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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
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Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
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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
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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
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A Complete Guide on Building an ETL Pipeline for Beginners

vijay
vijay Last Updated : 04 Aug, 2022
11 min read

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

Introduction on ETL Pipeline

ETL pipelines are a set of processes used to transfer data from one or more sources to a database, like a data warehouse. Extraction, transformation, and loading are three interdependent procedures used to pull data from one database and place it in another.

As organizations generate more data, data sources, and data types, the need for analytics, data science, and machine learning initiatives to generate business insights grows as well. Prioritizing these initiatives is becoming increasingly critical because it is crucial to translate raw, messy data into clean, fresh, reliable data before pursuing them. Data engineers use ETL or extract, transform, and load, to extract data from multiple sources, transform the data into a usable and trusted resource, and load that resource into the systems end-users can access and use downstream to solve business problems

ETL processes are conducted via an ETL pipeline (also known as a data pipeline). Data pipelines are a set of tools and actions for transferring data from one system to another, where it might be stored and managed differently. Pipelines also enable for the automatic gathering of data from a variety of sources, as well as the transformation and consolidation of that data into a single high-performance data storage.

Phases in ETL

 

ETL Process

Extract

In this process, the first step is to extract the data from the target sources, which are usually heterogeneous, such as business systems, APIs, sensor data, marketing tools, and transaction databases, among others. It is clear that some of these data types are likely to be the structured outputs of widely used systems, while others are semi-structured JSON server logs. There are three methods of extracting data

  • Partial Extraction – You can easily obtain data if you are notified whenever a record is updated by the source system
  • Partial Extraction (with update notification) – Some systems are unable to let you know if an update has occurred; however, they can point you to records that have changed so that you can get an extract of the records that have changed.
  • Full extract – For certain systems, it can be difficult to figure out which data has been altered. Extraction of the entire system is the only way to obtain the data. To show the changes made, you need a copy of the latest extract in the same form.

Transform

The next step is to transform the raw data, which has been extracted from the sources, into a format that can be accessed by different applications. The goal of this stage is to clean, map and transform data so that it is operationally useful. It involves several types of transformations that guarantee the quality and integrity of data. Typically, data is not loaded directly into the target data source but is instead uploaded into a staging database first. In case something doesn’t go as planned, you can easily rollback. Here, you can create audit reports for regulatory compliance, or analyze any data issues and fix them.

Load

Finally, the load function is the process of copying converted data from a staging area to a target database that may or may not have existed earlier. This method might be straightforward or complex, depending on the application’s requirements. ETL tools or custom code can be used to complete each of these processes.

A Few Key Differences to Understand

Traditional ETL vs. Cloud ETL

1. Traditional ETL

Traditional or legacy ETL is meant for data that is totally on-premises and overseen by a skilled in-house IT staff whose job it is to build and manage in-house data pipelines and databases.

It is usually based on time-consuming batch processing sessions that allow data to be transported in scheduled batches, ideally when network traffic is low. It can be difficult to perform real-time analysis. IT teams often construct extensive, labor-intensive adaptations and stringent quality control to extract the essential analytics. Furthermore, typical ETL systems struggle to manage significant data volume increases, forcing businesses to choose between detailed data and rapid performance.

2. Cloud ETL

Cloud ETL, also known as modern ETL, takes both structured and unstructured data from any data source type, whether on-premises or in the cloud, and combines, transforms, and loads that data into a centralized location where it can be accessed on-demand.

Across a range of use cases within a company, cloud ETL is often used to make data quickly available for analysts, developers, and decision-makers.

3.  ETL pipeline vs. Data Pipeline

While the phrases ETL pipeline and data pipeline are sometimes used interchangeably, they should not be because they describe vastly different things.

Any combination of procedures, tools, or activities used to ingest data from a range of sources and transport it to a target repository is referred to as a data pipeline. Within networked systems, this might result in added activities and process flows.

The transformed data is saved in a database or data warehouse via an ETL pipeline, and the data may then be used for business analytics and insights.

