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Operational Use case Patterns for Apache Kafka and Flink — Part 1
Operational Use case Patterns for Apache Kafka and Flink — Part 1
This is the first post of the series that shows building operational use cases with Apache Kafka and Apache Flink.
Dunith Dhanushka
Jan 3
EdU Technology Watch
Build Serverless Streaming Architectures with Upstash Kafka
Build Serverless Streaming Architectures with Upstash Kafka
How Upstash Kafka is making event streaming serverless while being compatible with Kafka APIs?
EdU Technology Watch
Feb 21, 2022
Trending
Understanding Kafka Topic Partitions
Understanding Kafka Topic Partitions
Everything in Kafka is modeled around partitions. They rule Kafka’s storage, scalability, replication, and message movement.
Dunith Dhanushka
Mar 28, 2021
A Gentle Introduction to Event-driven Change Data Capture
A Gentle Introduction to Event-driven Change Data Capture
How to detect, capture, and propagate changes in source databases to target systems in real-time, event-driven manner
Dunith Dhanushka
Feb 6, 2021
A Visual Introduction to Debezium
A Visual Introduction to Debezium
A story-based introduction to understanding what Debezium is, how it is made of, and how it works in a real-world scenario
Dunith Dhanushka
May 29, 2021
Querying Microservices with the CQRS and Materialized View Pattern
Querying Microservices with the CQRS and Materialized View Pattern
How to speed up and scale-out inter-service queries using a dedicated materialized view database that caches queries.
Dunith Dhanushka
Jun 27, 2021
Event-driven APIs — Understanding the Principles
Event-driven APIs — Understanding the Principles
What are event-driven APIs? How do they differ from REST APIs? What technology choices are there to build them?
Dunith Dhanushka
Apr 25, 2021
Real-time Analytics
Comparing Stateful Stream Processing and Streaming Databases
Comparing Stateful Stream Processing and Streaming Databases
How these two technologies work? how do they differ, and when is the right time to use them?
Dunith Dhanushka
Sep 29, 2022
Building CQRS Views with Debezium, Kafka, Materialize, and Apache Pinot — Part 1
Building CQRS Views with Debezium, Kafka, Materialize, and Apache Pinot — Part 1
How to build an incrementally updated materialized view that serves queries in a faster and scalable manner?
Dunith Dhanushka
Aug 10, 2022
Building CQRS Views with Debezium, Kafka, Materialize, and Apache Pinot — Part 2
Building CQRS Views with Debezium, Kafka, Materialize, and Apache Pinot — Part 2
How to build an incrementally updated materialized view that serves queries in a faster and scalable manner?
Dunith Dhanushka
Aug 10, 2022
CDC-based Upserts with Debezium, Apache Kafka, and Apache Pinot
CDC-based Upserts with Debezium, Apache Kafka, and Apache Pinot
How to build a streaming data pipeline to capture MySQL database changes and stream them to Apache Pinot via Debezium and Kafka
Dunith Dhanushka
Jul 26, 2022
Completeness, Speed, and Cost — Three Knobs Controlling Your Data Analytics Strategy
Completeness, Speed, and Cost — Three Knobs Controlling Your Data Analytics Strategy
How do speed, accuracy, and cost-effectiveness influence the choice between batch and streaming analytics?
Dunith Dhanushka
Jun 27, 2022
Unbundling the Modern Streaming Stack
Unbundling the Modern Streaming Stack
Why modern streaming stack is replacing the classic streaming architecture? What’s the composition and what values it brings?
Dunith Dhanushka
Apr 19, 2022
Real-Time Streaming for Mortals: How Redpanda and Materialize Making It a Reality
Real-Time Streaming for Mortals: How Redpanda and Materialize Making It a Reality
How these two non-JVM based products make stream processing accessible for the masses by reducing the operational overhead
Dunith Dhanushka
Oct 10, 2021
Rise of the Streaming Databases — Episode 2 : Apache Pinot
Rise of the Streaming Databases — Episode 2 : Apache Pinot
How Pinot solves the toughest problems in the data analytics today with its low-latency, high throughput query capabilities
Dunith Dhanushka
Sep 24, 2021
Understanding Materialized Views — 3 : Stream-Table Joins with CDC
Understanding Materialized Views — 3 : Stream-Table Joins with CDC
Join a stream with a lookup table to enrich the content and produce a materialized view. Then use Debezium to synchronise the lookup table…
Dunith Dhanushka
Sep 20, 2021
Understanding Materialized Views — Part 2
Understanding Materialized Views — Part 2
Leveraging stateful stream processing to maintain materialized views that are incrementally updated
Dunith Dhanushka
Sep 10, 2021
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