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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Build a Scalable Data Pipeline with Apache Kafka

Guvvala Sujitha (B19EE033)
Guvvala Sujitha (B19EE033) Last Updated : 10 Mar, 2023
9 min read

Introduction

Apache Kafka is a framework for dealing with many real-time data streams in a way that is spread out. It was made on LinkedIn and shared with the public in 2011. Kafka is based on the idea of a distributed commit log, which stores and manages streams of information that can still work even if something goes wrong. At its heart, Kafka is a messaging system that lets producers send records to topics and lets consumers read information from issues. In a broker cluster, records are kept in parts that are spread out among the servers. Each partition is copied, so there is a backup if something goes wrong.

Kafka is great for building scalable data pipelines because it has many important features that make it a good choice:

  1. Kafka is designed to work with much real-time data with little delay. This makes it great for real-time analytics, combining logs, and processing data.
  2. Horizontal scalability means that Kafka can grow horizontally to handle more data and traffic if you add more brokers to the cluster.
  3. Kafka works even if something goes wrong because it copies data and has an automatic failover. This keeps data from getting lost if a node fails or the network goes down.
  4. There are many ways to process data with Kafka, such as batch processing, stream processing, and complex event processing. It can be used to process data with tools like Apache Spark, Flink, and Storm.
  5. Kafka has become popular as a platform for building scalable data pipelines in many fields, like banking, e-commerce, social media, and others. It is scalable and flexible, so it can handle large amounts of real-time data reliably and effectively.
data pipeline

Source: docs.confluent.io

Learning Objectives:

  1. Learn Apache Kafka’s significant features and functions in developing data pipelines
  2. Learn how to set up and configure a Kafka cluster for maximum speed and scalability.
  3. Learn the many approaches for creating and receiving data from Kafka and the trade-offs associated with each.
  4. Discover how to grow a Kafka cluster to accommodate high throughput and significant amounts of data.
  5. Learn how to use Kafka with other data technologies like Hadoop, Spark, and Elasticsearch.
  6. Discover best practices for designing scalable and dependable Kafka data pipelines, including fault tolerance, data formats, monitoring, and optimization.
  7. Build an example data pipeline highlighting essential ideas and best practices to gain hands-on experience with Kafka.

This article was published as a part of the Data Science Blogathon.

Table of Contents

Creating a Kafka Cluster

To set up a Kafka cluster, you must first install Kafka on a group of servers. You will also need to configure the Kafka brokers and build Kafka topics to arrange your data.

The following are the steps for establishing a Kafka cluster:

  1. Install Kafka on Each Node: Download the Kafka binary package and place it in a directory on each cluster node. Ensure that all nodes are running the same version of Kafka.
  2. Setup Kafka Brokers: A Kafka broker will be installed on each node in the cluster. To set up the broker settings, edit the server.properties file on each node, including the broker ID, hostname, and port number. You’ll also need to install ZooKeeper to coordinate the cluster’s brokers.
  3. Begin Kafka Brokers: The bin/kafka-server-start.sh script launches the Kafka brokers on each node. Ascertain that all brokers can interact with one another and with ZooKeeper.
  4. Make a Kafka Topic: Using the bin/Kafka-topics.sh script to generate Kafka topics. Cases are used to organize data in Kafka and comprise one or more partitions spread among the cluster’s brokers. The number of divisions for each subject can be chosen based on the projected volume of data.

After your Kafka cluster is up and running, you can begin creating and consuming data to and from Kafka using Kafka producers and consumers. You may also use Kafka tools and metrics to monitor the performance and health of your Kafka cluster.

Producing Data to Kafka

You must install an Apache Kafka producer on your workstation to send data to Kafka. The following are the steps for configuring a Kafka producer in Java or Python:

  1. Install the Kafka Client Libraries: Download and install the Kafka client libraries for your preferred programming language (Java or Python).
  2. Set up the Kafka Producer: Configure the Kafka producer in your producer code using the broker list, topic name, and any other needed properties. The host names and port numbers of the Kafka brokers in your cluster should be included in the broker list.

For example, in Java:

Properties props = new Properties();
props.put("bootstrap.servers", "broker1:9092,broker2:9092");
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

Producer<String, String> producer = new KafkaProducer<>(props);
  • Deliver Data to Kafka: To submit data to Kafka, use the Kafka producer API. You may define the topic name, message key, and value.

