What is ETL (Extract, Transform, Load)? (2022)

ETL is a process that extracts, transforms, and loads data from multiple sources to a data warehouse or other unified data repository.

What is ETL?

ETL, which stands forextract, transform and load,is a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into adata warehouseor other target system.

As the databases grew in popularity in the 1970s, ETL was introduced as a process for integrating and loading data for computation and analysis, eventually becoming the primary method to process data for data warehousing projects.

ETL provides the foundation for data analytics and machine learning workstreams. Through a series of business rules, ETL cleanses and organizes data in a way which addresses specific business intelligence needs, like monthly reporting, but it can also tackle more advanced analytics, which can improve back-end processes orend userexperiences. ETL is often used by an organization to:

(Video) What is ETL (Extract, Transform, Load)?

  • Extractdatafrom legacy systems
  • Cleanse the data to improve data quality and establish consistency
  • Load data into atarget database

ETL vs ELT

The most obvious difference between ETL and ELT is the difference in order of operations. ELT copies or exports the data from the source locations, but instead of loading it to a staging area for transformation, it loads the raw data directly to the target data store to be transformed as needed.

While both processes leverage a variety of data repositories, such as databases, data warehouses, and data lakes, each process has its advantages and disadvantages. ELT is particularly useful for high-volume, unstructured datasets as loading can occur directly from the source. ELT can be more ideal for big data management since it doesn’t need much upfront planning for data extraction and storage. The ETL process, on the other hand, requires more definition at the onset. Specific data points need to be identified for extraction along with any potential “keys” to integrate across disparate source systems. Even after that work is completed, the business rules for data transformations need to be constructed. This work can usually have dependencies on the data requirements for a given type of data analysis, which will determine the level of summarization that the data needs to have. While ELT has become increasingly more popular with the adoption of cloud databases, it has its own disadvantages for being the newer process, meaning that best practices are still being established.

How ETL works

The easiest way to understand how ETL works is to understand what happens in each step of the process.

Extract

During data extraction, raw data is copied or exported from source locations to a staging area. Data management teams can extract data from a variety of data sources, which can be structured or unstructured. Those sources include but are not limited to:

  • SQL orNoSQLservers
  • CRM and ERP systems
  • Flat files
  • Email
  • Web pages

Transform

In the staging area, the raw data undergoes data processing. Here, the data is transformed and consolidated for its intended analytical use case. This phase can involve the following tasks:

(Video) What is Extract Transform Load (ETL)? | Edureka

  • Filtering, cleansing, de-duplicating, validating, and authenticating the data.
  • Performing calculations, translations, or summarizations based on the raw data. This can include changing row and column headers for consistency, converting currencies or other units of measurement, editing text strings, and more.
  • Conducting audits to ensure data quality and compliance
  • Removing, encrypting, or protecting data governed by industry or governmental regulators
  • Formatting the data into tables or joined tables to match the schema of the target data warehouse.

Load

In this last step, the transformed data is moved from the staging area into a target data warehouse. Typically, this involves an initial loading of all data, followed by periodic loading of incremental data changes and, less often, full refreshes to erase and replace data in the warehouse. For most organizations that use ETL, the process is automated, well-defined, continuous and batch-driven. Typically, ETL takes place during off-hours when traffic on the source systems and the data warehouse is at its lowest.

ETL and other data integration methods

ETL and ELT are just two data integration methods, and there are other approaches that are also used to facilitate data integration workflows. Some of these include:

  • Change Data Capture (CDC)identifies and captures only the source data that has changed and moves that data to the target system. CDC can be used to reduce the resources required during the ETL “extract” step; it can also be used independently to move data that has been transformed into a data lake or other repository in real time.
  • Data replicationcopies changes in data sources in real time or in batches to a central database.Data replicationis often listed as a data integration method. In fact, it is most often used to create backups fordisaster recovery.
  • Data virtualizationuses a software abstraction layer to create a unified, integrated, fully usableviewof data—without physically copying, transforming or loading the source data to a target system.Data virtualizationfunctionality enables an organization to create virtual data warehouses, data lakes and data marts from the same source data for data storage without the expense and complexity of building and managing separate platforms for each. While data virtualization can be used alongside ETL, it is increasingly seen as an alternative to ETL and to other physical data integration methods.
  • Stream Data Integration (SDI)is just what it sounds like—it continuously consumes data streams in real time, transforms them, and loads them to a target system for analysis. The key word here iscontinuously. Instead of integrating snapshots of data extracted from sources at a given time, SDI integrates data constantly as it becomes available. SDI enables a data store for powering analytics, machine learning and real-time applications for improving customer experience, fraud detection and more.

