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• Google Analytics 4 and GDPR : Everything You Need to Know

  17 mai 2022, par Erin

  Four years have passed since the European General Data Protection Regulation (GDPR, also known as DSGVO in German, and RGPD in French) took effect.

  That’s ample time to get compliant, especially for an organisation as big and innovative as Google. Or is it ? 

  If you are wondering how GDPR affects Google Analytics 4 and what the compliance status is at present, here’s the lowdown. 

  Is Google Analytics 4 GDPR Compliant ?

  No. As of mid-2022, Google Analytics 4 (GA4) isn’t fully GDPR compliant. Despite adding extra privacy-focused features, GA4 still has murky status with the European regulators. After the invalidation of the Privacy Shield framework in 2020, Google is yet to regulate EU-US data protection. At present, the company doesn’t sufficiently protect EU citizens’ and residents’ data against US surveillance laws. This is a direct breach of GDPR.

  Google Analytics and GDPR : a Complex Relationship 

  European regulators have scrutinised Google since GDPR came into effect in 2018.

  While the company took steps to prepare for GDPR provisions, it didn’t fully comply with important regulations around user data storage, transfer and security.

  The relationship between Google and EU regulators got more heated after the Court of Justice of the European Union (CJEU) invalidated the Privacy Shield — a leeway Google used for EU-US data transfers. After 2020, GDPR litigation against Google followed. 

  This post summarises the main milestones in this story and explains the consequences for Google Analytics users. 

  

  2018 : Google Analytics Meets GDPR 

  In 2018, the EU adopted the General Data Protection Regulation (GDPR) — a set of privacy and data security laws, covering all member states. Every business interacting with EU citizens and/or residents had to comply.

  GDPR harmonised data protection laws across member states and put down extra provisions for what constitutes sensitive personal information (or PII). Broadly, PII includes any data about the person’s :

  • Racial or ethnic origin 
  • Employment status 
  • Religious or political beliefs
  • State of health 
  • Genetic or biometric data 
  • Financial records (such as payment method data)
  • Address and phone numbers 

  Businesses were barred from collecting this information without explicit consent (and even with it in some cases). If collected, such sensitive information is also subject to strict requirements on how it should be stored, secured, transferred and used. 

  7 Main GDPR Principles Explained 

  Article 5 of the GDPR lays out seven main GDPR principles for personal data and privacy protection : 

  • Lawfulness, fairness and transparency — data must be obtained legally, collected with consent and in adherence to laws. 
  • Purpose limitation — all personal information must be collected for specified, explicit and legal purposes. 
  • Data minimisation — companies must collect only necessary and adequate data, aligned with the stated purpose. 
  • Accuracy — data accuracy must be ensured at all times. Companies must have mechanisms to erase or correct inaccurate data without delays. 
  • Storage limitation — data must be stored only for as long as the stated purpose suggests. Though there’s no upper time limit on data storage. 
  • Integrity and confidentiality (security) — companies must take measures to ensure secure data storage and prevent unlawful or unauthorised access to it. 
  • Accountability — companies must be able to demonstrate adherence to the above principles. 

  Google claimed to have taken steps to make all of their products GDPR compliant ahead of the deadline. But in practice, this wasn’t always the case.

  In March 2018, a group of publishers admonished Google for not providing them with enough tools for GDPR compliance :

  “[Y]ou refuse to provide publishers with any specific information about how you will collect, share and use the data. Placing the full burden of obtaining new consent on the publisher is untenable without providing the publisher with the specific information needed to provide sufficient transparency or to obtain the requisite specific, granular and informed consent under the GDPR.”

  The proposed Google Analytics GDPR consent form was hard to implement and lacked customisation options. In fact, Google “makes unilateral decisions” on how the collected data is stored and used. 

  Users had no way to learn about or control all intended uses of people’s data — which made compliance with the second clause impossible. 

  Unsurprisingly, Google was among the first companies to face a GDPR lawsuit (together with Facebook). 

