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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

Layers of the Data Platform Architecture

Harshit Ahluwalia
Harshit Ahluwalia Last Updated : 17 Jan, 2022
5 min read

Overview

In this article, I will walk you through the layers of the Data Platform Architecture. First of all, let’s understand what is a Layer, a layer represents a serviceable part that performs a precise job or set of tasks in the data platform. The different layers of the data platform architecture that we are going to discuss in this article include the Data ingestion layer, Data storage layer, Data processing layer and Analysis, User interface layer, and Data Pipeline layer. If you are new to Data Engineering, then follow these top 9 skills required to be a data engineer.

Source: Author

Table of content

  1. Data Collection Layer or Data ingestion Layer
  2. Data Storage Layer or Integration Layer
  3. Data Processing Layer
  4. Analysis and User Interface Layer
  5. Data Pipeline Layer

 

Data Collection Layer

 

Source: Author

This is the first layer of the data platform architecture. The Data Collection layer as the name suggests is responsible for connecting to the source systems and bringing data into the data platform in a periodic manner. This layer performs the following tasks:

 

  1. This layer is responsible for connecting to the data sources.
  2. This layer is responsible for transferring data from data sources to the data platform in streaming mode or batch mode or both.
  3. Moreover, this layer is responsible for maintaining the information about the data collected in the metadata repository. For example, how much data is gobbled into the data platform and other descriptive information?

 

There are various tools that are available in the market, but some of the popular tools include Google Cloud Data Flow, IBM Streams, Amazon Kinesis and Apache Kafka are some of the tools that are used for data ingestion that supports both batch and streaming modes.

 

Source: Author

Once the data is gobbled, it needs to be stored and integrated into the data platform like the way we store food in the stomach. For storing and integrating the data, we head towards the second layer of the data platform that is the Data storage layer or Data Integration layer.

Data Storage and Data Integration Layer

 

Source: Author

This is the second layer of the data platform architecture. The Data collection layer as the name suggests is responsible for storing data for processing and long-term use. Moreover, this layer is also responsible for making data available for processing in both streaming and batch modes. As this layer is responsible for making data available for processing, it needs to be reliable, scalable, high-performing and cost-efficient. IBM DB2, IBM DB2, Microsoft SQL Server, MySQL, Oracle Database, and PostgreSQL are some of the popular relational databases. But nowadays, cloud-based relational databases gained popularity over the recent years, some cloud-based relational databases are IBM DB2, Google Cloud SQL and SQL Azure. In the NoSQL or non-relational database systems on the cloud, we have IBM Cloudant, Redis, MongoDB, Cassandra, and Neo4J. Tools for integration includes IBM’s cloud Pak for Data, IBM’s cloud Pak for Integration and Open Studio. Once the data has been ingested, stored and integrated, it needs to be processed. So with this, we move forward to Data Processing Layer

 

Data Processing Layer

 

Source: Author

This is the third layer of the data platform architecture. As the name suggests, this layer is responsible for a processing task. The processing includes data validations, transformations and applying business logic to the data. The processing layer should be able to perform some tasks that include:

  1. Read data in batch or streaming modes from storage and apply transformations.
  2. Support popular querying tools and programming languages.
  3. Scale to meet the processing demands of a growing dataset.
  4. Provide a way for analysts and data scientists to work with data in the data platform.

The transformation task that usually occurs in this layer include:

  1. Structuring: These are the actions that change the structure of the data. This change can be simple or complex in nature. The simple one can also be like changing the arrangement of fields within the record or dataset or complex as combining fields complex structures using joins and unions.
  2. Normalization: This part focuses on reducing redundancy and inconsistency. It also focuses on cleaning the database of unused data.
  3. Denormalization: Denormalization is the task of combining data from multiple tables into a single table so that it can be queried more efficiently for reporting and analysis purposes.
  4. Data Cleaning: Data Cleaning, which fixes irregularities in data to provide credible data for downstream applications and uses.

 

There are numerous amount of tools available in the market for performing these operations on the data includes Such as spreadsheets, OpenRefine, Google DataPrep, Watson Studio Refinery, and Trifacta Wrangler. Python and R also offer several libraries and packages that are explicitly created for processing data. It’s very important to know that storage and processing are not always been performed in separate layers. For example, in relational databases, storage and processing are both occur in the same layer while in Big data systems, data is first stored in the Hadoop File Distributed system and then processed in the data processing engine such as Spark.

 

Analysis and User Interface Layer

 

Source: Author

This is the fourth layer of the data platform architecture. This layer is responsible for delivering the process data to the end-users that including business intelligence analysts and business stakeholders who consume these data with the help of interactive dashboards and reports, moreover, data scientists and data analysts fall under this end-user category that further process this data for the specific use case. This layer needs to support querying tools such as SQL tools and No-SQL tools and programming languages like Python, R and Java and moreover, these layers need to support API’s that can be used to run reports on data for both online and offline processing.

 

Data Pipeline Layer

 

Source: Author

This is the last layer of this architecture, this layer is responsible for implementing and maintaining a continuous flow of data through this data pipeline. It is the layer that has the capability to extract, transform and load tools. There are a number of data pipeline solutions available, most popular among them being Apache Airflow and DataFlow.

 

End Notes

In this article, you learned about the layers of a data platform architecture. This is a simplified rendition of a complex architecture that supports a broad spectrum of tasks.

data collectiondata platform architectureData Storage
Harshit Ahluwalia
Harshit Ahluwalia

Growth Hacker | Generative AI | LLMs | RAGs | FineTuning | 62K+ Followers https://www.linkedin.com/in/harshit-ahluwalia/ https://www.linkedin.com/in/harshit-ahluwalia/ https://www.linkedin.com/in/harshit-ahluwalia/

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