The benefits and challenges of ETL

ETL solutions improve quality by performing data cleansing prior to loading the data to a different repository. A time-consuming batch operation, ETL is recommended more often for creating smaller target data repositories that require less frequent updating, while other data integration methods—including ELT (extract, load, transform), change data capture (CDC), and data virtualization—are used to integrate increasingly larger volumes of data that changes or real-time data streams.

Learn more about data integration.

ETL tools

In the past, organizations wrote their own ETL code. There are now many open source and commercial ETL tools and cloud services to choose from. Typical capabilities of these products include the following:

(Video) What is ETL for Beginners | ETL Non-Technical Explanation

  • Comprehensive automation and ease of use:Leading ETL tools automate the entire data flow, from data sources to the target data warehouse. Many tools recommend rules for extracting, transforming and loading the data.
  • A visual, drag-and-drop interface:This functionality can be used for specifying rules and data flows.
  • Support for complex data management:This includes assistance with complex calculations, data integrations, and string manipulations.
  • Security and compliance:The best ETL tools encrypt data both in motion and at rest and are certified compliant with industry or government regulations, like HIPAA and GDPR.

In addition, many ETL tools have evolved to include ELT capability and to supportintegration of real-time and streaming data for artificial intelligence (AI) applications.

The future of integration -APIusing EAI

Application Programming Interfaces (APIs) using Enterprise Application Integration (EAI) can be used in place of ETL for a more flexible, scalable solution that includesworkflowintegration. While ETL is still the primarydata integrationresource, EAI is increasingly used withAPIsin web-based settings.

ETL, data integration, and IBM Cloud®

IBM offers several data integration tools and services which are designed to support a business-ready data pipeline and give your enterprise the tools it needs to scale efficiently.

IBM, a leader in data integration, gives enterprises the confidence they need when managing big data projects, SaaS applications and machine learning technology. With industry-leading platforms likeIBM Cloud Pak® for Data, organizations can modernize theirDataOpsprocesses while using best-in-class virtualization tools to achieve the speed and scalability their business needs now and in the future.

For more information on how your enterprise can build and execute an effective data integration strategy, explore the IBM suite ofdata integration offerings.

(Video) ETL (Extract, Transform, Load) Series: What is ETL?

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FAQs

What is Extract, Transform and Load explain? ›

ETL, which stands for extract, transform and load, is a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system.

What is the purpose of the Extract, Transform and Load ETL process? ›

Extract, transform, and load (ETL) is a data pipeline used to collect data from various sources. It then transforms the data according to business rules, and it loads the data into a destination data store.

What is ETL used for? ›

ETL stands for extract, transform, and load and is a traditionally accepted way for organizations to combine data from multiple systems into a single database, data store, data warehouse, or data lake.

What does transformation mean in ETL? ›

Transformation refers to the cleansing and aggregation that may need to happen to data to prepare it for analysis. Architecturally speaking, there are two ways to approach ETL transformation: Multistage data transformation – This is the classic extract, transform, load process.

What are the ETL components? ›

ETL project components includes Source, Transformation, Lookup, Staging, Destination, and Loader components.
  • Source components – deliver data for a transformation stream. ...
  • Transformation components, Lookup components, and Staging components – apply specific transformations to the data in the transformation stream.
24 May 2011

What are the steps of ETL process? ›

The 5 steps of the ETL process are: extract, clean, transform, load, and analyze. Of the 5, extract, transform, and load are the most important process steps.

What are the three common usage of ETL? ›

Major Use Case of ETL

Here are three of the main tasks ETLs can be used for: Data Integration. Data Warehousing. Data Migration.

What is extraction transformation and loading quizlet? ›

ETL (extraction, transformation, and loading) A process that extracts information from internal and external databases, transforms the information using a common set of enterprise definitions, and loads the information into a data warehouse.

What is ETL process example? ›

As The ETL definition suggests that ETL is nothing but Extract,Transform and loading of the data;This process needs to be used in data warehousing widely. The simple example of this is managing sales data in shopping mall.

What is an example of an ETL tool? ›

Cloud-Native ETL Tools

Many businesses use more than one cloud-native tool, as each includes connectors for different data sources and each has its own strengths and weaknesses. Examples include Segment, RudderStack, and Azure Data Factory.