ETL Pipeline vs. ELT Pipeline

ETL (extract transform load) and ELT (extract load transform) are two different data integration processes that use the same steps in a different order to help with different data management functions.

Both ELT and ETL extract raw data from different data sources like an enterprise resource planning (ERP) platform, social media platform, Internet of Things (IoT) data, spreadsheet, and more. With ELT, raw data is then loaded directly into the target data warehouse, data lake, relational database, or data store. This allows data transformation to happen as needed. It also lets you load datasets from the source. With ETL, after data is extracted, it is then defined and transformed to improve data quality & integrity and is later loaded into a data repository where it can be used.

If you’re creating data repositories that are smaller, need to be retained for a longer period, and don’t need to be updated very often, then ETL is the way to go. If you’re dealing with high-volume datasets and big data management in real-time, then ELT would be best for your use case.

ETL pipeline vs. ELT Pipeline
Parameters ETL ELT
Order of the Process Data is transformed at the staging area before being loaded into the target
system		 Data is extracted and loaded into the target system directly. The
transformation step(s) is/are managed in the target.
Key Focus Loading into databases where computing is a precious resource.
Transforming data, masking data, normalizing, joining between tables in-flight.		 Loading into Data Warehouses. Mapping schemas directly into the warehouse.
Separating load from transform and execute transforms on the warehouse.
Privacy Compliance Sensitive information can be redacted before loading into the target
system		 Data is uploaded in its raw form without any sensitive details removed.
Masking must be managed in the target system
Maintenance Requirements Transformation logic and schema-change management may need more manual
overhead		 Maintenance is addressed in the data warehouse where transformations are
implemented
Latency Generally higher latency with transformations can be minimized with
streaming ETL		 Lower latency in cases with little-to-no transformations
Data flexibility Edge cases can be managed with custom rules and logic to maximize uptime Generalized solutions for edge cases around schema drift and major
resyncs – can lead to downtime or increased latency in not carefully planned
Analysis flexibility Use cases and report models must be defined beforehand Data can be added at any time with schema evolution. Analysts can build
new views of the target warehouse.
Scale of Data Can be bottlenecked by ETL if it is not a scalable, distributed
processing system		 Implicitly more scalable as less processing takes place in the ELT tool

What are the Distinct Types of ETL Pipelines?

ETL data pipelines are categorized based on their latency. The most common forms of ETL pipelines employ either batch processing or real-time processing.

  • Real-time Processing Pipelines

Users can ingest structured and unstructured data from a variety of streaming sources, including IoT, linked devices, social media feeds, sensor data, and mobile applications, using real-time data pipelines. The data is accurately collected thanks to a high-throughput messaging system.

To drive application features like real-time analytics, GPS position tracking, fraud detection, predictive maintenance, targeted marketing campaigns, or initiative-taking customer care, data transformation is overseen using a real-time processing engine like Spark streaming.

  • Batch Processing Pipelines

Batch processing is used in classic analytics and business intelligence applications where data is collected, converted and transferred to a cloud data warehouse on a regular basis.

With minimal human intervention, users may swiftly load high-volume data from siloed sources into a cloud data lake or data warehouse and schedule jobs for data processing. To more efficiently manage massive amounts of data and repetitive operations, ETL in batch processing collects and stores data during an event known as a “batch window.”

Building an ETL Pipeline with Batch Processing 

To create a typical ETL process, follow the steps below to move and process data from source databases to a data warehouse in batches. Building an enterprise ETL pipeline from scratch is difficult; instead, you’ll use ETL solutions like Stitch or Blendo to simplify and automate much of the process.

  • Reference data

Create a data collection that defines the range of values that your data can have. You can set up the list of permitted country codes, for example, in a country data field.

  • Extract information from data sources

The correct extraction of data is the foundation for the success of future ETL procedures. Most ETL systems aggregate data from a variety of sources, each with its own data organization and format – such as relational databases, non-relational databases, CSV files, XML, JSON, and so on. Successful extraction puts data into a single format that can be processed in a standardized manner.

