MoSCoW prioritization stands as a seminal framework for categorizing the importance and urgency of tasks and features in various project management and development settings. This paper delves into the origins, conceptual framework, and applications of the MoSCoW method. Furthermore, a critical analysis is undertaken to explore the strengths, limitations, and challenges inherent to this methodology.
Continue readingThe RACI (Responsible, Accountable, Consulted, Informed) matrix, a framework for defining roles and responsibilities in organizational contexts, has been widely adopted across diverse industries. This paper offers an in-depth exploration of the conceptual origins of RACI, its application across various organizational paradigms, and its impact on project management and organizational culture. It also critically examines the limitations and challenges inherent to its implementation, while suggesting possible extensions and improvements to make it more effective in modern organizational ecosystems.
Continue readingBasic guide to using Amazon Kinesis Video Streams.
Continue readingHere’s a basic guide to help you calculate the number of shards needed for an Amazon Kinesis stream.
Step 1: Understand Shards Shards are the fundamental units of throughput in a Kinesis stream. Each shard can support a certain amount of data read and write throughput. To determine the number of shards needed, you’ll need to consider your data volume and your desired throughput.
Step 2: Estimate Data Volume
Step 3: Calculate Shards
Write Capacity = Estimated Data Volume (MB/s) / 1 MB/s per ShardRead Capacity = Estimated Data Volume (MB/s) / 2 MB/s per ShardNumber of Shards = Max(Write Capacity, Read Capacity)Step 4: Adjust for Scalability and Redundancy Keep in mind that the number of shards you initially calculate should provide enough capacity for current and future needs. Additionally, consider adding some extra shards to handle unexpected spikes in traffic and to ensure redundancy in case of shard failures.
Step 5: Consider Kinesis Data Streams Limits Be aware of AWS limits for the maximum number of shards you can have in a single stream. As of my last update in September 2021, the limit is 500 shards per stream.
Step 6: Monitor and Scale Regularly monitor your stream’s performance using AWS CloudWatch metrics. If you notice that you’re hitting shard limits or experiencing latency issues, you might need to adjust the number of shards by scaling up or down.
Tips:
Remember that shard calculations can be complex and may vary based on factors like data size, distribution, and your specific use case. Be prepared to iterate and adjust the number of shards as your application evolves and your understanding of its needs deepens.
Configuring the video producer to connect to your Amazon Kinesis Video Stream involves a few steps that ensure secure and reliable data transmission. Here’s a basic guide:
Configure the Video Producer with Stream Name and Credentials
In summary, configuring the video producer involves setting up the necessary credentials (access keys or IAM roles), specifying the stream name as the target destination, and utilizing AWS SDKs to handle the complexities of data encoding, packaging, and secure transmission. Properly configuring your video producer ensures that your video data is securely and efficiently transmitted to your Amazon Kinesis Video Stream for processing and analysis.
Introduction
The Server Side Public License (SSPL) was introduced by MongoDB, Inc. in 2018 as a way to address concerns about cloud providers profiting from open-source projects without contributing back to them. The SSPL has generated controversy and faced several criticisms:
Conclusion
It’s worth noting that MongoDB, Inc. introduced the SSPL to address what they saw as a significant issue: major cloud providers monetizing open-source software without giving back to the community or the original developers. However, the SSPL’s approach to solving this problem has led to debate within the tech community about the best ways to balance open source principles with sustainable business models.
For simpler tasks, working individually may prove more effective. Consider trade-offs between collaboration and efficiency.
A recent study questions teamwork’s efficiency, revealing social biases and “herding” effects impacting collective intelligence. “Social loafing” and limited learning opportunities in groups can hinder performance.
It calls to mind Fred Brooks’ work vis-a-vis “Mythical Man Month”, one of the cornerstone texts in IT/System delivery: “Brooks discusses several causes of scheduling failures. The most enduring is his discussion of Brooks’s law: Adding manpower to a late software project makes it later.”
Brooks identifies:
“Group intercommunication formula: n(n − 1)/2.
Example: 50 developers give 50 × (50 – 1)/2 = 1,225 channels of communication.”
