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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

Apache Sqoop: Features, Architecture and Operations

Prateek Majumder
Prateek Majumder Last Updated : 04 Aug, 2023
5 min read

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

Introduction

Apache SQOOP is a specialized tool that facilitates seamless data transfer between HDFS and various structured data repositories. These repositories could include relational databases, enterprise data warehouses, and NoSQL systems. SQOOP operates through a connector architecture, which employs plugins to enhance data connections with external systems, ensuring efficient data migration.

What is Apache Sqoop?

Apache Sqoop, a command-line interface tool, moves data between relational databases and Hadoop. It is used to export data from the Hadoop file system to relational databases and to import data from relational databases such as MySQL and Oracle into the Hadoop file system.

Why Use Sqoop?

A component of the Hadoop ecosystem is Apache Sqoop. There was a need for a specialized tool to perform this process quickly because a lot of data needed to be moved from relational database systems onto Hadoop. This is when Apache Sqoop entered the scene and is now widely used for moving data from RDBMS files to the Hadoop ecosystem for MapReduce processing and other uses.

Apache Sqoop

(Source: https://www.freecodecamp.org/news/an-in-depth-introduction-to-sqoop-architecture-ad4ae0532583/)

Data must first be fed into Hadoop clusters from various sources to be processed using Hadoop. However, it turned out that loading data from several heterogeneous sources was a challenging task. The issues that administrators ran with included:

  • Keeping data consistent
  • Ensuring effective resource management
  • Bulk data loading into Hadoop was not possible.
  • Data loading with scripts was sluggish.

The data stored in external relational databases cannot be accessed directly by the MapReduce application. This approach puts the system in danger of having cluster nodes generate too much stress. Sqoop was the answer. The difficulties of the conventional method were completely overcome by using Sqoop in Hadoop, which also made it simple to load large amounts of data from RDBMS into Hadoop.

Most of the procedure is automated by Sqoop, which relies on the database to specify the data import’s structure. Sqoop imports and exports data using the MapReduce architecture, which offers a parallel approach and fault tolerance. Sqoop provides a command line interface to make life easier for developers. Simple details like source, destination, and database authentication information are all that developers need to include in the sqoop command. Sqoop handles the remaining portion.

Important Features of Apache Sqoop

Apache Sqoop has many essential features. Some of them are discussed here:

  • Sqoop uses the YARN framework to import and export data. Parallelism is enhanced by fault tolerance in this way.
  • We may import the outcomes of a SQL query into HDFS using Sqoop.
  • For several RDBMSs, including MySQL and Microsoft SQL servers, Sqoop offers connectors.
  • Sqoop supports the Kerberos computer network authentication protocol, allowing nodes to authenticate users while securely communicating across an unsafe network.
  • Sqoop can load the full table or specific sections with a single command.

Sqoop Architecture

Using its connectors, sqoop facilitates data migration between Hadoop and external storage systems. These connectors enable Sqoop to work with various widely-used relational databases, such as MySQL, PostgreSQL, Oracle, SQL Server, and DB2. Each connector establishes communication with the corresponding DBMS it is associated with. A generic JDBC connector is also available for connecting to any database that adheres to the JDBC standard. Furthermore, Sqoop Big Data offers specialized connectors optimized for PostgreSQL and MySQL, leveraging database-specific APIs for enhanced performance.

Apache Sqoop

( Source: https://www.guru99.com/introduction-to-flume-and-sqoop.html)

Furthermore, Sqoop for big data is compatible with various third-party connectors for data storage, encompassing enterprise data warehouses and NoSQL stores like Netezza, Teradata, and Oracle (e.g., Couchbase). To use these connectors, users need to download them separately and integrate them into an existing Sqoop installation; they are not included in the default Sqoop bundle.

What happens when you run Sqoop on the back end is fairly simple. The transferred dataset is divided into various divisions, and a map-only job is created with distinct mappers in charge of transferring each partition. Sqoop uses the database information to deduce the data types, handling each data record in a type-safe manner.