  • Validation of Data

An automated process verifies that data retrieved from sources has the expected values — for example, a date field in a database of financial transactions from the previous year should hold valid dates from the previous year. If data does not pass the validation requirements, it is rejected by the validation engine. You continuously examine rejected records to see what went wrong, correct the source data, or alter extraction to fix the problem in the next batches.

  • Data Transformation

Deleting superfluous or incorrect data (cleaning), applying business rules, guaranteeing data integrity (ensuring that the data was not altered in the source or by ETL and that no data was dropped in earlier stages), and creating aggregates as needed If you need to examine revenue, for example, you can combine the dollar amounts of invoices into a daily or monthly total. You’ll need to create and test a set of rules or functions that can do the appropriate transformations, then run them on the data you’ve gathered.

  • Stage

Normally, altered data is not loaded at once into the target data warehouse. Data should be entered into a staging database first, which makes it easier to undo if something goes wrong. You can also generate audit reports for regulatory compliance, as well as find and correct data errors, at this point.

  • Upload Data to a Data Warehouse

The data is being loaded into the target tables. When the ETL pipeline loads a new batch, certain data warehouses overwrite earlier data. This can happen daily, weekly, or monthly. In other circumstances, ETL can add new data without overwriting it, signaling that it is new with a timestamp. You must go ahead with caution to avoid the data warehouse “exploding” due to disc space and performance constraints.

Using Stream Processing to Create an ETL Pipeline

Real-time data is often used in modern data operations, such as web analytics data from a huge e-commerce website. Because you can’t extract and transform data in huge batches in these scenarios, you’ll need to do ETL on data streams. This means that data should be managed, converted, and saved to the target datastore as soon as client applications give data to the data source.

Confluent describes an ETL pipeline based on Kafka in the diagram below.

Using Stream Processing to Create an ETL Pipeline
  • Data extraction into Kafka – the Confluent JDBC connector pulls each row of the source table and puts it as a key/value pair into a Kafka topic (you can do this without using Confluent’s commercial product). This subject is read by applications that are interested in the status of this table. Kafka writes new messages to the Kafka topic when client applications add rows to the source table, enabling a real-time data stream.
  • Pulling data from Kafka topics – the ETL programmed extracts messages from the Kafka topic as Avro records, builds an Avro schema file, deserializes the messages, and converts them to KStream objects.
  • Transform data in a KStream object – the stream processor gets one record at a time, processes it, and can output one or more output records for downstream processors using the Kafka Streams API. These can-do data operations on many messages, such as aggregation, or transforming messages one at a time, filtering them depending on conditions.
  • Data must now be streamed into target systems – such as a data warehouse or data lake, from the ETL application, which still holds the enriched data. Confluent, for example, suggests streaming data to Amazon S3 via their S3 Sink Connector. You can use Amazon Kinesis to integrate with other systems, such as streaming data to a Redshift data warehouse.

Use Case in Artificial intelligence (AI) and Machine learning (ML) in ETL

ETL powered by AI and machine learning automates key data operations, ensuring that the data you receive for analysis matches the quality standards needed to give trusted insights for decision-making. It can be used in conjunction with other data quality tools to ensure that data outputs fit your specific requirements.

  • Data Democratization and ETL

ETL is required by more than just technical folks. Business users must also be able to find data quickly and integrate it into their systems, services, and applications. This is simple to achieve by including AI into the ETL process at both design and run time. ETL tools using AI and machine learning may learn from past data and recommend the most reusable components for the business users’ case, such as data mappings, transformations, configurations, and more.

  • ETL pipelines can be Automated

Time-saving automation for onerous and recurrent data engineering chores is possible with AI-based ETL technologies. Improve the efficiency of your data administration and data distribution. Ingest, process, integrate, enrich, prepare, map, define, and catalog data automatically.