  By 2019, French data regulator CNIL, successfully argued that Google wasn’t sufficiently disclosing its data collection across products — and hence in breach of GDPR. After a failed appeal, Google had to pay a €50 million fine and promise to do better. 

  2019 : Google Analytics 4 Announcement 

  Throughout 2019, Google rightfully attempted to resolve some of its GDPR shortcomings across all products, Google Universal Analytics (UA) included. 

  They added a more visible consent mechanism for online tracking and provided extra compliance tips for users to follow. In the background, Google also made tech changes to its data processing mechanism to get on the good side of regulations.

  Though Google addressed some of the issues, they missed others. A 2019 independent investigation found that Google real-time-bidding (RTB) ad auctions still used EU citizens’ and residents’ data without consent, thanks to a loophole called “Push Pages”. But they managed to quickly patch this up before the allegations had made it to court. 

  In November 2019, Google released a beta version of the new product version — Google Analytics 4, due to replace Universal Analytics. 

  GA4 came with a set of new privacy-focused features for ticking GDPR boxes such as :

  • Data deletion mechanism. Users can now request to surgically extract certain data from the Analytics servers via a new interface. 
  • Shorter data retention period. You can now shorten the default retention period to 2 months by default (instead of 14 months) or add a custom limit.  
  • IP Anonymisation. GA4 doesn’t log or store IP addresses by default. 

  Google Analytics also updated its data processing terms and made changes to its privacy policy. 

  Though Google made some progress, Google Analytics 4 still has many limitations — and isn’t GDPR compliant. 

  2020 : Privacy Shield Invalidation Ruling 

  As part of the 2018 GDPR preparations, Google named its Irish entity (Google Ireland Limited) as the “data controller” legally responsible for EEA and Swiss users’ information. 

  The company announcement says : 

  

  Initially, Google assumed that this legal change would help them ensure GDPR compliance as “legally speaking” a European entity was set in charge of European data. 

  Practically, however, EEA consumers’ data was still primarily transferred and processed in the US — where most Google data centres are located. Until 2020, such cross-border data transfers were considered legal thanks to the Privacy Shield framework. 

  But in July 2020, The EU Court of Justice ruled that this framework doesn’t provide adequate data protection to digitally transmitted data against US surveillance laws. Hence, companies like Google can no longer use it. The Swiss Federal Data Protection and Information Commissioner (FDPIC) reached the same conclusion in September 2020. 

  The invalidation of the Privacy Shield framework put Google in a tough position.

   Article 14. f of the GDPR explicitly states : 

  “The controller (the company) that intends to carry out a transfer of personal data to a recipient (Analytics solution) in a third country or an international organisation must provide its users with information on the place of processing and storage of its data”.

  Invalidation of the Privacy Shield framework prohibited Google from moving data to the US. At the same time, GDPR provisions mandated that they must disclose proper data location. 

  But Google Analytics (like many other products) had no a mechanism for : 

  • Guaranteeing intra-EU data storage 
  • Selecting a designated regional storage location 
  • Informing users about data storage location or data transfers outside of the EU 

  And these factors made Google Analytics in direct breach of GDPR — a territory, where they remain as of 2022.

  2020-2022 : Google GDPR Breaches and Fines 

  The 2020 ruling opened Google to GDPR lawsuits from country-specific data regulators.

  Google Analytics in particular was under a heavy cease-fire. 

  • Sweden first fined Google for violating GDPR for no not fulfilling its obligations to request data delisting in 2020. 
  • France rejected Google Analytics 4 IP address anonymisation function as a sufficient measure for protecting cross-border data transfers. Even with it, US intelligence services can still access user IPs and other PII. France declared Google Analytics illegal and pressed a €150 million fine. 
  • Austria also found Google Analytics GDPR non-compliant and proclaimed the service as “illegal”. The authority now seeks a fine too. 

  The Dutch Data Protection Authority and  Norwegian Data Protection Authority also found Google Analytics guilty of a GDPR breach and seek to limit Google Analytics usage. 

  New privacy controls in Google Analytics 4 do not resolve the underlying issue — unregulated, non-consensual EU-US data transfer. 

