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Metric and goal setting is paramount when formulating business operations. That’s why Microsoft Metrics (previously known as “Goals”) make it possible to keep every member of a team striving toward a singular, unified key objective and ensures a higher probability of positive outcomes. By Jocelyn Porter.

Currently, most goal tracking systems require manual updates and are not immediately connected to a business’ data source. This makes it difficult to not only maintain metrics, but to dissect them for further analysis. Thankfully, Metrics in Power BI allow businesses to do just that – to take a look inside the data when further analysis is required.

The article then explains:

• Power BI metrics
• Creating and sharing metrics
• Metric and Details Pane
• Status rules
• Utilizing submetrics
• Data metrics
• Scorecards and workspaces

By fully utilizing Power BI Metrics and Scorecards, you can revolutionize productivity in your work environment. Microsoft Power BI Metrics is a fully customizable and shareable experience in tracking KPI’s that allow businesses to develop a strong platform in aligning business objectives and actionable insights. Good read!

Trust minimization is a valuable security property that blockchain technology is uniquely positioned to generate—replacing handshakes, brand reputation, and paper contracts with guarantees based on computer code, cryptography, and decentralized consensus. These superior guarantees provided by blockchains form the basis of cryptographic truth. By chain.link.

Them main bits of information in this article:

• Blockchains vs. traditional computing
• Three key properties of blockchain scaling
• Scaling the execution layer
• Scaling data storage
• Scaling consensus
• A scalable and secure cross-chain future

Blockchains have succeeded in bringing trust minimization to new use cases including monetary policy (e.g. Bitcoin) and digital asset trading (e.g. DEXs). However, blockchains have historically struggled to maintain trust minimization for use cases that require speeds and costs comparable to traditional computing systems. These scalability limitations can be felt by users in the form of high transaction costs and cause developers to doubt whether blockchains can support high-value use cases that hinge on handling data in real time. Good read!

This post is written from author’s personal experience in Google DevRel for 12 of the last 16 years. By Mano Marks.

Developer relations team is a team involved in establishing a relationship with a group of technical practitioners external to the organization that created it. That relationship is two-way. It is for the purposes of increasing adoption of and productivity using some tool provided by the original organization. The article then describes these topics:

• Step zero: What is Developer Relations
• Developer relations teams at Google
• Job types at Google in Developer Relations
• DREs
• Technical writers
• Program managers
• Manager positions
• Applying for jobs
• Preparing for the interview

Developer Relations is an evolving discipline across a lot of companies. General information about Developer Relations is available in a lot of places. My favorite place to send people is the DevRel Collective where there’s a lot of information about developer relations. There’s also DevRelCon which makes past videos from their conferences available on YouTube. Bottom line, learn as much as you can about the role you’re applying for and use that to prepare for the interview experience. This is super informative!

The terms “blockchain” and “distributed ledger technology” (DLT) may be widely used but are not always well understood. This article explains the differences between them. Rather than covering the technical aspects in depth, we will focus on how and why these technologies have the potential to disrupt existing business models and perhaps even create entirely new ones. By Bankless Publishing.

Blockchain first started to gain serious recognition after “Satoshi Nakamoto” authored the Bitcoin White Paper in 2008. The article then explains the following:

• Blockchain
• What is Distributed Ledger Technology (DLT)?
• All blockchains are DLTs but not all DLTs are blockchains.
• DLT networks remove the need for the middleman
• Open blockchains take the potential one step further

Unfortunately, blockchain networks are still in their infancy and are not yet capable of delivering a comparable service to the millions (or billions) of people currently using the various platforms owned by big tech. Today’s blockchains face difficulties with scaling, especially while trying to maintain a high degree of decentralisation. Nice one!

Back in June 2014, the top five most popular databases were exactly the same as June 2022: Oracle, MySQL, Microsoft SQL Server, PostgreSQL and MongoDB. The difference is their relative popularity: PostgreSQL and MongoDB have been gaining in popularity relative to the relational incumbents. By Matt Asay.

In his opening keynote, MongoDB CEO Dev Ittycheria shared statistics that showed MongoDB has become mainstream data infrastructure for over 35,000 customers, from Fortune 500 companies to garage-dwelling startups alike.

Two main trends are clear from the article:

• The rise and rise of general purpose databases
• Databases become data platforms

In 2014, MongoDB helped to spark an industry trend toward specialization; in 2022, it’s part of a movement away from specialization. The irony is that MongoDB has never touted specialization, and has instead marketed itself as a general-purpose database from the start. Why? Because, as O’Grady explained, “general purpose” makes developers’ lives easier, and MongoDB has always focused on developer convenience.

MongoDB has always handled data relations just fine; it has handled them differently than a relational database. So, back then, the company accepted the NoSQL label, despite its problems (who wants to be defined by what they’re not?), because it helped developers think beyond tabular data structures. Since that time, there has been an explosion in non-relational, or multi-model, databases. Today, DB-Engines includes nearly 400 databases, but less than half of them are relational databases. From document to time series to graph to columnar to key-value to [insert new database type here], the industry has kept using relational databases even as it has found a home for a wide variety of new databases. Good read!

