😩 Have you ever experienced data inconsistency between systems? This can occur when you encounter the dual-write problem, where one write fails and the other is successful. 🔎 For instance, when an object’s state needs to be updated in a relational database (RDBMS) while the corresponding event has to be written to #ApacheKafka. Data inconsistencies can often occur since these two systems are not connected and lack transactional logic. 📌 Follow along in this #Microservices 101 video with Wade Waldron as he discusses the causes of the dual-write problem and shares tips on how to resolve it.
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🤯 The dual-write problem arises when writing to two distinct systems, such as a database and #ApacheKafka, where one write fails and the other is successful. Learn more about how the transactional outbox pattern can help solve the challenge.
The Transactional Outbox Pattern
confluent.smh.re
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🤯 The dual-write problem arises when writing to two distinct systems, such as a database and #ApacheKafka, where one write fails and the other is successful. Learn more about how the transactional outbox pattern can help solve the challenge.
The Transactional Outbox Pattern
confluent.smh.re
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🤯 The dual-write problem arises when writing to two distinct systems, such as a database and #ApacheKafka, where one write fails and the other is successful. Learn more about how the transactional outbox pattern can help solve the challenge.
The Transactional Outbox Pattern
confluent.smh.re
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From Theory to Practice: Developing a Distributed Key-Value Database with Sharding and Replication
From Theory to Practice: Developing a Distributed Key-Value Database with Sharding and Replication
dev.to
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ORM makes database communication simple and efficient. Combining that with the repository pattern, brings us that required decoupling. Different frameworks can be used to implement them. #noNameChats
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🚨WEBINAR TOMORROW🚨 Join PingCAP Solution Architect Kerry Li for an in-depth exploration of #TiDB, a cutting-edge, open-source distributed SQL database. 🎓 Key Takeaways: 🔹 Scalability & Performance: Discover TiDB's horizontal scalability and automatic sharding for large-scale data management with minimal latency. 🔹 Fault Tolerance & High Availability: Learn about TiDB’s raft-based consensus protocol ensuring data integrity across clusters. 🔹 Real-time Analytics: See how TiDB merges OLTP & OLAP for real-time analytics on live data. 🔹 Ease of Use: Understand TiDB’s MySQL compatibility for seamless integration into your existing infrastructure. 📍 Register today! #DataManagement #Scalability #RealTimeAnalytics
Webinar | Key Components of a Distributed High Availability Database
social.pingcap.com
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Capturing filtered data from group of tables using GoldenGate
Capturing filtered data from group of tables using GoldenGate
http://abpaas.wordpress.com
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Swipe Right for Seamless Integration: Navigate the Benefits of Object-Relational Mapping (ORM). Dive into this carousel to discover how ORM streamlines database management, enhances scalability, and boosts development productivity. #orm #objectrelationalmapping #database #brigita
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Database replication with read-only secondary databases, it's an interesting approach to increasing scalability and performance for large applications at database layer it's possible to do master-master replication as well to increase the insert/update as well but it needs a extra attention to data conflict. And it's possible to partition verticaly or horizontally my databases... look, I've got a new vision today. --- Database with vertical partition + horizontal partition +DB replicas + cache... it's possible to do a good thing here.
Globally Distributed Databases with Read Replicas
https://www.youtube.com/
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Implementing Change Data Capture (CDC) with .NET, Debezium, and RabbitMQ! In this insightful video by Milan Jovanović, the concept of Change Data Capture (CDC) is explained in detail, showing how to track database changes in real-time or near real-time. 📊 Debezium is used to monitor database operations (insert, update, delete) on PostgreSQL, capturing these changes and streaming them into a message queue system like RabbitMQ. From there, MassTransit is utilized in a .NET application to react to these events efficiently. Milan walks through how to set up a complete system and demonstrates practical use cases for CDC.
Building Change Data Capture (CDC) in .NET with Debezium + RabbitMQ
https://www.youtube.com/
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