  Google Analytics GDPR non-compliance effectively opens any website tracking or analysing European visitors to legal persecution.

  In fact, this is already happening. noyb, a European privacy-focused NGO, has already filed over 100 lawsuits against European websites using Google Analytics.

  2022 : Privacy Shield 2.0. Negotiations

  Google isn’t the only US company affected by the Privacy Shield framework invalidation. The ruling puts thousands of digital companies at risk of non-compliance.

  To settle the matter, US and EU authorities started “peace talks” in spring 2022.

  European Commission President Ursula von der Leyen said that they are working with the Biden administration on the new agreement that will “enable predictable and trustworthy data flows between the EU and US, safeguarding the privacy and civil liberties.” 

  However, it’s just the beginning of a lengthy negotiation process. The matter is far from being settled and contentious issues remain as we discussed on Twitter (come say hi !).

  

  For one, the US isn’t eager to modify its surveillance laws and is mostly willing to make them “proportional” to those in place in the EU. These modifications may still not satisfy CJEU — which has the power to block the agreement vetting or invalidate it once again. 

  While these matters are getting hashed out, Google Analytics users, collecting data about EU citizens and/or residents, remain on slippery grounds. As long as they use GA4, they can be subject to GDPR-related lawsuits. 

  To Sum It Up 

  • Google Analytics 4 and Google Universal Analytics are not GDPR compliant because of Privacy Shield invalidation in 2020. 
  • French and Austrian data watchdogs named Google Analytics operations “illegal”. Swedish, Dutch and Norwegian authorities also claim it’s in breach of GDPR. 
  • Any website using GA for collecting data about European citizens and/or residents can be taken to court for GDPR violations (which is already happening). 
  • Privacy Shield 2.0 Framework discussions to regulate EU-US data transfers have only begun and may take years. Even if accepted, the new framework(s) may once again be invalidated by local data regulators as has already happened in the past. 

  Time to Get a GDPR Compliant Google Analytics Alternative 

  Retaining 100% data ownership is the optimal path to GDPR compliance.

  By selecting a transparent web analytics solution that offers 100% data ownership, you can rest assured that no “behind the scenes” data collection, processing or transfers take place. 

  Unlike Google Analytics 4, Matomo offers all of the features you need to be GDPR compliant : 

  • Full data anonymisation 
  • Single-purpose data usage 
  • Easy consent and an opt-out mechanism 
  • First-party cookies usage by default 
  • Simple access to collect data 
  • Fast data removals 
  • EU-based data storage for Matomo Cloud (or storage in the country of your choice with Matomo On-Premise)

  Learn about your audiences in a privacy-centred way and protect your business against unnecessary legal exposure. 

  Start your 21-day free trial (no credit card required) to see how fully GDPR-compliant website analytics works ! 

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• Segmentation Analytics : How to Leverage It on Your Site

  27 octobre 2023, par Erin — Analytics Tips

  The deeper you go with your customer analytics, the better your insights will be.

  The result ? Your marketing performance soars to new heights.

  Customer segmentation is one of the best ways businesses can align their marketing strategies with an effective output to generate better results. Marketers know that targeting the right people is one of the most important aspects of connecting with and converting web visitors into customers.

  By diving into customer segmentation analytics, you’ll be able to transform your loosely defined and abstract audience into tangible, understandable segments, so you can serve them better.

  In this guide, we’ll break down customer segmentation analytics, the different types, and how you can delve into these analytics on your website to grow your business.

  What is customer segmentation ?

  Before we dive into customer segmentation analytics, let’s take a step back and look at customer segmentation in general. 

  Customer segmentation is the process of dividing your customers up into different groups based on specific characteristics.

  These groups could be based on demographics like age or location or behaviours like recent purchases or website visits. 

  By splitting your audience into different segments, your marketing team will be able to craft highly targeted and relevant marketing campaigns that are more likely to convert.

  Additionally, customer segmentation allows businesses to gain new insights into their audience. For example, by diving deep into different segments, marketers can uncover pain points and desires, leading to increased conversion rates and return on investment.

