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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

NewSQL: The Bridge between SQL and NoSQL

Dulari
Dulari Last Updated : 23 Jun, 2022
4 min read

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

Introduction

NewSQL is more specific than NoSQL. Relational data and the SQL query language are the foundations of NewSQL systems. They want to address the NoSQL movement’s scalability, flexibility, and lack of focus difficulties. More consistency is provided with the new SQL. Within this category, however, there are a few differences. HANA was a robust business reporting platform that could also handle low transactional demand, making it perfect for SAP deployments. Hekaton adds enhanced memory processing to the more typical MS SQL Server. Both systems are currently non-clustering and are designed to quickly replace or supplement SQL deployments.

Suppose you have gigabytes to terabytes of data that require high-speed transactional access. You may have an input stream, such as sensors, mobile phones, or network access points, and you will require per-event transactions to compute real-time answers and analytics. Using the “ingest, analyze, decide” approach to tackle this issue, with analytics and choices determined per request rather than processing in batch processing. In this case, NewSQL systems, which combine the scalable architecture of NoSQL with greater consistency, are the best option.

Characteristics of NewSQL

1. SQL as the query language of choice
2. Transactions are supported with Atomicity, Consistency, Isolation, and Durability property
3. A concurrency control technique that isn’t locked (e.g., MVCC)
4. An architecture with significantly improved per-node performance
5. A shared-nothing, scale-out architecture

Completely new database platforms are the first type of NewSQL system. These are made to work in a cluster of shared-nothing nodes, with each node owning a portion of the data. These databases are frequently built up with architecture in mind, with features like concurrency management, flow control, and query processing.

Google Spanner, Clustrix, VoltDB, MemSQL, Pivotal’s GemFire XD, SAP HANA, NuoDB, TiDB, and Trafodion are examples of systems in this category.

Difference between SQL and NoSQL and NewSQL

There are some differences between SQL, NoSQL, and New SQL. The following figure depicts the difference very nicely.

 

Differences | SQL

                                                  Figure 1: SQL v/s NoSQL v/s NewSQL [1]

Parameters to compare

SQL

NoSQL

NewSQL
1. Scalability

Scalable Reads

Scalable reads and writes, horizontally scalable

Scalable reads and writes, horizontally scalable
2. Schema

Relational Schema / Table

Schema free

Supports both.
3. High Availability

Custom High Availability

Auto High Availability

Built-in High Availability
4. Storage

On-Disk +Cache

On-Disk +Cache

On-Disk +Cache
5. Cloud Support

Not Fully

Supported

Fully Supported
6. Query Complexity

Low

High

Very High
7. ACID-CAP BASE

ACID

CAP through BASE

ACID
8. OLTP

Not Fully Supported

Not Supported

Fully Supported
9. Security Concerns

Very Very High

Low

Low
10. Examples

Oracle, MsSQL, MySQL, etc.

MongoDB, Cassandra, Redis, etc.

CockroachDb, Google Spanner, VoltDb

The Most Effective NewSQL Databases

NewSQL is an intriguing technology that is becoming more widely available. The top NewSQL databases on the market today are adaptable and powerful solutions that have been meticulously designed to fit your company’s requirements.

VoltDB

VoltDB, founded in 2009, has lately risen to become one of the most prominent NewSQL providers in the booming in-memory database market. Python, Java, PHP, and C++ are among the languages supported. VoltDB is an open-source and commercial tool designed to operate with OLTP queries and includes ACID guarantees, sharding, and data replication. It employs deterministic concurrency management and a global serial order controller for sharded transactions that span many partitions.

ClustrixDB

ClustrixDB is a database that provides good peace of mind thanks to automated fault tolerance and a mechanism designed to preserve numerous data copies. Clustrix is compatible with MySQL and combines multi-version concurrency control (MVCC) with two-phase locking for input conflict resolution. It was founded in 2006 and was recently bought by MariaDB. Clustrix has various benefits over AWS and other popular cloud-based database servers, including sophisticated failover and fault-tolerance features and the capacity to expand and perform beyond 1TB. Additional features of the cluster-based cloud architecture include online schema modifications, linearly scalable, and online growth.

 NuoDB

NuoDB, founded in Cambridge, Massachusetts, is widely used by prominent FinTech companies. Dassault Systèmes, a French 3D design corporation, purchased it in November 2020 for the supply of scalable, multi-cloud distributed SQL database solutions. Dassault Système utilizes NuoDB to manage their worldwide database of online 3D component makers and clients, bringing together engineers and designers from all over the world.

CockroachDB

CockroachDB is an adaptable NewSQL database invented and built by Cockroach Labs that is utilized by a broad range of well-known businesses, including JPMorgan Chase, Cisco, and Best Buy. In addition, CockroachDB is a strong tool for organizational automation since it can simply link to cloud-based cluster solutions like Kubernetes. CoackroachDB, created by ex-Google personnel, is based on Spanner and uses an open-source distributed SQL database with horizontal scaling and a highly consistent transactional layer.

VMware Tanzu GemFire

This NewSQL database, previously known as Pivotal Gemfire XC, was bought by VMware in 2019 and renamed VMware Tanzu GemFire. Gemfire is a multi-tiered, geo-distributed, clustered cloud database with exceptional performance. Gemfire’s memory, horizontally scalable design distinguishes it from other NewSQL DBs by ensuring consistent low latency even during high usage.

NewSQL’s Advantages

  • It reduces application complexity by greater consistency and provides complete transactional support.
  • Elastic scalability that dynamically rebalances partitions
  • Instant event responses from real-time alerts
  • Resilient’shared-nothing’ architecture that provides high availability is just a few of Gemfire XC’s standout characteristics.
  • .NET and NodeJS support for different languages is frictionless.
  • New SQL uses common tools and SQL that is familiar.
  • It has more analytics, including SQL and extensions.
  • Many New SQL systems combine traditional data, such as RDBMS and query models, with NoSQL-style clustering.

NewSQL Limitations

  • No
    NewSQL systems are intended to be as general-purpose as SQL systems.
  • In-memory
    systems may not suit data sets larger than a few terabytes.
  • Only
    provides limited access to SQL systems’ extensive tools.

Conclusion

NewSQL is a subset of current RDBMS that aims to match the scalability of NoSQL systems for read-write workloads in online transaction processing (OLTP) while keeping the ACID (Atomicity, Consistency, Isolation, Durability) features of a traditional database system. The primary limitation of NoSQL is that it lacks the ACID feature.

  • This article explains NewSQL and the tools on which it can be performed. 
  • It also describes the significant differences between SQL, NoSQL, and NewSQL.
  • NewSQL is just like a bridge between SQL and NoSQL. NewSQL is a type of database solution that is gaining a lot of attention from the IT community.

Reference

[1] https://medium.com/rabiprasadpadhy/google-spanner-a-newsql-journey-or-beginning-of-the-end-of-the-nosql-era-3785be8e5c38

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

Dulari
Dulari

Dr. Dulari Bhatt is Master Trainer at Edunet Foundation. She is having 12 years of experience in academia. Her main research interests are in the field of Big Data Analytics, Computer Vision, Machine Learning, and Deep Learning. She has authored 3 technical books and published several research papers in reputed international journals. She is an active writer in Analytics Vidhya and OSFY magazine. She has received a gold category award from GTU for spreading IPR awareness. Several times winner of Data Science Blogathons..

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