Monolithic vs Microservices Architecture

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Monolithic vs Microservices Architecture

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Is Machine Learning still a buzz word or can we easily put it to use to gain actionable insights from our data? In this demo-heavy, hands-on session, Ram will explain how to find hidden patterns in the data, find outliers and predict results using real-world datasets. By the end of this session, you will be familiar with Machine Learning concepts (regression, classification, over-fitting, cross-validation etc.) and you should be able to build, deploy and consume Machine Learning models with ease. About the Presenter Based in Brisbane, Ram Katepally is a Microsoft Certified Solutions Expert and Data Analytics consultant at WARDY IT Solutions. As a consultant, Ram has significant experience working with companies of all sizes across Australia, empowering them to make data-driven business decisions. He’s passionate about Machine Learning, Internet of Things, Office 365 and Power BI. In his free time, he is an avid player of chess.

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Vertex AI is a managed ML platform for practitioners to accelerate experiments and deploy AI models. Enhanced developer experience - Build with the groundbreaking ML tools that power Google - Approachable from the non-ML developer perspective (AutoML, managed models, training) - Ease the life of a data scientist/ML (has feature store, managed datasets, endpoints, notebooks) - Infrastructure management overhead have been almost completely eliminated - Unified UI for the entire ML workflow - End-to-end integration for data and AI with build pipelines that outperform and solve complex ML tasks - Explainable AI and TensorBoard to visualize and track ML experiments

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• Understand the issues with commercial database pricing and licensing. • Learn about the benefits of Amazon Aurora for improving performance and decreasing costs. • See how AWS Database Migration Service helps with your migration. • See how AWS Schema Conversion Tool makes conversions simple and quick. If you’re looking to improve application performance and availability and decrease database costs, it’s time to replace your expensive Oracle databases with an open-source compatible solution. Amazon Aurora is a MySQL-compatible relational database that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. You'll learn how to use the AWS Database Migration Service to migrate your data with minimal downtime, and how the AWS Schema Conversion Tool converts your Oracle schemas and procedural code into Amazon Aurora. We’ll follow with a quick demo of the entire process.

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Kevin Huang: AWS San Francisco Startup Day, 9/7/17 Architecture: When, how, and if to adopt microservices - Microservices are not for everyone! If you're a small shop, a monolith provides a great amount of value and reduces the complexities involved. However as your company grows, this monolith becomes more difficult to maintain. We’ll look at how microservices allow you to easily deploy and debug atomic pieces of infrastructure which allows for increased velocity in reliable, tested, and consistent deploys. We’ll look into key metrics you can use to identify the right time to begin the transition from monolith to microservices.

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This Edureka "What is Hadoop" Tutorial (check our hadoop blog series here: https://goo.gl/lQKjL8) will help you understand all the basics of Hadoop. Learn about the differences in traditional and hadoop way of storing and processing data in detail. Below are the topics covered in this tutorial: 1) Traditional Way of Processing - SEARS 2) Big Data Growth Drivers 3) Problem Associated with Big Data 4) Hadoop: Solution to Big Data Problem 5) What is Hadoop? 6) HDFS 7) MapReduce 8) Hadoop Ecosystem 9) Demo: Hadoop Case Study - Orbitz Subscribe to our channel to get updates. Check our complete Hadoop playlist here: https://goo.gl/4OyoTW

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ADR, or Architecture Decision Record, is a valuable tool in software development for several reasons. It provides a centralized location for documenting and tracking architectural decisions, aiding both current and future team members. ADRs enhance communication among team members by documenting the rationale behind architectural decisions, especially beneficial during onboarding of new team members or when revisiting decisions. They serve as a knowledge base, enabling teams to learn from past decisions and refine their decision-making process. Additionally, ADRs contribute to transparency by helping stakeholders understand the reasons behind specific architectural choices. As with any other tool or process, introducing them into an organization can face several obstacles, and overcoming these challenges is crucial for successful implementation. In this talk I go through some common problems and our way of solving them.

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Data spaces in distributed environments should be allowed to evolve in agile ways providing data space owners with large flexibility about which data they store. Agility and heterogeneity, however, jeopardize data exchanges because representations may build on varying ontologies and data consumers may not rely on the semantic correctness of their queries in the context of semantically heterogeneous, evolving data spaces. Graph data spaces are one example of a powerful model for representing and querying data whose semantics may change over time. To assert and enforce conditions on individual graph data spaces, shape languages (e.g SHACL) have been developed. We investigate the question of how querying and programming can be guarded by reasoning over SHACL constraints in a distributed setting and we sketch a picture of how a future landscape based on semantically heterogeneous data spaces might look like.

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Monolithic vs Microservices Architecture

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