DEA.L. adpcm_adx SEGA CRI ADX ADPCM
D.A.L. adpcm_afc ADPCM Nintendo Gamecube AFC
D.A.L. adpcm_ct ADPCM Creative Technology
D.A.L. adpcm_dtk ADPCM Nintendo Gamecube DTK
D.A.L. adpcm_ea ADPCM Electronic Arts
D.A.L. adpcm_ea_maxis_xa ADPCM Electronic Arts Maxis CDROM XA
D.A.L. adpcm_ea_r1 ADPCM Electronic Arts R1
D.A.L. adpcm_ea_r2 ADPCM Electronic Arts R2
D.A.L. adpcm_ea_r3 ADPCM Electronic Arts R3
D.A.L. adpcm_ea_xas ADPCM Electronic Arts XAS
DEA.L. adpcm_g722 G.722 ADPCM (decoders: g722 ) (encoders: g722 )
DEA.L. adpcm_g726 G.726 ADPCM (decoders: g726 ) (encoders: g726 )
D.A.L. adpcm_g726le G.726 ADPCM little-endian (decoders: g726le )
D.A.L. adpcm_ima_amv ADPCM IMA AMV
D.A.L. adpcm_ima_apc ADPCM IMA CRYO APC
D.A.L. adpcm_ima_dk3 ADPCM IMA Duck DK3
D.A.L. adpcm_ima_dk4 ADPCM IMA Duck DK4
D.A.L. adpcm_ima_ea_eacs ADPCM IMA Electronic Arts EACS
D.A.L. adpcm_ima_ea_sead ADPCM IMA Electronic Arts SEAD
D.A.L. adpcm_ima_iss ADPCM IMA Funcom ISS
D.A.L. adpcm_ima_oki ADPCM IMA Dialogic OKI
DEA.L. adpcm_ima_qt ADPCM IMA QuickTime
D.A.L. adpcm_ima_rad ADPCM IMA Radical
D.A.L. adpcm_ima_smjpeg ADPCM IMA Loki SDL MJPEG
DEA.L. adpcm_ima_wav ADPCM IMA WAV
D.A.L. adpcm_ima_ws ADPCM IMA Westwood
DEA.L. adpcm_ms ADPCM Microsoft
D.A.L. adpcm_sbpro_2 ADPCM Sound Blaster Pro 2-bit
D.A.L. adpcm_sbpro_3 ADPCM Sound Blaster Pro 2.6-bit
D.A.L. adpcm_sbpro_4 ADPCM Sound Blaster Pro 4-bit
DEA.L. adpcm_swf ADPCM Shockwave Flash
D.A.L. adpcm_thp ADPCM Nintendo Gamecube THP
D.A.L. adpcm_xa ADPCM CDROM XA
DEA.L. adpcm_yamaha ADPCM Yamaha
DEA..S alac ALAC (Apple Lossless Audio Codec)
D.A.L. amr_nb AMR-NB (Adaptive Multi-Rate NarrowBand) (decoders: amrnb )
D.A.L. amr_wb AMR-WB (Adaptive Multi-Rate WideBand) (decoders: amrwb )
D.A..S ape Monkey's Audio
D.A.L. atrac1 ATRAC1 (Adaptive TRansform Acoustic Coding)
D.A.L. atrac3 ATRAC3 (Adaptive TRansform Acoustic Coding 3)
..A.L. atrac3p ATRAC3+ (Adaptive TRansform Acoustic Coding 3+)
D.A.L. binkaudio_dct Bink Audio (DCT)
D.A.L. binkaudio_rdft Bink Audio (RDFT)
D.A.L. bmv_audio Discworld II BMV audio
..A.L. celt Constrained Energy Lapped Transform (CELT)
DEA.L. comfortnoise RFC 3389 Comfort Noise
D.A.L. cook Cook / Cooker / Gecko (RealAudio G2)
D.A.L. dsicinaudio Delphine Software International CIN audio
DEA.LS dts DCA (DTS Coherent Acoustics) (decoders: dca ) (encoders: dca )
..A.L. dvaudio
DEA.L. eac3 ATSC A/52B (AC-3, E-AC-3)
D.A.L. evrc EVRC (Enhanced Variable Rate Codec)
DEA..S flac FLAC (Free Lossless Audio Codec)
DEA.L. g723_1 G.723.1
D.A.L. g729 G.729
D.A.L. gsm GSM
D.A.L. gsm_ms GSM Microsoft variant
D.A.L. iac IAC (Indeo Audio Coder)
..A.L. ilbc iLBC (Internet Low Bitrate Codec)
D.A.L. imc IMC (Intel Music Coder)
D.A.L. interplay_dpcm DPCM Interplay
D.A.L. mace3 MACE (Macintosh Audio Compression/Expansion) 3:1
D.A.L. mace6 MACE (Macintosh Audio Compression/Expansion) 6:1
D.A.L. metasound Voxware MetaSound
D.A..S mlp MLP (Meridian Lossless Packing)
D.A.L. mp1 MP1 (MPEG audio layer 1) (decoders: mp1 mp1float )
DEA.L. mp2 MP2 (MPEG audio layer 2) (decoders: mp2 mp2float )
DEA.L. mp3 MP3 (MPEG audio layer 3) (decoders: mp3 mp3float ) (encoders: libmp3lame )
D.A.L. mp3adu ADU (Application Data Unit) MP3 (MPEG audio layer 3) (decoders: mp3adu mp3adufloat )
D.A.L. mp3on4 MP3onMP4 (decoders: mp3on4 mp3on4float )