There are some universal truths about open source software (OSS). It has revolutionized our world and become the foundation of our digital society, the backbone of our digital economy, and the basis of our digital existence. By @linuxfoundation.

Open source software has played a significant part in everything from the internet and mobile apps we use every day to operating systems and programming languages used to construct the future. Even the systems we traditionally think of as being closed, such as Microsoft Windows and Apple’s Mac and iPhone, are developed using open source software.

But only a few decades ago, few people had even heard of open source software, and it was limited to a small group of enthusiastic devotees. Yet the concept of free and open source software (FOSS) has been around a long time, going back to the early days of the user communities for IBM mainframes and academic institutions. FOSS is software that anyone can use, study, modify, and distribute without restriction. The term “open source” was coined to describe this type of software, and the concept was formalized with the launch of the Open Source Initiative (OSI) in 1998.

Many new industries and thousands of businesses have joined the open source revolution. Those organizations that chose a deliberate OSS strategy, incorporating best practices, methods, and engineering processes, emerged as leaders in their industries or verticals for open source initiatives.

This research is a collection of learnings and best practices that Dr. Haddad has developed, collaborating with the LF AI & Data community members who have pursued their own open source journeys for years. The link to the 31 page long research pdf is also in the article. Nice one!

Computer vision is a rapidly growing field in research and applications. Advances in computer vision research are now more directly and immediately applicable to the commercial world. By Richmond Alake.

AI developers are implementing computer vision solutions that identify and classify objects and even react to them in real time. Image classification, face detection, pose estimation, and optical flow are some of the typical tasks. Computer vision engineers are a subset of deep learning (DL) or machine learning (ML) engineers that program computer vision algorithms to accomplish these tasks.

The article further describes solutions related to:

• Cloud computing
• AutoML
• Machine learning libraries and frameworks
• Mobile devices

Computer vision technology continues to increase as AI becomes more integrated in our daily lives. Computer vision is also becoming more and more common in the latest news headlines. As this technology scales, the demand for specialists with knowledge in computer vision systems will also rise due to trends in cloud computing service, Auto ML pipelines, transformers, mobile-focused DL libraries, and computer vision mobile applications. Good read!

AIOps is a term introduced by Gartner in 2016, referring to AI for IT Operations. It combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination. By ODSC Community.

As more companies adopt digital transformation initiatives, we are seeing an explosion in digital systems and their associated data exhaust. By rationalizing digital signals in an intelligent way, AIOps plays a crucial role in helping companies reduce operation costs, improve engineering efficiency, and enhance customer experience. As a result, AIOps adoption has grown exponentially along with the uptick of digital transformation. Ponemon Institute© Research Report estimated that the cost-saving from AIOps systems is $17K/Outage min.

Further in the article we have:

• How to get started with Azure Metrics Advisor
• Data preparation: map your AIOps problem to the dataset
• Data onboarding
• Tuning the detection
• Root cause analysis

In addition to the detected anomalies, Azure Metrics Advisor also offers insights into what might have been the cause of the issue to help you further troubleshoot. Stakeholders can get detection results via communication channels set up by the user, along with the anomaly detected and root cause analysis. You will also find reference architecture attached. Good read!

You manage access in AWS by creating policies and attaching them to AWS Identity and Access Management (IAM) principals (roles, users, or groups of users) or AWS resources. AWS evaluates these policies when an IAM principal makes a request, such as uploading an object to an Amazon Simple Storage Service (Amazon S3) bucket. Permissions in the policies determine whether the request is allowed or denied. By Matt Luttrell and Josh Joy.

In this blog post, we will walk you through a scenario and explain when you should use which policy type, and who should own and manage the policy. You will learn when to use the more common policy types: identity-based policies, resource-based policies, permissions boundaries, and AWS Organizations service control policies (SCPs).

The article explains the following:

• Different policy types and when to use them
  • Service control policies overview
  • Permissions boundaries overview
  • Identity-based policies overview
• Resource-based policies overview
  • How to implement different policy types
  • Service control policies
  • Permissions boundary policies
  • Identity-based policies
  • Resource-based policies
• Putting it all together

In this blog post, you learned about four different policy types: identity-based policies, resource-based policies, service control policies (SCPs), and permissions boundary policies. You saw examples of situations where each policy type is commonly applied. Then, you walked through a real-life example that describes an implementation that uses these policy types. Excellent read!

With lots of frameworks to choose from, and all those strong opinions, it’s easy to feel a little lost. Comparing frameworks based on the features they list can be a headache, and features are only part of the picture. It would be awesome if you had a clear checklist you could evaluate Node.js frameworks against. By Simon Plenderleith.

The article is split into the following sections:

• What’s your use case?
• Framework styles
• Support for promises and async / await
• Documentation
• Practical examples
• Community and ecosystem
• Project health

Context is everything. Try and get clear on your requirements before you start looking at frameworks and their features. This will help you you figure out if a framework is designed to support your intended use case. Good read!