For example, in Java:

String topic = "my-topic";
String key = "key1";
String value = "value1";

ProducerRecord<String, String> record = new ProducerRecord<>(topic, key, value);

producer.send(record);
producer.close();
  • When you have completed creating data, shut down the Kafka producer to free up resources.
  • When you’ve configured your Kafka producer and delivered data to Kafka, you can use Kafka tools and metrics to monitor the performance and health of your Kafka cluster.
Apache Kafka

Source: dev.to

Using Apache Kafka to Consume Data

Before using Apache Kafka data on your workstation, you must install a Kafka consumer. The steps for making a Kafka consumer in Java or Python are as follows:

  1. Install the Client Libraries for Kafka: Download the Kafka client libraries for your favorite programming language and install them (Java or Python).
  2. Prepare the Kafka Reader: Set up the Kafka consumer in your client code by using the broker list, the topic name, and any other attributes you need. You must also include the consumer group ID, identifying users who share a workload.

For example, in Java:

Properties props = new Properties();
props.put("bootstrap.servers", "broker1:9092,broker2:9092");
props.put("group.id", "my-group");
props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");

Consumer<String, String> consumer = new KafkaConsumer<>(props);
  • Sign up for a Kafka Newsletter: Use the Kafka consumer API to sign up for a Kafka subject. You can also choose to only read from certain partitions instead of reading from all by default.

For example, in Java:

String topic = "my-topic";
consumer.subscribe(Collections.singletonList(topic));
  • Consume Data from Kafka: You can use the Kafka consumer API to get data from Kafka. You can process the key and value of each Kafka record as needed by looping through the records returned by the consumer.

For example, in Java:

while (true) {
    ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
    for (ConsumerRecord<String, String> record : records) {
        String key = record.key();
        String value = record.value();
        // process the record
    }
}
  • Don’t let Anyone Read Kafka: Don’t forget to turn off the Kafka consumer when you’re done getting data from it. This will free up resources.
  • After you’ve set up your Kafka consumer and used data from Kafka, you can use Kafka tools and metrics to monitor how well your Kafka cluster is running and how healthy it is.

Kafka Cluster Scaling

To expand a Kafka cluster, you add or remove Kafka brokers as the data pipeline’s demands change. The following are the stages of scaling a Kafka cluster:

  1. Among these are the following new Kafka brokers: You will need to set up extra computers or instances and install the Kafka broker software to add more Kafka brokers to your cluster. Using configuration management technologies such as Ansible or Puppet to automate this procedure.
  2. Here’s how to get the Kafka cluster up and running: As you add more Kafka brokers, you must modify the Kafka cluster’s configuration to accommodate the new brokers. Change the broker-id and listeners properties in the Kafka configuration file for each broker to do this. the health and performance of your Kafka cluster.

For example, in the server.properties file:

broker.id=3
listeners=PLAINTEXT://new-broker:9092
  • Update the Kafka Topics. If you use Kafka topics with a replication factor greater than one, the new brokers will follow some of the old topics’ partitions. Use the Kafka-topics command-line tool to see if this is true.

For example, to check the replication factor for a topic:

$ kafka-topics --describe --topic my-topic --bootstrap-server broker1:9092
Topic: my-topic	PartitionCount: 3	ReplicationFactor: 3	Configs:
	Topic: my-topic	Partition: 0	Leader: 1	Replicas: 1,2,3	Isr: 1,2,3
	Topic: my-topic	Partition: 1	Leader: 2	Replicas: 2,3,1	Isr: 2,3,1
	Topic: my-topic	Partition: 2	Leader: 3	Replicas: 3,1,2	Isr: 3,1,2
  • Rebalance the Kafka Partitions: Once you’ve added more brokers and changed the topics, you’ll need to rebalance the Kafka partitions to spread the load evenly among the brokers. Use the command-line tool Kafka-reassign-partitions to create and run a new partition assignment plan.
  • Monitor the Kafka Cluster. Once you’ve scaled your Kafka cluster, you should use Kafka tools and metrics to monitor its performance and health. You can use tools like Kafka Manager, Kafka Monitor, or the Confluent Control Center to monitor the status of your Kafka brokers, topics, and partitions and be notified of any problems or oddities.
Apache Kafka

Source: developer.confluent.io

Integrating Apache Kafka with Other Data Technologies

Kafka is built to work with a wide range of data technologies, making it a versatile and adaptable component of any data pipeline. These are some examples of standard data integrations:

  1. Apache Spark: Apache Spark is a well-known data processing framework that may be used to process Kafka data. The Spark Streaming API may receive data from Kafka and perform real-time processing and analysis.
  2. Apache Storm: Apache Storm is another real-time data processing framework that Kafka may utilize. The Storm-Kafka connection allows you to read data from Kafka and process it in real time.
  3. Apache Flink is a distributed stream processing framework that may be used to process Kafka data. The Flink-Kafka connection may be used to read data from Kafka and process it in real time.
  4. Elasticsearch is a popular search and analytics engine that may be used to store and index Kafka data. To stream data from Kafka to Elasticsearch, utilize the Kafka Connect Elasticsearch Sink connection.
  5. Hadoop: Hadoop is a popular distributed processing platform for processing and analyzing massive datasets. The Kafka Connect HDFS Sink connector may transmit data from Kafka to Hadoop HDFS for storage and processing.
  6. NoSQL databases, like MongoDB and Cassandra, may be used to store and analyze Kafka data. To stream data from Kafka to these databases, utilize the Kafka Connect MongoDB Sink and Cassandra Sink connectors.
  7. Cloud Services: As an alternative to Kafka, cloud services such as Amazon Kinesis, Google Cloud Pub/Sub, and Azure Event Hubs can be utilized. These services offer similar real-time streaming data processing capabilities and can be combined with other data technologies.

Integrating Kafka with other data technologies may create a solid and scalable data pipeline that matches your unique business needs.

Apache Kafka

Source: developer.confluent.io

Best Strategies for Creating Scalable Apache Kafka Data Pipelines

Here are some tips for making Kafka data pipelines that can be expanded:

  1. Use a multi-topic architecture: Split your data into different groups based on where it came from or what kind of data it is. This lets you grow each subject separately based on how fast data flows and how much processing power you need.
  2. Change how the Kafka cluster is set up: Set up the Kafka cluster so that it works well and can grow as needed. When setting up your system, think about the replication factor, message retention, and compression parameters.
  3. Use the most recent Kafka translation: Upgrade to the latest version of Kafka to take advantage of the new features and improvements to speed and scalability.
  4. Set up an architecture that can handle faults: Use the built-in replication and fault-tolerance features of Kafka to make sure that your data pipeline won’t lose data if a node or broker fails.
  5. Use batching and compression. Use Kafka’s built-in batching and compression features to reduce the number of messages sent across the network and increase overall speed.
  6. Keep an eye on your Kafka cluster and make it work better: Use Kafka monitoring tools to keep an eye on your Kafka cluster’s health and performance and make changes based on the data.
  7. Choose the right format for your data: Choose the right arrangement for the way you want to use the data. Use a binary format like Avro or Protobuf to reduce the size of a message and speed it up.
  8. Use a schema registry if you want to: Use a schema registry to keep track of the structure of your data. This lets you change the schema without affecting the users who are already using it.
  9. Combine Kafka Connect with other data tools: You can connect Kafka Connect to Hadoop, Elasticsearch, and NoSQL databases, among others.

By following these best practices, you can use Kafka to build a reliable and scalable data pipeline that meets your business needs.

Conclusion

In conclusion, Apache Kafka is a flexible tool for making data pipelines that can grow and be trusted. Due to its distributed design, ability to handle errors, and compatibility with many data technologies, Kafka is often used to stream and process data in real-time. The best way to use Kafka to build a scalable data pipeline is to use a multi-topic design, optimize your cluster configuration, use a fault-tolerant architecture, batch and compress your data, and monitor and optimize your cluster. You can set up a solid and scalable data pipeline for your business using these best practices and Kafka’s features. Kafka can help you analyze big data or build an analytics solution that works well and reliably in real-time.

Key takeaways of this article:

  1. Because Kafka has a distributed architecture, you can add more brokers to your cluster to make it grow horizontally. This makes it a great choice for high-throughput data pipelines.
  2. Kafka’s built-in fault tolerance and replication help make sure that your data pipeline can handle mistakes without losing data.
  3. Kafka works with several different data technologies, such as Apache Spark, Elasticsearch, Hadoop, and NoSQL databases, making it a flexible part of any data pipeline.
  4. Best practices for building scalable data pipelines with Apache Kafka include using a multi-topic design, optimizing your Kafka cluster setup, setting up a fault-tolerant architecture, and making use of batching and compression.
  5. Lastly, monitoring and tuning your Kafka cluster to keep your data pipeline’s speed and ability to grow over time is important.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Guvvala Sujitha (B19EE033)
Guvvala Sujitha (B19EE033)

I have recently graduated aselectrical engineering at IIT Jodhpur. I am interested in software and data engineering domain. I am exploring the same . I am good at organizing skills and team management

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