D.A..S mp4als MPEG-4 Audio Lossless Coding (ALS) (decoders: als )
D.A.L. musepack7 Musepack SV7 (decoders: mpc7 )
D.A.L. musepack8 Musepack SV8 (decoders: mpc8 )
DEA.L. nellymoser Nellymoser Asao
DEA.L. opus Opus (Opus Interactive Audio Codec) (decoders: libopus ) (encoders: libopus )
D.A.L. paf_audio Amazing Studio Packed Animation File Audio
DEA.L. pcm_alaw PCM A-law / G.711 A-law
D.A..S pcm_bluray PCM signed 16|20|24-bit big-endian for Blu-ray media
D.A..S pcm_dvd PCM signed 20|24-bit big-endian
DEA..S pcm_f32be PCM 32-bit floating point big-endian
DEA..S pcm_f32le PCM 32-bit floating point little-endian
DEA..S pcm_f64be PCM 64-bit floating point big-endian
DEA..S pcm_f64le PCM 64-bit floating point little-endian
D.A..S pcm_lxf PCM signed 20-bit little-endian planar
DEA.L. pcm_mulaw PCM mu-law / G.711 mu-law
DEA..S pcm_s16be PCM signed 16-bit big-endian
DEA..S pcm_s16be_planar PCM signed 16-bit big-endian planar
DEA..S pcm_s16le PCM signed 16-bit little-endian
DEA..S pcm_s16le_planar PCM signed 16-bit little-endian planar
DEA..S pcm_s24be PCM signed 24-bit big-endian
DEA..S pcm_s24daud PCM D-Cinema audio signed 24-bit
DEA..S pcm_s24le PCM signed 24-bit little-endian
DEA..S pcm_s24le_planar PCM signed 24-bit little-endian planar
DEA..S pcm_s32be PCM signed 32-bit big-endian
DEA..S pcm_s32le PCM signed 32-bit little-endian
DEA..S pcm_s32le_planar PCM signed 32-bit little-endian planar
DEA..S pcm_s8 PCM signed 8-bit
DEA..S pcm_s8_planar PCM signed 8-bit planar
DEA..S pcm_u16be PCM unsigned 16-bit big-endian
DEA..S pcm_u16le PCM unsigned 16-bit little-endian
DEA..S pcm_u24be PCM unsigned 24-bit big-endian
DEA..S pcm_u24le PCM unsigned 24-bit little-endian
DEA..S pcm_u32be PCM unsigned 32-bit big-endian
DEA..S pcm_u32le PCM unsigned 32-bit little-endian
DEA..S pcm_u8 PCM unsigned 8-bit
D.A.L. pcm_zork PCM Zork
D.A.L. qcelp QCELP / PureVoice
D.A.L. qdm2 QDesign Music Codec 2
..A.L. qdmc QDesign Music
DEA.L. ra_144 RealAudio 1.0 (14.4K) (decoders: real_144 ) (encoders: real_144 )
D.A.L. ra_288 RealAudio 2.0 (28.8K) (decoders: real_288 )
D.A..S ralf RealAudio Lossless
DEA.L. roq_dpcm DPCM id RoQ
DEA..S s302m SMPTE 302M
D.A..S shorten Shorten
D.A.L. sipr RealAudio SIPR / ACELP.NET
D.A.L. smackaudio Smacker audio (decoders: smackaud )
..A.L. smv SMV (Selectable Mode Vocoder)
D.A.L. sol_dpcm DPCM Sol
DEA... sonic Sonic
.EA... sonicls Sonic lossless
..A.L. speex Speex
D.A..S tak TAK (Tom's lossless Audio Kompressor)
D.A..S truehd TrueHD
D.A.L. truespeech DSP Group TrueSpeech
DEA..S tta TTA (True Audio)
D.A.L. twinvq VQF TwinVQ
D.A.L. vima LucasArts VIMA audio
D.A.L. vmdaudio Sierra VMD audio
DEA.L. vorbis Vorbis (decoders: vorbis libvorbis ) (encoders: vorbis libvorbis )
..A.L. voxware Voxware RT29 Metasound
D.A... wavesynth Wave synthesis pseudo-codec
DEA.LS wavpack WavPack
D.A.L. westwood_snd1 Westwood Audio (SND1) (decoders: ws_snd1 )
D.A..S wmalossless Windows Media Audio Lossless
D.A.L. wmapro Windows Media Audio 9 Professional
DEA.L. wmav1 Windows Media Audio 1
DEA.L. wmav2 Windows Media Audio 2
D.A.L. wmavoice Windows Media Audio Voice
D.A.L. xan_dpcm DPCM Xan
..D... dvd_nav_packet DVD Nav packet
..D... klv SMPTE 336M Key-Length-Value (KLV) metadata
DES... ass ASS (Advanced SSA) subtitle
