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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

AWS Kinesis: Benefits & Use cases

T
Trupti Dekate Last Updated : 29 Jul, 2022
8 min read

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

Introduction

Amazon Kinesis is one of the best-managed services that scale particularly flexibly, especially for processing real-time data at a massive site. These services can collect large streams of data records mainly consumed by the application process running on Amazon EC2 instances. This Amazon Kinesis is used to collect, streamline the processes and analyze data so we can easily get perfect insights and quick responses concerning the information.

 

AWS Kinesis
It also offers key features at a cost-effective price to handle rationalized data on a concrete scale with flexible tools per needs and requirements.
Through Amazon Kinesis, you can also get real-time data such as video, audio, application logs, as well as clickstreams for websites, machine learning, and other applications. This new technique from Amazon allows you to analyze and process data immediately instead of waiting long hours after data collection.
Amazon Kinesis is also well used to solve many problems, which was officially launched in November 2013 during the Rebirth conferences. Kinesis is specifically designed to collect data from thousands and hundreds of different sources by bringing it under one roof through group filtering, aggregation, and performing simple manipulations when transferring data from a source location to an end location.
Amazon Kinesis is here to enable the process and analyze the data shortly after it arrives and reacts in real-time, rather than waiting for the data to be collected before the process has even begun. It is highly scalable and supports proof-of-concept or other evaluations.
1. Real-Time: Amazon Kinesis allows you to receive, store and process data in real-time. Insights can be easily derived within seconds or minutes.
2. Fully managed: Amazon Kinesis can easily run streaming applications and can be fully managed without any infrastructure management requirement.
3. Scalability: Amazon Kinesis can easily handle any amount of streaming data and can easily process data from thousands of sources with low latency.
AWS Kinesis

Source: https://aws.amazon.com/kinesis/

What is Amazon Kinesis Stream?

This Amazon Kinesis Stream mostly collects and processes huge amounts of data records in real-time. Data processing applications called Amazon Kinesis Streams Applications can be easily created. Typical kinesis stream applications read data from a Kinesis stream as data records. Processed data records can be easily sent to dashboards that can easily generate alerts; the data can also be sent with several other AWS reports that are used to generate alerts dynamically.
Here are typical Amazon Kinesis Stream usage scenarios:
 
1. Accelerated reception and processing of records and data sources: Producers can easily send data to the stream via a direct route. Application systems and protocols are easy to port and easy to handle. It mostly prevents frontend log data loss or application server failure. It mostly provides accelerated data to help feed the revenue that can easily lead to data on the servers.
2. Real-time metrics and reports: It is possible to easily use the data collected by Kinesis Streams with simple data analysis and real-time reporting. One can easily handle the processing of data applications and work on metrics and reports for system and application logs that are completely flowing into the data.
3. Real-time data analysis: This real-time data analysis mostly combines the power of parallel processing with the real-time value of the data. One can easily process website clickstreams using real-time scenarios. Web usability engagement analysis using several different kinesis stream applications running in parallel.
4. Complex flow processing: One can easily create directed acyclic graphs using Amazon Kinesis streaming applications and data streams. It mostly involves feeding data from multiple Amazon Kinesis Streams applications into another stream, followed by processing with different Amazon Kinesis Streams Applications.

What is AWS Kinesis Firehose?

Amazon Kinesis Firehose is a completely fully managed service for delivering real-time streaming data to destinations such as Amazon S3 (Simple Storage Service), Amazon Elasticsearch Service, or Amazon Redshift. It is fully integrated with the Kinesis Streaming data platform with Amazon Kinesis Analytics and Kinesis Streams.
With the help of Kinesis Firehose, it is easy to write applications or manage resources. Data Producers can be easily configured to send data to Kinesis Firehose, which can automatically deliver the data to the desired destination field. You can also easily configure Kinesis Firehose to transform data before it is delivered.

These are some important features of Kinesis Firehose:

1. Amazon Kinesis Data Streams: Amazon Kinesis Data Streams  Amazon Kinesis Streams are used to collect and process huge streams of data records in real-time. AWS Kinesis Data Stream Applications, which are data processing applications, can be created. These applications read from the data stream in the form of data records. They use the Kinesis Client Library for these operations and can run on Amazon EC2 instances. Processed logs can be sent to AWS dashboards and used to generate alerts, send data to other AWS services, and dynamically change advertising and pricing strategies.
2. Data producer: AWS Kinesis Data Firehose provides the ability to load data streams into AWS data stores. Kinesis Data Firehose provides the simplest approach to capture, transform, and load data streams into AWS data stores. Automatic scaling management in the gigabytes per second range and support for batching, encryption, and compression of streaming data are also essential features in Amazon Kinesis Firehose. Firehose also helps when streaming to RedShift, S3, or Elastic Search to copy data for processing by other services.
3. Buffer size and buffer interval: Kinesis Data Firehose buffers incoming streaming data to a certain size or for some time before delivering it to the targets. The buffer size and buffer interval are configurable when creating the delivery stream. The buffer size is in MB and ranges from 1 MB to 128 MB for the S3 target and 1 MB to 100 MB for the Elasticsearch target. The buffer interval is in seconds and ranges from 60 seconds to 900 seconds. Firehose dynamically increases the buffer size to catch up and ensure that all data is delivered to the destination. If data delivery to the destination lags behind data being written to the delivery stream the buffer size is applied before compression.

What are the benefits of AWS Kinesis?

The main benefits of AWS Kinesis are listed below:

1. Real-Time: Kinesis Streams provide real-time data processing reliably and flexibly. Once data is generated, complex business information and various operations can be easily continuously collected and promptly responded to optimally.

2. Ease of use: A Kinesis Stream is created in seconds. The required data can be easily placed into a Kinesis stream using the Kinesis Producer Library and Kinesis Client Library and can create Kinesis applications to process the data.

