You have to build a system which should be consistent in nature. For example, if you are getting product feeds either through flat file or any event stream you have to make sure you don't lose any events related to product specially inventory and price. If we talk about price and availability it should always be consistent because there might be possibility that product is sold or seller doesn't want to sell it anymore or any other reason. However, attributes like Name, description doesn't make that much noise if not updated on time. John wants to build an e-commerce portal like Amazon, Flipkart or Paytm.
Kafka is a messaging system used for big data streaming and processing. In this tutorial, we discuss the basics of getting started with Kafka. We'll discuss the architecture behind Kafka and demonstrate how to get started publishing and consuming basic messages. Kafka is a messaging system. It safely moves data from system A to system B. Kafka runs on a shared cluster of servers making it a highly available and fault-tolerant platform for data streaming.
The day when armies of business analysts can query incoming data in real time may be drawing closer. Supporting such continuous interactive queries is a goal of KSQL, software put forward this week by the Kafka data-streaming software originators at Confluent Inc. KSQL is a SQL engine that directly handles Apache Kafka data streams. She also said KSQL is intended to broaden the use of Kafka beyond Java and Python, opening up Kafka programming to developers familiar with SQL; although, the form of SQL Confluent is using here is a dialect, one the company has developed to deal with the unique architecture of Kafka streaming. The software is appearing first as a developer preview, and it will be available under an Apache 2.0 license, according to the company. Created at LinkedIn, Kafka began life as a publish-and-subscribe messaging system that focused on handling log files as system events.
This Slack Team will focus on the Apache Kafka technology and its ecosystem, also allowing its members to interact and share their use cases, do and don't, how to's, etc. We will also hear about the Confluent Platform and topics like Kafka's Connect API and streaming data pipelines, Kafka's Streams API and stream processing, Security, Microservices and anything else related to Apache Kafka.
In a previous blog post, we introduced exactly once semantics for Apache Kafka . That post covered the various message delivery semantics, introduced the idempotent producer, transactions, and the exactly once processing semantics for Kafka Streams. We will now pick up from where we left off and dive deeper into transactions in Apache Kafka. The goal of the document is to familiarize the reader with the main concepts needed to use the transaction API in Apache Kafka effectively.