Equally, teams burn out. Often one individual at a time. This can be bolstered by swapping in new people but eventually, the team burns out and needs to be refreshed too. I suspect for many people burnout is exasperated by the volume of communication required, especially where they are neurodivergent.
When allocating tasks consider assigning them to the team or individual contributors. And when you do remember to consider making sure they are “Mission Based” objectives.
Link to study: https://theconversation.com/teamwork-is-not-always-the-best-way-of-working-new-study-211693
Link to book: https://en.wikipedia.org/wiki/The_Mythical_Man-Month
Thank you to Professor Amanda Kirby for sharing the research and study on LinkedIn.
Here’s a concise overview of my articles on neurodiversity, Asperger’s Syndrome, Autism, and Autism Spectrum Disorder (ASD) so far.
Continue readingThe term “high-functioning autism” has been criticized by many individuals within the autism community, as well as by advocates and experts, for a variety of reasons. While it is not inherently offensive to everyone, there are several concerns associated with its usage that highlight potential issues.
Continue readingThe task of finding an alternative name for what was formerly referred to as Asperger’s Syndrome is undoubtedly a complex and challenging endeavour. This challenge stems from the intricacies of capturing the essence of a unique cognitive profile within the broader autism spectrum while avoiding any unintended negative connotations or exclusionary subtext.
Continue readingWhile the historical association of Hans Asperger with the Nazi regime has cast a shadow over his name, it is worth considering the case for retaining the term “Asperger’s Syndrome” and allowing the past to become a part of history. By recognizing the valuable contributions made by Asperger’s and acknowledging the unique experiences of individuals previously diagnosed with Asperger’s Syndrome, we can strike a balance between honoring the legacy and promoting inclusivity.
Continue readingThe decision to move away from using the term “Asperger’s Syndrome” has raised concerns regarding the lack of meaningful consultation with the very community it represents. Furthermore, there are suggestions that this shift has been imposed upon the community by external forces, potentially fueled by underlying tensions within the broader Neurodiversity community. In the spirit of inclusivity and empowerment, it is essential to prioritize the voices and perspectives of autistic individuals when making decisions that directly impact their identities and well-being.
Continue readingAcross three decades of diving deep into the intricacies of IT, from software engineering to enterprise architecture, there’s a multitude of lessons I’ve unearthed. However, two recent experiences bring to light some invaluable insights about software delivery and the broader strokes of business ethos.
Consider a scenario where your project hinges on the delivery timeline of an external firm. A firm that’s been given two years to code a mere API HTTP PUT request, with the deadline stretching from mid to end September. The stakes? The very funding of your project. Enter the vendor’s sales representative: brimming with confidence, he assures an on-time delivery. Yet, when playfully challenged with a wager to affirm this confidence, he declines. A simple bet revealing the chasm between rhetoric and conviction.
Such instances resonate with an enduring truth in software delivery and business: actions always echo louder than words. The real “test” in business is not just about meeting estimates or deadlines, but about the conviction, commitment, and authenticity behind the promises.
However, alongside this test of commitment, lies another challenge I’ve grappled with regardless of the cap I wore: estimating software delivery. Despite my extensive track record, I’ve faced moments when estimates missed the mark. And I’m not alone in this.
Early in my career, Bill Vass, another IT leader, imparted a nugget of wisdom that remains etched in my memory. He quipped, “When it comes to developer estimates, always times them by four.” This wasn’t mere cynicism, but a recognition of the myriad unpredictabilities inherent in software development, reminiscent of the broader unpredictabilities in business.
Yet, the essence isn’t about perfecting estimates. It revolves around three pillars: honesty in setting and communicating expectations; realism in distinguishing optimism from capability; and engagement to ensure ongoing dialogue through the project’s ups and downs.
In the grand tapestry of IT and business, it’s not always the flawless execution of an estimate or a delivered promise that counts. At the end of the day, an estimate is just that; an estimate. The crux lies in how we navigate the journey, armed with authenticity, grounded expectations, and unwavering engagement. These cornerstones, combined with real-world lessons, are what construct the foundation of trust, catalyse collaborations, and steer us toward true success.
