Business Compass LLC

In today’s data-driven world, real-time insights are critical for businesses looking to stay competitive and make informed decisions. Amazon Kinesis Data Analytics provides a robust solution for processing and analyzing streaming data in real-time, allowing companies to unlock the full potential of their data streams. In this podcast, we’ll explore how Amazon Kinesis Data Analytics works, its key features, and its advantages while also looking at real-world applications across various industries.  #AmazonKinesis #DataAnalytics #RealTimeData #StreamingData #CloudAnalytics #AWS #BigData #MachineLearning #AWSKinesis #StreamingAnalytics https://businesscompassllc.com/unlocking-real-time-insights-with-amazon-kinesis-data-analytics/

In today's data-driven world, real-time insights are critical for businesses looking to stay competitive and make informed decisions. Amazon Kinesis Data Analytics provides a robust solution for processing and analyzing streaming data in real-time, allowing companies to unlock the full potential of their data streams. In this post, we’ll explore how Amazon Kinesis Data Analytics works, its key features, and its advantages while also looking at real-world applications across various industries. Introduction to Amazon Kinesis Data Analytics: Understanding Real-Time Stream Processing Amazon Kinesis Data Analytics is a fully managed service that makes it easier for developers and data engineers to analyze and process streaming data in real-time. Unlike traditional data processing, where insights are generated from historical data, Kinesis Data Analytics enables real-time event processing from sources like IoT devices, social media, or log files. This allows businesses to act on insight...

As web applications grow, scaling database performance becomes crucial. One effective strategy is to implement  read-write splitting , where write operations (such as  INSERT ,  UPDATE , and  DELETE ) are directed to the primary database. At the same time, read operations (such as  SELECT  queries) are routed to replicas. This approach improves the efficiency of database interactions and enables better load balancing and high availability. In this podcast, we will explore how to set up  PostgreSQL read-write splitting  using  Docker  to create a mock Aurora PostgreSQL cluster,  HAProxy  for load balancing, and  SQLAlchemy  in Python to manage read-write query separation. #PostgreSQL #SQLAlchemy #Docker #PythonDevelopment #DatabaseScaling #HAProxy #CloudArchitecture #DevOps https://businesscompassllc.com/scaling-postgresql-with-read-write-splitting-a-docker-sqlalchemy-tutorial/

Introduction: Understanding Read-Write Splitting and Database Architecture for Scalability As web applications grow, scaling database performance becomes crucial. One effective strategy is to implement read-write splitting , where write operations (such as INSERT , UPDATE , and DELETE ) are directed to the primary database. At the same time, read operations (such as SELECT queries) are routed to replicas. This approach improves the efficiency of database interactions and enables better load balancing and high availability. In this guide, we will explore how to set up PostgreSQL read-write splitting using Docker to create a mock Aurora PostgreSQL cluster, HAProxy for load balancing, and SQLAlchemy in Python to manage read-write query separation. Creating a Mock Aurora PostgreSQL Cluster with Docker Compose To simulate an Aurora PostgreSQL cluster locally, we will use Docker Compose to spin up one primary and two replica PostgreSQL instances. Here’s a simple docker-compose.yml to...