DES... dvb_subtitle DVB subtitles (decoders: dvbsub ) (encoders: dvbsub )
..S... dvb_teletext DVB teletext
DES... dvd_subtitle DVD subtitles (decoders: dvdsub ) (encoders: dvdsub )
..S... eia_608 EIA-608 closed captions
D.S... hdmv_pgs_subtitle HDMV Presentation Graphic Stream subtitles (decoders: pgssub )
D.S... jacosub JACOsub subtitle
D.S... microdvd MicroDVD subtitle
DES... mov_text MOV text
D.S... mpl2 MPL2 subtitle
D.S... pjs PJS (Phoenix Japanimation Society) subtitle
D.S... realtext RealText subtitle
D.S... sami SAMI subtitle
DES... srt SubRip subtitle with embedded timing
DES... ssa SSA (SubStation Alpha) subtitle
DES... subrip SubRip subtitle
D.S... subviewer SubViewer subtitle
D.S... subviewer1 SubViewer v1 subtitle
D.S... text raw UTF-8 text
D.S... vplayer VPlayer subtitle
D.S... webvtt WebVTT subtitle
DES... xsub XSUB -
Adventures In NAS
1er janvier, par Multimedia Mike — GeneralIn my post last year about my out-of-control single-board computer (SBC) collection which included my meager network attached storage (NAS) solution, I noted that :
I find that a lot of my fellow nerds massively overengineer their homelab NAS setups. I’ll explore this in a future post. For my part, people tend to find my homelab NAS solution slightly underengineered.
So here I am, exploring this is a future post. I’ve been in the home NAS game a long time, but have never had very elaborate solutions for such. For my part, I tend to take an obsessively reductionist view of what constitutes a NAS : Any small computer with a pool of storage and a network connection, running the Linux operating system and the Samba file sharing service.
Many home users prefer to buy turnkey boxes, usually that allow you to install hard drives yourself, and then configure the box and its services with a friendly UI. My fellow weird computer nerds often buy cast-off enterprise hardware and set up more resilient, over-engineered solutions, as long as they have strategies to mitigate the noise and dissipate the heat, and don’t mind the electricity bills.
If it works, awesome ! As an old hand at this, I am rather stuck in my ways, however, preferring to do my own stunts, both with the hardware and software solutions.
My History With Home NAS Setups
In 1998, I bought myself a new computer — beige box tower PC, as was the style as the time. This was when normal people only had one computer at most. It ran Windows, but I was curious about this new thing called “Linux” and learned to dual boot that. Later that year, it dawned on me that nothing prevented me from buying a second ugly beige box PC and running Linux exclusively on it. Further, it could be a headless Linux box, connected by ethernet, and I could consolidate files into a single place using this file sharing software named Samba.
I remember it being fairly onerous to get Samba working in those days. And the internet was not quite so helpful in those days. I recall that the thing that blocked me for awhile was needing to know that I had to specify an entry for the Samba server machine in the LMHOSTS (Lanman hosts) file on the Windows 95 machine.
However, after I cracked that code, I have pretty much always had some kind of ad-hoc home NAS setup, often combined with a headless Linux development box.