Below is a table that compares popular open-source licenses.
| License | Permissiveness | Copyleft | Patent Grant | Complexity | Attribution | Derivative & Redistribution Licensing | Examples |
|---|---|---|---|---|---|---|---|
| MIT License | Very high | None | No | Low | Required | Any license, No Requirement | jQuery, .NET Core |
| GNU GPL | Low | Strong | No | High | Required | GPL Only | Linux kernel, WordPress |
| GNU LGPL | Moderate | Weak | No | Moderate | Required | LGPL or More Permissive, Required LGPL | GTK |
| Apache License 2.0 | Very high | None | Yes | Moderate | Required with changes | Any license, No Requirement | Apache HTTPD, Kafka |
| BSD Licenses | Very high | None | No | Low | Varies by clause | Any license, No Requirement | FreeBSD, NetBSD |
| MPL 2.0 | Moderate | File-level | No | Moderate | Required | MPL or More Permissive, Required MPL | Firefox |
| Creative Commons | Varies | Varies | No | Low to Moderate | Varies by license type | Varies | Artwork, music, blogs |
| Eclipse Public License | Moderate | Moderate | No | Moderate | Required | Any license, No Requirement | Eclipse IDE |
Notes:
The difference between “license” and “licence” is primarily regional:
In essence, in American English, “license” serves as both the noun and verb form. In contrast, British English differentiates between the two: “licence” is the noun, and “license” is the verb. However, it’s crucial to remember the context and audience when writing, as using the appropriate form can enhance clarity and adherence to regional language standards.
Open source licenses allow developers to share their code with the public while also dictating how that code can be used, modified, and distributed. Different licenses cater to various philosophies and use cases. Here’s a comparison of some popular open source licenses:
Conclusion: Choosing a license depends on the developer’s goals. If they want maximum freedom and adaptability, MIT or BSD are excellent choices. If ensuring that derivative works remain open source is crucial, GPL might be the way to go. For something in between, MPL or LGPL could be ideal. As always, when considering licensing, it might be wise to consult with someone knowledgeable about intellectual property laws.
Amazon Rekognition Video offers real-time video analysis for streaming videos, and it’s tailored to handle video streams seamlessly. Using Rekognition Video, you can identify and recognize faces in real-time, detect unsafe content, and track people, among other features.
Here’s how Amazon Rekognition works with video streams:
Rekognition Video is integrated with Amazon Kinesis Video Streams, which captures, processes, and stores video streams for analytics and machine learning. Here’s a basic flow:
Track persons even when they are partially hidden from view in your video, such as when they go behind an object. This powerful feature offers:
Identify potentially unsafe or inappropriate content in your video streams. This feature’s capabilities include:
Here’s a high-level approach to setting up real-time video analysis:
In conclusion, Amazon Rekognition Video provides a powerful platform for real-time video analysis when paired with Kinesis Video Streams. It enables applications in security, monitoring, user engagement, content moderation, and more. Always refer to the official AWS documentation for the most up-to-date and detailed information.
Amazon Rekognition is a service that allows developers to add image and video analysis to their applications. With it, you can detect objects, scenes, faces, identify celebrities, and even spot potentially unsafe content. This guide will walk you through the basics of the platform and its API.
Amazon Rekognition provides a web-based console where you can try out many of its features without writing any code. Before diving deep into the API:
There are several functions provided by the Rekognition API to analyze images:
import boto3 client = boto3.client('rekognition') response = client.detect_labels( Image={ 'S3Object': { 'Bucket': 'your-bucket-name', 'Name': 'your-image-name.jpg' } }, MaxLabels=10 ) print(response['Labels'])Rekognition’s video analysis functions similarly to its image functions, but due to the asynchronous nature of video analysis, you’ll often start an analysis task and get results later.
Some key video functions include:
With Amazon Rekognition Custom Labels, you can train your own machine learning model to detect specific items in images tailored to your needs.
You can create collections of faces, which lets you search for faces in an image that match those in your collection. Useful functions include:
Amazon Rekognition offers a powerful set of tools for developers interested in image and video analysis. Like any AWS service, the key is understanding how its different features fit together and mapping them to your specific needs. With hands-on experimentation and real-world applications, you’ll become proficient in no time. Happy coding!
Always prioritize content that provides value to your audience for optimal engagement.
Tips above are taken from combining the soft and hard recommendations from the following articles: