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The Quick and Easy Way to Analyze Numpy Arrays

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The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par

Kafka Flowchart Useful for Dummies

Architecture for dummies


How Kafka Works


Here're the prime points on Kafka stream-processing. In Mainframe, the data you receive/process in two methods (Batch and online). In Kafka, it receives data and sends it to consumers. Here're the details with Architecture, Logs, and applications that use Kafka.

The streaming data is different (YouTube Live). When the data comes into Queue the data will then be processed. In the batch process, you need to wait till you get the Batch completes. In the case of stream processing, it is on the fly.

1. Architecture



Kafka Architecture

2. Process

  • 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.

Kafka Logs

  • It writes the incoming messages to logs.
  • It appends to the logs. it will not delete the existing records. 

Zookeeper 

  • 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.

3. Stream Data Structure

Stream Data Key Points:

  • 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.
How the stream data comes into the Kafka server you can see here.

4. The Kafka Based Applications

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.


In 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.

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