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As software developers, we are constantly faced with the need to improve and optimize our code. Whether it’s for performance, readability, or maintainability, refactoring code is an essential skill. By Tomek Skupiński.

There are a number of different techniques that can be used when refactoring code. In this article, we will explore some of the best practices for refactoring code. The blog post then focuses on:

• Identify the problem areas
• Make a plan
• Keep your changes small
• Write tests
• Refactor incrementally
• Use a refactoring tool
• Document your changes
• Use a source control system
• Perform regression testing
• Be prepared to undo changes

Refactoring code is an essential skill for every software developer. By following the best practices outlined in this article, you can make sure that you won’t get lost in the process. Nice one!

Docker is an increasingly popular choice for businesses dealing with containerized applications. However, as with any new technology, Docker introduces complexities that need to be managed. Some of these complexities relate to infrastructure and application monitoring. Due to the abstraction offered by containers, traditional monitoring solutions might not be suitable for Docker-based workloads. By Cameron Pavey

The article provides information on the following:

• Why monitor Docker
• Prerequisites
• Monitoring Docker with InfluxDB and Telegraf

As a time series database, InfluxDB is perfectly positioned to store and visualize the kind of metrics that application monitoring often deals with, as there are usually lots of data points at regular intervals. With large volumes of data like this, you must have a mechanism to visualize, search, and understand the data to derive insights. InfluxDB fits the bill in this regard thanks to its easy-to-configure visualizations and the powerful Flux data scripting language that allows you to query and analyze your data. Good read!

There are several benchmarking tools in the market but most of them require esoteric coding knowledge. NoSQLBench is simple to use while providing sophisticated benchmarking for Cassandra and other NoSQL databases. It provides results within minutes. By Jones-Gilardi.

In this post, you’ll get hands-on experience with benchmarking and stress testing Cassandra using NoSQLBench. Rather than going in-depth, our tutorial will scratch the surface and cover:

• Understanding parameters and key metrics for benchmarking
• How cycles, bindings and statements work together
• Experimenting with stdout
• Scaling up a test and customizing your own scenarios
• Packaging a performance test with named scenarios

NoSQLBench is an open-source, pluggable testing tool for the NoSQL ecosystem. It’s primarily designed to test Cassandra, but you can also use it for other NoSQL technology like Apache Kafka, MongoDB, and DataStax Astra DB. Good read!

OpenTelemetry is an open-source observability framework for infrastructure instrumentation hosted by the Cloud Native Computing Foundation (CNCF). The project gained a lot of momentum with contributions from all major cloud providers (AWS, Google, Microsoft) as well as observability vendors (including Timescale) to the point it became the second-highest ranked CNCF project by activity and contributors, only coming second to Kubernetes itself. By James Blackwood-Sewell.

Source: https://www.cncf.io/blog/2022/06/08/a-deep-dive-into-opentelemetry-metrics/ OpenTelemetry aims to define a single standard across all types of observability data (which it refers to as signals), including metrics, logs, and traces. Through a collection of tools, libraries, APIs, SDKs, and exporters, OpenTelemetry radically simplifies the process of collecting signals from your services and sending them to the backend of your choice, opening the doors of observability to a wider range of users and vendors.

• OpenTelemetry metrics
• Measurements to metrics
• Instruments and emitting measurements
• Views and aggregations

A View in OpenTelemetry defines an aggregation, which takes a series of measurements and expresses them as a single metric value at that point in time. As more measurements are created, the metric is continuously updated. If there is no View created for an Instrument, then a default aggregation is selected based on the Instrument type. Custom views can be targeted by Meter name, Instrument name, Instrument type, or with a wildcard. Nice one!

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!