August 2021 Summaries

The knapsack problem is a classic optimization dilemma that can be applied to various use cases, including packing luggage for a holiday. PostgreSQL and recursive Common Table Expressions (CTEs) can help solve this problem by finding the combination of items with maximum value within a defined weight constraint. In this tutorial, we demonstrate how to create an inventory table in PostgreSQL and use recursive CTEs to find all combinations of items that fit into a luggage with a 20 capacity. The solution provides a theoretical exercise for solving the knapsack problem and can be adapted to real-world constraints.

In a recent webinar, Rajesh Ajmera from Traveloka and Nitish Jha from Gojek discussed the adoption of cloud and open-source technologies in the APAC region. They highlighted how businesses can harness these technologies to scale smartly and meet unique business demands. The discussion also touched upon challenges faced by APAC companies, such as choice paralysis due to too many options, high cloud costs, and the need for custom solutions from cloud providers. To prepare for digital transformation, businesses should assess their readiness, train their people, choose a hybrid solution, develop a plan for scaling, and keep track of costs. Managed services are recommended over DIY as they ease the pain of migrating to the cloud. Cloud providers must adapt to a new role as strategic partners, offering top-of-the-line security, great cost-efficiency, and confidence in wielding cloud tech for business growth.

Event-driven architecture (EDA) is an alternative approach to building microservice applications, offering a decoupled system of producers and consumers that process events asynchronously through an intermediary called a broker. This contrasts with the traditional request-response pattern used in computing and web applications, where a client makes a synchronous HTTP request and waits for a response from the server. EDA's benefits include high decoupling between producers and consumers, asynchronous interactions, and fault tolerance due to independent functioning of services. Key components of EDA include events, event records, producers, consumers, streams, message brokers, event schemas, and various patterns such as notification about state change, replication of state, and event collaboration. Apache Kafka is a popular open-source message broker for implementing EDA due to its scalability, reliability, and fault tolerance.

This tutorial demonstrates how to use M3 to monitor systems and Grafana to visualize the collected data. It involves setting up a PostgreSQL service, collecting metrics from it using Aiven for M3, and then using Grafana to inspect the collected data. The integrations are in place, allowing users to see both the data coming in and the dashboard integration from the M3 service overview page. This setup can be replicated with other Aiven services or non-Aiven services by sending data to the created M3 database and visualizing it with Grafana.

In a recent webinar, experts Alex Timlin and Jan Hegewald discussed the challenges facing retailers and how open source cloud technology can support their digital transformation efforts. They highlighted four key trends that are here to stay in the retail industry: increased reliance on mobile devices, video interactions with brands, demand for sustainable products, and personalized shopping experiences leveraging data. The biggest challenges currently faced by retailers include hesitations surrounding AI technology implementation and unpredictability of demand. Cloud and open-source technologies can help retailers scale into the future by offering scalable data centers, flexible operation costs, safe test environments, expanded services, better productivity, and more innovation. To prepare for digital transformation through cloud and open-source technologies, businesses should adopt a mindset/philosophy/culture shift, align business goals with technology functionalities, prepare their architecture, develop a clear migration strategy, and align all stakeholders.

This blog post demonstrates how to use Apache Superset to create data visualizations from a PostgreSQL table in an Aiven environment, which can be utilized to drive business decisions. The process involves running Apache Superset on Docker, pushing the dataset to PostgreSQL, connecting Apache Superset to PostgreSQL, and visualizing the data using various charts and maps. Additionally, calculated fields can be added to the dataset for further analysis.