How does Sqoop work?

Sqoop executes user commands via a command-line interface. Connectors aid in the transfer of data from any external source to Hadoop. They are also required to isolate production tables in the event of job failure corruption. The map job populates the tables and merges them with the destination table to convey the data.
You can utilize specialized connections to external systems, optimizing import and export. This is incompatible with the Java Database Connectivity built-in (JDBC). The Sqoop extension framework contains plugins that can be used to install Sqoop. Sqoop is only capable of importing and exporting data depending on human instructions; it cannot aggregate data.

Let us look in detail at the two main operations of Sqoop:

Sqoop Import :

The procedure is carried out with the aid of the sqoop import command. We can import a table from the Relational database management system to the Hadoop database server with the aid of the import command. Each record loaded into the Hadoop database server as a single record is kept in text files as part of the Hadoop framework. While importing data, we may also load and split Hive. Sqoop also enables the incremental import of data, which means that if we have already imported a database and want to add a few more rows, we can only do it with the aid of these functions, not the entire database.

Sqoop Export:

The Sqoop export command facilitates the execution of the task with the aid of the export command, which performs operations in reverse. Here, we can transfer data from the Hadoop database file system to the relational database management system with the aid of the export command. Before the operation is finished, the data that will be exported is converted into records. Two processes are involved in the export of data: the first is searching the database for metadata, and the second is moving the data.

Advantages of using Sqoop

Using Apache Sqoop has a lot of advantages. They are:

  1. It entails data transfer from numerous structured sources, like Oracle, Postgres, etc.
  2. Due to the parallel data transport, it is quick and efficient.
  3. Many procedures can be automated, which increases efficiency.
  4. Integration with Kerberos security authentication is feasible.
  5. Direct data loading is possible from HBase and Hive.
  6. It is a powerful tool with a sizable support network.
  7. As a result of its ongoing development and contributions, it is frequently updated.

Limitations of Using Sqoop

Just like the advantages, there are some limitations of using Sqoop; they are:

1. Apache Sqoop cannot be stopped or started again.
2. The hardware setup of the RDBMS server affects how well the Sqoop Export performs.
3. Sqoop is slow since the backend processing uses the MapReduce model.
4. Special handling is required for partial import and export failures.
5. Apache Sqoop offers larger connectors for a few of the databases.

Conclusion

Massive amounts of data must be loaded into Hadoop clusters for analytical processing using Hadoop. Several difficulties are involved with loading heterogeneous bulk data sources into Hadoop and processing it. Before choosing the best strategy for data load, it is important to maintain and ensure data consistency and optimal resource utilization. To summarise the operation of Sqoop:

  • Sqoop is used in the Hadoop cluster.
  • HDFS receives data imports from RDBMS or NoSQL databases.
  • The data is loaded into HDFS after being divided into several forms using mappers.
  • Sqoop exports data back into the RDBMS while ensuring the database’s data schema is maintained and followed.

In the modern world, where data needs to be moved from multiple sources and in various forms onto Hadoop and subsequently moved back to relational database systems, Apache Sqoop is a valuable tool. With the release of its most recent versions, Apache Sqoop will only increase due to the growing importance of big data in all business sectors in a world driven by technology.

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Prateek Majumder
Prateek Majumder

Prateek is a dynamic professional with a strong foundation in Artificial Intelligence and Data Science, currently pursuing his PGP at Jio Institute. He holds a Bachelor's degree in Electrical Engineering and has hands-on experience as a System Engineer at TCS Digital, where he excelled in API management and data integration. Prateek also has a background in product marketing and analytics from his time with start-ups like AppleX and Milkie Way, Inc., where he was involved in growth campaigns and technical blog management. Recognized for his structured thinking and problem-solving abilities, he has received accolades like the Dr. Sudarshan Chakraborty Award for Best Student Performance. Fluent in multiple languages and passionate about technology, Prateek continues to expand his expertise in the rapidly evolving AI and tech landscape.

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