In the early 2000s, I built a new beige box PC for a file server, with a new hard disk, and a coworker tutored me on setting up a (P)ATA UDMA 133 (or was it 150 ? anyway, it was (P)ATA’s last hurrah before SATA conquered all) expansion card and I remember profiling that the attached hard drive worked at a full 21 MBytes/s reading. It was pretty slick. Except I hadn’t really thought things through. You see, I had a hand-me-down ethernet hub cast-off from my job at the time which I wanted to use. It was a 100 Mbps repeater hub, not a switch, so the catch was that all connected machines had to be capable of 100 Mbps. So, after getting all of my machines (3 at the time) upgraded to support 10/100 ethernet (the old off-brand PowerPC running Linux was the biggest challenge), I profiled transfers and realized that the best this repeater hub could achieve was about 3.6 MBytes/s. For a long time after that, I just assumed that was the upper limit of what a 100 Mbps network could achieve. Obviously, I now know that the upper limit ought to be around 11.2 MBytes/s and if I had gamed out that fact in advance, I would have realized it didn’t make sense to care about super-fast (for the time) disk performance.
At this time, I was doing a lot for development for MPlayer/xine/FFmpeg. I stored all of my multimedia material on this NAS. I remember being confused when I was working with Y4M data, which is raw frames, which is lots of data. xine, which employed a pre-buffering strategy, would play fine for a few seconds and then stutter. Eventually, I reasoned out that the files I was working with had a data rate about twice what my awful repeater hub supported, which is probably the first time I came to really understand and respect streaming speeds and their implications for multimedia playback.
Smaller Solutions
For a period, I didn’t have a NAS. Then I got an Apple AirPort Extreme, which I noticed had a USB port. So I bought a dual drive brick to plug into it and used that for a time. Later (2009), I had this thing called the MSI Wind Nettop which is the only PC I’ve ever seen that can use a CompactFlash (CF) card for a boot drive. So I did just that, and installed a large drive so it could function as a NAS, as well as a headless dev box. I’m still amazed at what a low-power I/O beast this thing is, at least when compared to all the ARM SoCs I have tried in the intervening 1.5 decades. I’ve had spinning hard drives in this thing that could read at 160 MBytes/s (‘dd’ method) and have no trouble saturating the gigabit link at 112 MBytes/s, all with its early Intel Atom CPU.Around 2015, I wanted a more capable headless dev box and discovered Intel’s line of NUCs. I got one of the fat models that can hold a conventional 2.5″ spinning drive in addition to the M.2 SATA SSD and I was off and running. That served me fine for a few years, until I got into the ARM SBC scene. One major limitation here is that 2.5″ drives aren’t available in nearly the capacities that make a NAS solution attractive.
Current Solution
My current NAS solution, chronicled in my last SBC post– the ODroid-HC2, which is a highly compact ARM SoC with an integrated USB3-SATA bridge so that a SATA drive can be connected directly to it :
ODROID-HC2 NAS
I tend to be weirdly proficient at recalling dates, so I’m surprised that I can’t recall when I ordered this and put it into service. But I’m pretty sure it was circa 2018. It’s only equipped with an 8 TB drive now, but I seem to recall that it started out with only a 4 TB drive. I think I upgraded to the 8 TB drive early in the pandemic in 2020, when ISPs were implementing temporary data cap amnesty and I was doing what a r/DataHoarder does.
The HC2 has served me well, even though it has a number of shortcomings for a hardware set chartered for NAS :
- While it has a gigabit ethernet port, it’s documented that it never really exceeds about 70 MBytes/s, due to the SoC’s limitations
- The specific ARM chip (Samsung Exynos 5422 ; more than a decade old as of this writing) lacks cryptography instructions, slowing down encryption if that’s your thing (e.g., LUKS)
- While the SoC supports USB3, that block is tied up for the SATA interface ; the remaining USB port is only capable of USB2 speeds
- 32-bit ARM, which prevented me from running certain bits of software I wanted to try (like Minio)
- Only 1 drive, so no possibility for RAID (again, if that’s your thing)
I also love to brag on the HC2’s power usage : I once profiled the unit for a month using a Kill-A-Watt and under normal usage (with the drive spinning only when in active use). The unit consumed 4.5 kWh… in an entire month.
