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POD: Two Practices [WORK]

But Kubernetes is complex, and not all data engineers are familiar with how to set up and maintain Kubernetes. Following Spark best practices requires advanced configuration of both Kubernetes and Spark applications.

POD: Two Practices

In a previous blog post, we reviewed how to deploy a Spark job on Amazon EKS using a Kubernetes Job. Here is another blog post; in it, you can find performance optimizations and considerations. In this blog post, we will go through the different best practices related to Amazon EKS scheduling and provide an end-to-end Spark application example that implements them. We will cover different ways to configure Kubernetes parameters in Spark workloads to achieve resource isolation with dedicated nodes, flexible single Availability Zone deployments, auto scaling, high speed and scalable volumes for temporary data, Amazon EC2 Spot usage for cost optimization, fine-grained permissions with AWS Identity and Access Management (IAM), and AWS Fargate integration.

Now that we have covered the best practices for running Spark on Amazon EKS, we will go through an end-to-end example that demonstrates these best practices. In this example, we will process the New York taxi public data set and analyze the most profitable pickup locations for drivers so they can search for customers around those locations. We will launch a Spark job that will read the CSV files for the Amazon S3 public bucket, process the data into Spark, and write two versions of the data: the raw records cleaned and parsed into Parquet format and the aggregated records analyzing profitability per geolocation, also in Parquet format.

In this blog post, we have seen how to configure Apache Spark and Amazon EKS to support common requirements, including resources isolation, cost reduction, dynamic scaling, performance optimization, and fine-grained access control. We also have seen that configuring these best practices requires customization and maintenance effort. AWS also provides a managed product with Amazon EMR on EKS that supports all these features. Additionally, it removes the maintenance effort on the docker image and provides additional features including optimized Spark runtime for performance, automatic logging with Amazon CloudWatch, debugging with a serverless Spark History Server, Amazon S3 integration with EMRFS optimized connector, AWS Glue Data Catalog integration for synchronizing catalog tables, and Apache Airflow Operator for data pipeline.

With labels, Kubernetes provides powerful capabilities to achieve infrastructure visibility, perform efficient operations, and respond quickly to issues. Organizations and their DevOps teams can leverage these labeling features and realize tremendous benefits by following best practices. Think about what labels you might add or which tools you would use to query such labeled resources to gain these advantages.

SAFe teams use Agile practices of choice based primarily on Scrum, Kanban, and Extreme Programming (XP) to improve their performance. To ensure they are solving the right problem, teams apply Design Thinking. Teams apply Built-In Quality practices to drive disciplined content creation and quality. Collective ownership, pair work, standards, test-first, and Continuous Integration help keep things Lean by embedding quality and operating efficiency directly into the process.

Kubernetes clusters require a balance of resources in both pods and nodes to maintain high availability and scalability. This article outlines some best practices to help you avoid common disruption problems.

Advanced pod scheduling in Kubernetes allows for the implementation of many interesting use cases and best practices for deploying complex applications and microservices on Kubernetes. With pod affinity, you can implement pod colocation and data locality for tightly coupled application stacks and microservices.

The existing, outdated layout of the Second Floor at the 1250 Building lacked the efficiency and flexibility required to meet the current and future growth needs of its practices. The layout was subject to negative patient experience due to the disjointed separation of practices and difficulty in wayfinding.

This article will guide you through the best practices to deploy and distribute the workload on a multi-cloud Kubernetes environment on Scaleway's Kosmos. It follows the first part of the Hands-On prepared for

While Kubernetes best practices dictate that you should always set resource limits and requests on your workloads, it is not always easy to know what values to use for each application. As a result, some teams never set requests or limits at all, while others set them too high during initial testing and then never course correct. The key to ensuring scaling actions work properly is to dial in your resource limits and requests on each workload so that workloads run efficiently.

The open source project, Goldilocks, by Fairwinds helps teams allocate resources to their Kubernetes deployments and get those resource calibrations just right. Goldilocks is a Kubernetes controller that collects data about running pods and provides recommendations on how to set resource requests and limits. It can help organizations understand resource use, resource costs, and best practices around efficiency. Goldilocks employs the Kubernetes Vertical Pod Autoscaler (VPA). It takes into account the historical memory and CPU usage of your workloads, along with the current resource usage of your pods, in order to recommend how to set your resource requests and limits. (While the VPA can actually set limits for you, it is often best to use the VPA engine only to provide recommendations.) Essentially, the tool creates a VPA for each deployment in a namespace and then queries that VPA for information.

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Most educational development for inclusive excellence does not draw directly on the experiences and perspectives of students. This article presents two different approaches to positioning undergraduate students as critical partners in developing inclusive pedagogical practices. Co-authored by the directors of and student partners who participated in each approach, the article defines inclusive excellence and inclusive teaching and provides selected examples of partnership work that strives for equity and inclusion. It then describes our different approaches, discusses potential benefits of launching student-faculty partnership work through these approaches, and offers recommendations for developing pedagogical partnership efforts for inclusive excellence at other institutions.

While the approaches at Lafayette College and Bryn Mawr and Haverford Colleges are different in numerous ways, they have in common how a relatively small number of student fellows or student consultants can have an impact on a large number of faculty and students. Anna and Nicole noted that their partnership work for more inclusive practices forged new connections among students and faculty, provided important personal and professional experiences for them, and were empowering both for them and for other students.

Tracie Addy, PhD, MPhil, is the Associate Dean of Teaching & Learning at Lafayette College in Pennsylvania. As the director of the Center for the Integration of Teaching, Learning, and Scholarship, she is responsible for working with instructors across all divisions and ranks to develop and administer programming related to the teacher-scholar model, from classroom teaching to the scholarship of teaching and learning. Her scholarship focuses on learner-centered practices, including active learning and inclusive teaching.

Nicole Litvitskiy is a recent graduate of Haverford College, where she studied Psychology and Education and worked as a student consultant for the SaLT program. She currently works as a research assistant at the Medical University of South Carolina, supporting research on telehealth services for children who have experienced a traumatic event. Nicole aims to pursue a graduate degree in school-clinical psychology, focusing on the role of inclusive, accessible practices in supporting student mental health.

CHARLES ROBINSON: All these joint practices, it was funny because when I went to visit a team on the camp tour, I was talking to the GM, and they were about to head off to some joint practices. And I kind of made the joke, I was like, you know, man, I'm going to miss it. Every single time I'm ever in joint practices, there's crazy fights. Like every join practice--

CHARLES ROBINSON: --I've ever been to, there's always been fights that break out, and they get pretty heated, not just the typical I'm pulling your face mask, pushing. Like you've seen guys on the ground, punches being thrown, all this stuff. And we've seen it multiple times in joint practices this off season. This is a cut above what normally happens.

But after this, I would have to think that at some point the league, and it might be the league meetings coming up here in the fall, they make it a point to send a memo or something saying, hey, look, if we're going to do these joint practices or maybe it's next off season, this needs to be a point of emphasis that you can't allow things like this to escalate to the level of an Aaron Donald and maybe even alert teams that if someone's hitting guys with helmets in the joint practice, we're going to step in and start suspending those players because I-- 350c69d7ab


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