3. Elastic: Amazon Kinesis stream throughput can easily scale from megabytes to terabytes in seconds.
Parallel Processing: This mostly helps when multiple Kinesis applications are processing the same stream concurrently. You can easily have one application to do real-time analytics and another to send data to Amazon s3.

4. Low cost: Kinesis Streams has no upfront costs, and payment will only be made for the resources used.
Reliability: Kinesis Streams that replicate with multiple facilities in an AWS region. Data can be retained for 24 hours to prevent data loss in case of a machine or another application failure.

5. Fully Managed: It is fully managed and can be easily run by streaming all applications without the need for infrastructure.

6. Scalability: This is very easy to handle any amount of streaming data with thousands and hundreds of sources with low latency.
  • It can automatically create all streamed data with perfect copies available in all zones, along with durability and backup options
  • You can easily integrate all the data into the relevant AWS service without using the right connectors, reducing data latency.
  • Amazon Kinesis will have unlimited data storage with all the capabilities using the best services.
  • All data streaming processes can be started and set up in seconds without needing more.
  • To scale down and scale up all computing power in seconds,
  • To keep all the data streaming, the data flow easily accordingly.

Amazon Kinesis Capabilities

AWS Kinesis

Source – https://docs.aws.amazon.com/streams/latest/dev/key-concepts.html

 

1. Amazon Kinesis Video Streams: It is used to secure all data streams such as videos, photos, and connected devices to AWS for machine learning, analysis, and further processing, which allows access to all video fragments and encrypts the stored data without any problems.
2. Amazon Kinesis Data Streams: Specifically, this Amazon Kinesis data flow at Amazon is used to build real-time custom model applications by pre-processing the data flow using the most popular frameworks. The best tools like Apache Spark that can successfully run on EC2 instances can easily handle all stored data with data streaming pricing.
3. Kinesis Data Firehouse: This Kinesis data firehouse is stored in an AWS data warehouse near all analytics with all existing intelligence tools to capture, load, and transform data streams into relevant data streams. These tools can be used to continuously prepare all data loads by destination with a durable analysis that provides output such as streaming data analysis.
5. Kinesis Data Analysis: Kinesis Data Analytics in Amazon Kinesis is one of the easiest ways to process all real-time techniques with SQL that must learn all programming languages ​​with processing frameworks. This kinesis data analysis is used to capture streaming data that can run with all standard streaming queries to pre-empt analytics tools to create alerts by responding to them in real-time.
 

Amazon Kinesis Use Cases

1. Video analytics applications: This in-app Amazon Kinesis is also used to secure all streaming video for camera-equipped devices that are located in factories, public places, offices, and homes to AWS account. This video streaming process is also used for video playback, security monitoring, machine learning, face detection, and other analytics.
2. Batch analysis in real-time: With this Amazon Kinesis, you can also easily perform all analytical steps in real-time on relevant data and analyze batch processing from data warehouses through Hadoop frameworks. Data lakes, data sciences, and machine learning are some of the most common methods used in these cases. You use Kinesis Firehouse to update all machine learning models more frequently for new and accurate data outputs to ingest data continuously.
3. Create real-time applications: If you want to build real-time applications, you can also use this Amazon Kinesis to track fraud detection and live leaderboard results. This process can be used to easily process all streamed data into Kinesis Streams with analytics and data stored in the application itself with end-to-end latency. All of these processes can help you learn more about clients, products, services, and applications so you can respond immediately.
4. Analysis of IoT devices: This Amazon Kinesis is used to process streaming data directly from IoT devices such as embedded sensors, TV set-top boxes, and consumer devices. You can also use this data to programmatically send real-time alerts for actions when the sensor exceeds the full operating threshold. It is better to use sample IoT analytics codes while building your application.

What is AWS Kinesis Agent?

AWS Kinesis Agent is considered a standalone Java software application that provides an easy way to collect and send data to Kinesis Firehose.
AWS Kinesis Analytics and AWS Kinesis Pricing: When you go for prices, these Amazon Kinesis Streams go for prices. AWS Kinesis pricing is mostly based on base Shard Hour and PUT Payload Unit sizes and optimal Extended Storage sizes. An hourly rate will also be based on the average number of kinesiology processing units. This Amazon Kinesis Analytics helps to automatically and elastically scale with the required number of KPIs to complete the analytics models.

Conclusion

Amazon Kinesis is also well used to solve many problems, which was officially launched in November 2013 during the Rebirth conferences. Kinesis is specifically designed to collect data from thousands and hundreds of different sources by bringing it under one roof through group filtering, aggregation, and performing simple manipulations when transferring data from a source location to an end location.

  • Amazon Kinesis Stream mostly collects and processes huge amounts of data records in real-time. Data processing applications called Amazon Kinesis Streams Applications can be easily created
  • Data Producers can be easily configured to send data to Kinesis Firehose, which can automatically deliver the data to the desired destination field. Features of AWS Kinesis include:- Amazon Kinesis Data Streams, Data producer, Buffer size, and buffer interval.
  • The main benefits of AWS Kinesis are:- Real-time, easy to use, Elastic, Low cost, and fully managed. Amazon Kinesis use cases consist of Video analytics applications, batch analyses in real-time, creating real-time applications, Analysis of IoT devices
  • This kinesis data analysis is used to capture streaming data that can run with all standard streaming queries to pre-empt analytics tools to create alerts by responding to them in real-time.

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

amazon kinesisAWSblogathon
T
Trupti Dekate

I am an Accountant at Global private Analytics Services working with the Data Analysis Team for handling the budget of various Growing Companies. We provide service of analytics and made the work of new tech companies easy by helping them manage their total investment and giving suggestions.

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