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Best Machine Learning Book for Beginners

You need a mixof different technologies for Data Science projects. Instead of learning many skills, just learn a few. The four main steps of any project are extracting the data, model development, artificial intelligence, and presentation. Attending interviews with many skills is not so easy. So keep the skills short.
A person with many skills can't perform all the work. You had better learn a few skills like Python, MATLAB, Tableau, and RDBMS. So that you can get a job quickly in the data-science project.
Out of Data Science skills, Machine learning is a new concept. Why because you can learn Python, like any other language. Tableau also the same. Here is the area that needs your 60% effort is Machine learning.  Machine Learning best book to start.

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Kafka Stream Processing Quick Flow Chart for Dummies

The difference between Stream processing Vs Batch processing and role of Kafka I can tell you using simple example.  I have used Mainframe and Youtube for this.


In Mainframe, you remember the data processing majority of it is based on Batch processing. That means the whole collected data processed by some scheduled jobs. 



The streaming data on the other hand is different (Youtube Live). When the data comes into Queue the data will be processed. So, organizations no need to wait until the batch is completed. The organizations will get results Quickly.


Stream Data Flow Before You Move on to Kafka


Batch Vs Stream Data Processing Role of Kafka

*Here are the key points to remember: 
If you need to report on or take action immediately as data arrives, stream processing is a good approach.


*If you want to Carry detailed analysis
If you need to perform in-depth analysis or are compiling a large repository of data for later analysis, a stream-processing approach may not be a good fit.


The Real-time Uses of Stream Data


  • Credit card fraud—A credit card owner may not notice a card has been stolen, but by reviewing purchases as they happen against established patterns (location, general spending habits), you may be able to detect a stolen credit card and alert the owner.

  • Intrusion detectionAnalyzing application log files after a breach has occurred may be helpful to prevent future attacks or to improve security, but the ability to monitor aberrant behavior in real-time is critical.

  • A large race, such as the New York City Marathon—Almost all runners will have a chip on their shoe, and when runners pass sensors along the course, you can use that information to track the runners’ positions. By using the sensor data, you can determine the leaders, spot potential cheating, and detect whether a runner is potentially having problems.

  • The financial industryThe ability to track market prices and direction in real-time is essential for brokers and consumers to make effective decisions about when to sell or buy.


Kafka Architecture: Subscribe and Publishing Broker



Kafka Architecture

How Kafka Process Stream Data

  • Kafka is a publish/subscribe system, but it would be more precise to say that Kafka acts as a message broker. A broker is an intermediary that brings together two parties that don’t necessarily know each other for a mutually beneficial exchange or deal.

  • Kafka stores messages in topics and retrieves messages from topics. There’s no direct connection between the producers and the consumers of the messages. Additionally, Kafka doesn’t keep any state regarding the producers or consumers. It acts solely as a message clearinghouse.

  • The underlying technology of a Kafka topic is a log, which is a file that Kafka appends incoming records to. To help manage a load of messages coming into a topic, Kafka uses partitions. 

  • The use of partitions is to bring data located on different machines together on the same server. We’ll discuss partitions in detail shortly.

How Kafka Writes Data in the Logs

  1. Kafka Writes data in the logs
  2. Only it appends to the first entered record. It will not delete records 

Role of ZooKeeper in Kafka

  • ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. 
  • All of these kinds of services are used in some form or another by distributed applications.

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