  But, to grasp the different customer segments, organisations need to know how to collect, digest and interpret the data for usable insights to improve their business. That’s where segmentation analytics comes in.

  What is customer segmentation analytics ?

  Customer segmentation analytics splits customers into different groups within your analytics software to create more detailed customer data and improve targeting.

  

  With customer segmentation, you’re splitting your customers into different groups. With customer segmentation analytics, you’re doing this all within your analytics platform so you can understand them better.

  One example of splitting your customers up is by country. For example, let’s say you have a global customer base. So, you go into your analytics software and find that 90% of your website visitors come from five countries : the UK, the US, Australia, Germany and Japan.

  In this area, you could then create customer segmentation subsets based on these five countries. Moving forward, you could then hop into your analytics tool at any point in time and analyse the segments by country. 

  For example, if you wanted to see how well your recent marketing campaign impacted your Japanese customers, you could look at your Japanese subset within your analytics and dive into the data.

  The primary goal of customer segmentation analytics is to gather actionable data points to give you an in-depth understanding of your customers. By gathering data on your different audience segments, you’ll discover insights on your customers that you can use to optimise your website, marketing campaigns, mobile apps, product offerings and overall customer experience.

  Rather than lumping your entire customer base into a single mass, customer segmentation analytics allows you to meet even more specific and relevant needs and pain points of your customers to serve them better.

  By allowing you to “zoom in” on your audience, segmentation analytics helps you offer more value to your customers, giving you a competitive advantage in the marketplace.

  5 types of segmentation

  There are dozens of different ways to split up your customers into segments. The one you choose depends on your goals and marketing efforts. Each type of segmentation offers a different view of your customers so you can better understand their specific needs to reach them more effectively.

  While you can segment your customers in almost endless ways, five common types the majority fall under are :

  

  Geographic

  Another way to segment is by geography.

  This is important because you could have drastically different interests, pain points and desires based on where you live.

  If you’re running a global e-commerce website that sells a variety of clothing products, geographic segmentation can play a crucial role in optimising your website.

  For instance, you may observe that a significant portion of your website visitors are from countries in the Southern Hemisphere, where it’s currently summer. On the other hand, visitors from the Northern Hemisphere are experiencing winter. Utilising this information, you can tailor your marketing strategy and website accordingly to increase sells.

  Where someone comes from can significantly impact how they will respond to your messaging, brand and offer.

  Geographic segmentation typically includes the following subtypes :

  • Cities (i.e., Austin, Paris, Berlin, etc.)
  • State (i.e., Massachusetts)
  • Country (i.e., Thailand)

  Psychographic

  Another key segmentation type of psychographic. This is where you split your customers into different groups based on their lifestyles.

  Psychographic segmentation is a method of dividing your customers based on their habits, attitudes, values and opinions. You can unlock key emotional elements that impact your customers’ purchasing behaviours through this segmentation type.

  Psychographic segmentation typically includes the following subtypes :

  • Values
  • Habits
  • Opinions

  Behavioural

  While psychographic segmentation looks at your customers’ overall lifestyle and habits, behavioural segmentation aims to dive into the specific individual actions they take daily, especially when interacting with your brand or your website.

  Your customers won’t all interact with your brand the same way. They’ll act differently when interacting with your products and services for several reasons. 

  Behavioural segmentation can help reveal certain use cases, like why customers buy a certain product, how often they buy it, where they buy it and how they use it.

  By unpacking these key details about your audience’s behaviour, you can optimise your campaigns and messaging to get the most out of your marketing efforts to reach new and existing customers.

  Behavioural segmentation typically includes the following subtypes :

  • Interactions
  • Interests
  • Desires

  Technographic

  Another common segmentation type is technographic segmentation. As the name suggests, this technologically driven segment seeks to understand how your customers use technology.

  While this is one of the newest segmentation types marketers use, it’s a powerful method to help you understand the types of tech your customers use, how often they use it and the specific ways they use it.