  • ETL for Data Processing Allows for Greater Business Agility

Because ETL decreases the effort needed to gather, prepare, and integrate data, teams will be able to move more swiftly. ETL automation powered by AI boosts productivity by allowing data professionals to get the data they need, when they need it, without having to develop code or scripts, saving time and money.

The Challenges of moving from ETL to ELT

Cloud data warehouses and data lakes’ greater processing capabilities have changed the way data is transformed. Many firms have shifted from ETL to ELT because of this transformation. This isn’t usually a simple adjustment.

ETL mappings have matured to the point that they can manage a wide range of data kinds, sources, frequency, and formats. To convert these mappings into an ELT-friendly format, you’ll need an enterprise data platform that can handle data and provide pushdown optimization without damaging the front end. Developers often wind up hand-coding queries to add complex transformations if the platform can’t create the ecosystem or data warehouse-specific code required. This time-consuming procedure is costly, difficult, and inconvenient. That’s why choosing a platform with a user-friendly interface and the ability to replicate the same mappings and run in an ELT pattern is critical.

Benefits of ETL Processes

ETL integration offers several advantages, including:

  • Preserves resources: ETL can lower the amount of data stored in the warehouse, allowing businesses to conserve storage, bandwidth, and compute resources in cases where storage costs are a concern. This is less of an issue with commoditized cloud computing engines.
  • Improves compliance: Before delivering data to the data warehouse, ETL can mask and remove sensitive data such as IP or email addresses. Companies can follow data privacy and protection standards such as GDPR, HIPAA, and CCPA by masking, dropping, and encrypting certain information.
  • Well-developed tools: Data extraction, transformation, and loading (ETL) have been around for decades, and businesses can use a variety of robust platforms to do so. This makes creating and keeping an ETL pipeline a lot easier.
  • Get a thorough understanding of your company’s history: ETL provides historical context for your business by merging legacy data with data collected from new platforms and applications when used with an enterprise data warehouse (data at rest).
  • Migrating data to the cloud should be as simple as possible: To improve data accessibility, application scalability, and security, ETL helps you move your data to a cloud data lake or cloud data warehouse. More than ever, businesses rely on cloud integration to boost operations.
  • Allow business intelligence to be derived from any data with any latency: Today’s businesses must evaluate a variety of data types, including structured, semi-structured, and unstructured data, from a variety of sources, including batch, real-time, and streaming.ETL solutions make it easier to extract relevant insights from data, allowing you to spot new business possibilities and make better decisions.
  • Deliver data that is both clean and dependable for decision-making: Throughout the data lifecycle, use ETL technologies to convert data while keeping data lineage and traceability. This means that, regardless of their data demands, all data practitioners, from data scientists to data analysts to line-of-business users, have access to exact data.

Drawbacks of ETL Processes

Companies that use ETL also must deal with several drawbacks:

  • Legacy ETL is slow: Disk-based staging and transformations are required by traditional ETL systems.
  • Frequent maintenance: Extraction and transformation are managed through ETL data pipelines. However, if analysts need other data types or if the source systems begin to output data in different formats and schemas, they must be refactored.
  • Higher Upfront Cost: The scope of a data integration project can be expanded by defining business logic and transformations.

Conclusion

The purpose of this guide is to provide you with an introduction to the ETL pipeline that covers different aspects like introductions, key differences, different types of pipelines, Case studies, Area of application, and building an ETL pipeline.

When data is shifted from one place to another, various operators can answer a query systematically and correctly rather than searching through diverse data sources. Data management could be improved by a well-structured ETL pipeline. Additionally, they allow data managers to quickly iterate to meet the ever-changing data requirements of the business.

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

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vijay
vijay

||📊Lead Technical Analyst at HP||Connecting data to dollar||📋Certified - Data Scientist||SAP ERP||Microsoft Data Analyst,LSS Yellow Belt||🥇3*MVP,1*APAC Champion@HP||🏆Blogathon Winner’22@Xebia,2*AVCC||AVCC Member’22||

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