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I’ve worked as a software engineer, manager, consultant, and business owner. All of these jobs have involved meetings. What those meetings look like has varied greatly. As an engineer, meetings typically entailed technical conversations with peers, one-on-ones with managers, and planning meetings or demos with stakeholders. As a manager, these looked more like quarterly goal-setting with engineering leadership, one-on-ones with direct reports, and decision-making discussions with the team. As a consultant, my day often consists of talking to clients to provide input and guidance, communicating with partners to develop leads and strategize on accounts, and meeting with sales prospects to land new deals. ...

Part of what we do at Real Kinetic is give companies confidence to ship software in the cloud. Many of our clients are large organizations that have been around for a long time but who don’t always have much experience when it comes to cloud. Others are startups and mid-sized companies who may have some experience, but might just want another set of eyes or are looking to mature some of their practices. Whatever the case, one of the things we frequently talk to our clients about is the value of both serverless and managed services. We have found that these are critical to getting big wins with small teams on tight deadlines in the cloud. Serverless in particular has been key to helping clients get some big wins in ways others didn’t think possible. ...

AWS Glue is a managed service for building ETL (Extract-Transform-Load) jobs. It’s a useful tool for implementing analytics pipelines in AWS without having to manage server infrastructure. Jobs are implemented using Apache Spark and, with the help of Development Endpoints, can be built using Jupyter notebooks. This makes it reasonably easy to write ETL processes in an interactive, iterative fashion. Once finished, the Jupyter notebook is converted into a Python script, uploaded to S3, and then run as a Glue job. ...

ETL (Extract-Transform-Load) processes are an essential component of any data analytics program. This typically involves loading data from disparate sources, transforming or enriching it, and storing the curated data in a data warehouse for consumption by different users or systems. An example of this would be taking customer data from operational databases, joining it with data from Salesforce and Google Analytics, and writing it to an OLAP database or BI engine. ...

Ingress on Google Kubernetes Engine (GKE) uses a Google Cloud Load Balancer (GCLB). GCLB provides a single anycast IP that fronts all of your backend compute instances along with a lot of other rich features. In order to create a GCLB that uses HTTPS, an SSL certificate needs to be associated with the ingress resource. This certificate can either be self-managed or Google-managed. The benefit of using a Google-managed certificate is that they are provisioned, renewed, and managed for your domain names by Google. These managed certificates can also be configured directly with GKE, meaning we can configure our certificates the same way we declaratively configure our other Kubernetes resources such as deployments, services, and ingresses. ...