  Technographic segmentation typically includes the following subtypes :

  • Smartphone type
  • Device type : smartphone, desktop, tablet
  • Apps
  • Video games

  Demographic

  The most common approach to segmentation is to split your customers up by demographics. 

  Demographic segmentation typically includes subtypes like language, job title, age or education.

  This can be helpful for tailoring your content, products, and marketing efforts to specific audience segments. One way to capture this information is by using web analytics tools, where language is often available as a data point.

  However, for accurate insights into other demographic segments like job titles, which may not be available (or accurate) in analytics tools, you may need to implement surveys or add fields to forms on your website to gather this specific information directly from your visitors.

  How to build website segmentation analytics

  With Matomo, you can create a variety of segments to divide your website visitors into different groups. Matomo’s Segments allows you to view segmentation analytics on subsets of your audience, like :

  • The device they used while visiting your site
  • What channel they entered your site from
  • What country they are located
  • Whether or not they visited a key page of your website
  • And more

  While it’s important to collect general data on every visitor you have to your website, a key to website growth is understanding each type of visitor you have.

  For example, here’s a screenshot of how you can segment all of your website’s visitors from New Zealand :

  

  The criteria you use to define these segments are based on the data collected within your web analytics platform.

  Here are some popular ways you can create some common themes on Matomo that can be used to create segments :

  Visit based segments

  Create segments in Matomo based on visitors’ patterns. 

  For example :

  • Do returning visitors show different traits than first-time visitors ?
  • Do people who arrive on your blog experience your website differently than those arriving on a landing page ?

  This information can inform your content strategy, user interface design and marketing efforts.

  Demographic segments

  Create segments in Matomo based on people’s demographics. 

  For example :

  • User’s browser language
  • Location

  This can enable you to tailor your approach to specific demographics, improving the performance of your marketing campaigns.

  Technographic segments

  Create segments in Matomo based on people’s technographics. 

  For example :

  • Web browser being used (i.e., Chrome, Safari, Firefox, etc.)
  • Device type (i.e., smartphone, tablet, desktop)

  This can inform how to optimise your website based on users’ technology preferences, enhancing the effectiveness of your website.

  Interaction based segments

  Create segments in Matomo based on interactions. 

  For example :

  • Events (i.e., when someone clicks a specific URL on your website)
  • Goals (i.e., when someone stays on your site for a certain period)

  Insights from this can empower you to fine-tune your content and user experience for increasing conversion rates.

  

  Campaign-based segments

  Create segments in Matomo based on campaigns. 

  For example :

  • Visitors arriving from specific traffic sources
  • Visitors arriving from specific advertising campaigns

  With these insights, you can assess the performance of your marketing efforts, optimise your ad spend and make data-driven decisions to enhance your campaigns for better results.

  Ecommerce segments

  Create segments in Matomo based on ecommerce. 

  For example :

  • Visitors who purchased vs. those who didn’t
  • Visitors who purchased a specific product

  This allows you to refine your website and marketing strategy for increased conversions and revenue.

  Leverage Matomo for your segmentation analytics

  By now, you can see the power of segmentation analytics and how they can be used to understand your customers and website visitors better. By breaking down your audience into groups, you’ll be able to gain insights into those segments to know how to serve them better with improved messaging and relevant products.

  If you’re ready to begin using segmentation analytics on your website, try Matomo. Start your 21-day free trial now — no credit card required.

  Matomo is an ideal choice for marketers looking for an easy-to-use, out-of-the-box web analytics solution that delivers accurate insights while keeping privacy and compliance at the forefront.

• Adventures In NAS

  1er janvier, par Multimedia Mike — General

  In 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 :

  1. 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
  2. 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)
  3. While the SoC supports USB3, that block is tied up for the SATA interface ; the remaining USB port is only capable of USB2 speeds
  4. 32-bit ARM, which prevented me from running certain bits of software I wanted to try (like Minio)
  5. 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 :

  1. 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)
  2. 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
  3. The USB port is still only USB2, so no improvement there
  4. 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
  5. 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.