AWS App Runner and Google Cloud Run are two serverless computing platforms that can help you deploy and run containerized applications without having to worry about servers. Both platforms are relatively new, but they have quickly become popular choices for developers. What are the similarities? Both platforms are serverless, meaning that you don’t have to provision or manage servers. The platforms will automatically scale your application up or down based on demand, so you only pay for the resources that you use.

Azure networking and GCP networking are both comprehensive cloud networking services that offer a wide range of features and capabilities. However, there are some key differences between the two platforms. Azure networking offers a more traditional networking model, with a focus on virtual networks (VNets), subnets, and network security groups (NSGs). VNets are isolated networks that can be used to group together resources, such as virtual machines (VMs), storage, and applications.

There are a few ways to monitor costs in Azure. One way is to use the Azure Cost Management + Billing portal. This portal provides a graphical interface that you can use to view your costs over time, track your spending against budgets, and identify areas where you can save money. Another way to monitor costs is to use the Azure Cost Management API. This API allows you to programmatically access your cost data and integrate it with other systems.

Here is a Terraform file that you can use to create a Kubernetes cluster in Azure: provider "azurerm" { version = "~> 3.70.0" subscription_id = var.azure_subscription_id client_id = var.azure_client_id client_secret = var.azure_client_secret tenant_id = var.azure_tenant_id } resource "azurerm_resource_group" "aks_cluster" { name = var.resource_group_name location = var.location } resource "azurerm_kubernetes_cluster" "aks_cluster" { name = var.aks_cluster_name location = azurerm_resource_group.aks_cluster.location resource_group_name = azurerm_resource_group.aks_cluster.name node_count = 3 vm_size = "Standard_D2s_v3" network_profile { kubernetes_network_interface_id = azurerm_network_interface.

Here are some tips on how to create an effective SRE culture: Start with the right mindset. SRE is a mindset that sees reliability as everyone’s responsibility, not just the responsibility of the SRE team. It is important to create a culture where everyone is empowered to take ownership of reliability and to make decisions that will improve the reliability of the systems they work on. Embrace failure. Failure is inevitable, so it is important to create a culture where failure is seen as an opportunity to learn and improve.

This one is better explained with the presentation below. If you want to learn how to run quantitative analytics at scale, it’s well worth a watch.

Built and maintained a client and market side booking service, off order book trade reporting engine and trade manager/repository Like most banks, this one suffered from the not invented here syndrome. They had decided to pretty much reimplement the core libraries to optimize for Zero Garbage Collection, low latency and high throughput. Unfortunately they were not optimized for large development teams and maintainability. I helped the team debug issues and introduce new functionality using a tech stack that consisted of Java, Groovy, Spring, FIX, JUnit, MSSQL and JRebel.

I developed an eTrading platform routing client FIX flow to the firm’s Algorithmic Trading platform. The platform was used by traders send orders to the market based on a particular strategy (e.g VWAP). VWAP stands for Volume-Weighted Average Price. It is a technical analysis indicator that is used to measure the average price of a security over a given period of time, taking into account the volume of trades. The VWAP is calculated by adding up the dollar value of all trades for a security and then dividing by the total volume of trades.

Developed an eTrading platform routing client FIX flow to the firm’s Algorithmic Trading platform. Used profiling/debugging tools to resolve critical issues around lost trade messages. Java, YourKit, Swing, Spring, Tibco EMS, FIX

Design and development of a real time matching engine to provide insights into liquidity for funding forecasts and regulatory requirements. Java, BDD/TDD, JUnit/TestNG, Maven, Coherence, Spring Core, Spring Data, Spring Boot Micro Services, IBM MQ, Maven, Jira, TeamCity