New Solution
Enter the ODroid-HC4 (I purchased mine from Ameridroid but Hardkernel works with numerous distributors) :
ODroid-HC4 with an SSD and a conventional drive
I ordered this earlier in the year and after many months of procrastinating and obsessing over the best approach to take with its general usage, I finally have it in service as my new NAS. Comparing point by point with the HC2 :
- The gigabit ethernet runs at full speed (though a few things on my network run at 2.5 GbE now, so I guess I’ll always be behind)
- The ARM chip (Amlogic S905X3) has AES cryptography acceleration and handles all the LUKS stuff without breaking a sweat ; “cryptsetup benchmark” reports between 500-600 MBytes/s on all the AES variants
- The USB port is still only USB2, so no improvement there
- 64-bit ARM, which means I can run Minio to simulate block storage in a local dev environment for some larger projects I would like to undertake
- Supports 2 drives, if RAID is your thing
How I Set It Up
How to set up the drive configuration ? As should be apparent from the photo above, I elected for an SSD (500 GB) for speed, paired with a conventional spinning HDD (18 TB) for sheer capacity. I’m not particularly trusting of RAID. I’ve watched it fail too many times, on systems that I don’t even manage, not to mention that aforementioned RAID brick that I had attached to the Apple AirPort Extreme.I had long been planning to use bcache, the block caching interface for Linux, which can use the SSD as a speedy cache in front of the more capacious disk. There is also LVM cache, which is supposed to achieve something similar. And then I had to evaluate the trade-offs in whether I wanted write-back, write-through, or write-around configurations.
This was all predicated on the assumption that the spinning drive would not be able to saturate the gigabit connection. When I got around to setting up the hardware and trying some basic tests, I found that the conventional HDD had no trouble keeping up with the gigabit data rate, both reading and writing, somewhat obviating the need for SSD acceleration using any elaborate caching mechanisms.
Maybe that’s because I sprung for the WD Red Pro series this time, rather than the Red Plus ? I’m guessing that conventional drives do deteriorate over the years. I’ll find out.
For the operating system, I stuck with my newest favorite Linux distro : DietPi. While HardKernel (parent of ODroid) makes images for the HC units, I had also used DietPi for the HC2 for the past few years, as it tends to stay more up to date.
Then I rsync’d my data from HC2 -> HC4. It was only about 6.5 TB of total data but it took days as this WD Red Plus drive is only capable of reading at around 10 MBytes/s these days. Painful.
For file sharing, I’m pretty sure most normal folks have nice web UIs in their NAS boxes which allow them to easily configure and monitor the shares. I know there are such applications I could set up. But I’ve been doing this so long, I just do a bare bones setup through the terminal. I installed regular Samba and then brought over my smb.conf file from the HC2. 1 by 1, I tested that each of the old shares were activated on the new NAS and deactivated on the old NAS. I also set up a new share for the SSD. I guess that will just serve as a fast I/O scratch space on the NAS.
The conventional drive spins up and down. That’s annoying when I’m actively working on something but manage not to hit the drive for like 5 minutes and then an application blocks while the drive wakes up. I suppose I could set it up so that it is always running. However, I micro-manage this with a custom bash script I wrote a long time ago which logs into the NAS and runs the “date” command every 2 minutes, appending the output to a file. As a bonus, it also prints data rate up/down stats every 5 seconds. The spinning file (“nas-main/zz-keep-spinning/keep-spinning.txt”) has never been cleared and has nearly a quarter million lines. I suppose that implies that it has kept the drive spinning for 1/2 million minutes which works out to around 347 total days. I should compare that against the drive’s SMART stats, if I can remember how. The earliest timestamp in the file is from March 2018, so I know the HC2 NAS has been in service at least that long.
For tasks, vintage cron still does everything I could need. In this case, that means reaching out to websites (like this one) and automatically backing up static files.
I also have to have a special script for starting up. Fortunately, I was able to bring this over from the HC2 and tweak it. The data disks (though not boot disk) are encrypted. Those need to be unlocked and only then is it safe for the Samba and Minio services to start up. So one script does all that heavy lifting in the rare case of a reboot (this is the type of system that’s well worth having on a reliable UPS).
Further Work
I need to figure out how to use the OLED display on the NAS, and how to make it show something more useful than the current time and date, which is what it does in its default configuration with HardKernel’s own Linux distro. With DietPi, it does nothing by default. I’m thinking it should be able to show the percent usage of each of the 2 drives, at a minimum.I also need to establish a more responsible backup regimen. I’m way too lazy about this. Fortunately, I reason that I can keep the original HC2 in service, repurposed to accept backups from the main NAS. Again, I’m sort of micro-managing this since a huge amount of data isn’t worth backing up (remember the whole DataHoarder bit), but the most important stuff will be shipped off.
The post Adventures In NAS first appeared on Breaking Eggs And Making Omelettes.
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