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Startups worldwide turn to Google Cloud tools to build fast on a strong and easy to use platform that helps them get to market and launch products faster, all while building on the cleanest cloud in the industry. Startups leverage Google Cloud and our Google for Startups Cloud Program to go from idea to IPO, and there are a variety of products on Google Cloud that can help them. By Fillipo Madella.

Here are the 8 top products that startups use on Google Cloud to innovate and grow:

• Firebase for app development
• Cloud SQL for database needs
• AI and machine learning products
• BigQuery for data analytics
• Google Kubernetes Engine (GKE) for containers
• Looker for data visualization

… for the last 2 follow the link to the full article. Interesting mix of products!

It seems like it’s getting harder to hire for security roles, just as attacks grow more sophisticated. Here’s some advice to ease recruiting — and nurture security talent in-house. By Jennifer Riggins.

The International Information System Security Certification Consortium’s (ISC²) annual cybersecurity workforce study found a worldwide gap of 3.4 million cybersecurity workers — and that’s after this workforce grew just over 11% from 2021 to 2022, adding 464,000 jobs last year alone.

This isn’t just a risk of burnout among the current security staff, but a risk to the whole organization. The same study found that a significant percentage of the 11,779 practitioners and decision makers it surveyed reported that the following things that they have experienced might have been mitigated if they had enough cybersecurity staff.

Some of topics covered in the article:

• Broad job descriptions scare off candidates
• Sell the purpose in cybersecurity roles
• Biggest barriers to filling security jobs
• How to improve hiring processes
• Upskilling for security skills in-house
• Security hiring amplifies tech’s diversity woes

Cybersecurity hiring processes are notoriously gatekeeping, even for the tech industry. Every person interviewed for this piece cited a person’s network as the most common way to find a cybersecurity job — and building that network often favors people who have the time and money to attend conferences. Interesting read!

Technically, what you’re going to see in this chapter is more precisely known in the broader programming community as “forwarding” rather than “delegation”. However, in the Kotlin world, it’s always referred to as delegation, so we’ll continue to use that term here. By typealias.com.

The tutorial then dives into following topics:

• Delegation in restaurants and code
• Manual delegation
• Easy delegation, the Kotlin way
• Multiple delegates
• Overriding a delegated call
• Managing conflicts
• Delegation for general and specific types

In Kotlin, sometimes an object is fully capable of fulfilling a request (such as a function call) on its own, and in other cases, it might need to hand off the request to another object. Nice tutorial!

App startup time is a critical metric for users, as it’s their first interaction with the app, and even minor improvements can have significant benefits for the user experience. By Filip Busic.

In this article, we will explore three separate optimizations that reduced the time it took to launch our iOS consumer app by 60%. We identified these opportunities using proprietary performance tools, but Xcode instruments or DTrace could also be suitable alternatives.

Further in the article:

• Changing String(describing:) to ObjectIdentifier()
• Stop converting unnecessary objects to AnyHashable
• Auditing 3rd party framework initializers

Accurately identifying performance bottlenecks and opportunities is often the most challenging part of any optimization. Notoriously, a common mistake is to measure A, optimize B, and conclude C. That is where good performance tools help highlight bottlenecks and surface them. Good read!

A Service Level Agreement (SLA) isn’t just a list of services, metrics, and penalties. It is a crucial part of your Managed Services Agreement (MSA). MSP SLAs help determines expectations and how to measure them, so you can keep clients satisfied and eliminate potential disputes. By technative.io.

This is very detailed article covering amongst others:

• What is an SLA?
• Why do you need an SLA?
• SLA overview: How to write a Service Level Agreement?
• Service level agreement best practices
• Most common SLA mistakes

An SLA might not be the most exciting part of MSP work, but it is essential for building solid client relations. In this article, you will find tips on how to draft a thorough and clear SLA agreement. Producing a robust Service Level Agreement (SLA) begins with a practical approach that can protect both customers and the MSP. A well-written service-level agreement will help you build a strong relationship with your clients that will last for a long time. Nice one!

AWS recently announced a distributed map for Step Functions, a solution for large-scale parallel data processing. Optimized for S3, the new feature of the AWS orchestration service targets interactive and highly parallel serverless data processing workflows. By Renato Losio.

The new distributed map state allows writing Step Functions to coordinate large-scale workloads, iterating over millions of objects on S3, for example, logs, images, or CSV files. While AWS previously supported Step Function’s map state to execute the same processing steps for multiple entries in a dataset, it was limited to 40 parallel iterations.

The distributed map stops reading after 100 million items and supports JSON or CSV files of up to 10GB. Rafal Wilinski, founder of Dynobase, shares a CDK-based PoC of a migrations framework taking advantage of the new feature and comments: Step Functions Distributed Maps are awesome. Combined with DynamoDB Parallel scans, they enable blazingly fast, whole-table data migrations and transformations. Interesting read!

YAML is a well-known language for generating JSON files, but as we have been generating Docker Compose for integration tests, we’ve found Jsonnet to be better. Before we discuss both languages, keep in mind that this piece is strictly to discuss the use of each language when generating JSON. By Colin Mo.

The article walks over:

• YAML pros
• YAML cons
• Jsonnet pros
• Jsonnet cons
• Examples

Jsonnet is a data serialization format and a programming language. As a superset of JSON, Jsonnet can parse any valid JSON file and add extra functionality to it. Jsonnet is specifically designed to easily work with JSON data, especially in large and complex projects, by providing features such as variables, conditionals, and loops.

On of disadvantage of YAML is its lack of support for certain types of data, such as dates and times. While YAML can handle numbers and strings, it does not have built-in support for more complex data types, which can make it more difficult to work with certain types of data. Interesting read!

Deno Fresh WASM is pretty easy to set up, just by adding a single dependency to your project. This lets you write code in Rust, compile it to WASM and then use that generated module in your Deno project. By Rodney Lab.

The article then covers:

• Setting up the project for Rust WASM
• Rust code
• Deno Fresh WASM: Rust source
• Compiling the module
• Using the module

In the post we have had a whistle-stop tour of setting up your first Deno WASM project. In particular, we saw how to use wasmbuild to quickly add a Rust WASM module to your Deno project, how to add console logs in your WASM Rust code, some basic Rust image manipulation. Excellent read with code in GitHub repo provided!

Over the course of the last few months, we at Memgraph have been working on something that we believe could be helpful with classical graph prediction tasks. With our latest newborn query module, you will have the option of performing both label classification and link prediction. By Antonio Filipovic.

The following is explained:

• Graph neural networks
• Temporal graph networks
• Amazon data example
• Exploring an Amazon data network in Memgraph

You probably already know that a graph consists of nodes (vertices) and edges (relationships). Every node can have its feature vector, which essentially describes that node with a vector of numbers. We can look at this feature vector as the representation vector of each node, also called embedding of the node. To avoid getting lost in technical details, graph neural networks work as a message passing system, where each node aggregates feature representations of its 1-hop neighbors.

Plenty of examples and code is provided as well in the article. Good read!

While more and more teams are adopting Kubernetes as their standard container orchestration technology, cost insight is lacking. Teams often don’t know how much they’re spending, where in their organization they are spending, or what is driving their infrastructure cost increases. OpenCost helps alleviate this problem by bringing real-time cost monitoring to Kubernetes workloads with a solution that encompasses both an open specification and an open source project. By Mark Poko, JuanJo Ciarlante.

Further in the article:

• How Grafana Labs uses OpenCost
  • Getting more accurate insight into vCPU and memory cost
  • Monitoring costs of a multi-cluster architecture
• How Grafana Labs will contribute to the OpenCost OSS project
  • Observability
  • Ease of use
  • Performance

To get started with OpenCost and cost observability, you can find the OpenCost code in GitHub and check out the OpenCost documentation. Nice one!