Which ETL tool is used most? ›

Talend's ETL tool is the most popular open source ETL product. Open Studio generates Java code for ETL pipelines, rather than running pipeline configurations through an ETL engine. This approach gives it some performance advantages. Pentaho Data Integration (PDI).

What are types of data transformation? ›

Here, we have listed the top eight data transformation methods in alphabetical order.
  • 1| Aggregation. ...
  • 2| Attribute Construction. ...
  • 3| Discretisation. ...
  • 4| Generalisation. ...
  • 5| Integration. ...
  • 6| Manipulation. ...
  • 7| Normalisation. ...
  • 8| Smoothing.
22 Jan 2021

What is Data Transformation example? ›

Data transformation is the process of applying few or many changes (you decide!) to data to make it valuable to you. Some examples of the types of changes that may take place during data transformation are merging, aggregating, summarizing, filtering, enriching, splitting, joining, or removing duplicated data.

Why do we transform data? ›

Data is transformed to make it better-organized. Transformed data may be easier for both humans and computers to use. Properly formatted and validated data improves data quality and protects applications from potential landmines such as null values, unexpected duplicates, incorrect indexing, and incompatible formats.

How many ETL tools are there? ›

Types of ETL Tools. ETL tools can be grouped into four categories based on their infrastructure and supporting organization or vendor. These categories — enterprise-grade, open-source, cloud-based, and custom ETL tools — are defined below.

Why is ETL important? ›

ETL tools break down data silos and make it easy for your data scientists to access and analyze data, and turn it into business intelligence. In short, ETL tools are the first essential step in the data warehousing process that eventually lets you make more informed decisions in less time.

What is initial load in ETL? ›

In ETL, Initial Load refers to history tables and transaction tables that are loaded into these data flows. The performance of ETL processes can be vastly improved by setting properties such as load intervals and filters.

How do you run ETL? ›

Run the ETL Process
  1. If necessary, click the ETL Workspace tab to return to the Data Transforms web part.
  2. Click Run Now for the "Demographics >>> Patients (Females)" row to transfer the data to the Patients table. ...
  3. You will be taken to the ETL Job page, which provides updates on the status of the running job.

What is ETL in testing? ›

ETL — Extract/Transform/Load — is a process that extracts data from source systems, transforms the information into a consistent data type, then loads the data into a single depository. ETL testing refers to the process of validating, verifying, and qualifying data while preventing duplicate records and data loss.

What is offline extract transform and load? ›

What is ETL? 💡ETL is an acronym that stands for Extract, Transform, Load.💡 Essentially, it's the process your data has to go through before you an analyze it. First, you extract the source data from different platforms, then transform the data into a different format, and finally, load the data into a data warehouse.

What is ETL workflow? ›

ETL (Extract, Transform, Load) is an automated process which takes raw data, extracts the information required for analysis, transforms it into a format that can serve business needs, and loads it to a data warehouse.

What is data transformation? ›

Data transformation is the process of converting data from one format to another, typically from the format of a source system into the required format of a destination system. Data transformation is a component of most data integration and data management tasks, such as data wrangling and data warehousing.

Which is the common transformation type? ›

Translation is when we slide a figure in any direction. Reflection is when we flip a figure over a line. Rotation is when we rotate a figure a certain degree around a point.

Which ETL tool should I learn? ›

Hevo Data is an easy learning ETL tool which can be set in minutes. Hevo moves data in real-time once the users configure and connect both the data source and the destination warehouse. The tool involves neither coding nor pipeline maintenance. Hevo provides connectivity to numerous cloud-based and on-site assets.

Which is not a ETL tool? ›

D Visual Studio is not an ETL tool.

What is ETL quizlet? ›

ETL is the process of Extraction, Transforming and Loading.

What is a primary key quizlet? ›

Primary key. A field, or collection of fields, whose values uniquely identify each record in a table (different ID numbers) - used to identify each record because there can't be more than one.

Which partition is used to improve the performances of ETL transactions? ›

To improve the performances of ETL transactions, the session partition is used.

How do I load ETL data? ›

The ETL process is comprised of 3 steps that enable data integration from source to destination: data extraction, data transformation, and data loading.
  1. Step 1: Extraction. ...
  2. Step 2: Transformation. ...
  3. Step 3: Loading. ...
  4. Delivering a single point-of-view. ...
  5. Providing historical context. ...
  6. Improving efficiency and productivity.

What are ETL projects? ›

ETL stands for Extract-Transform-Load, it includes a set of procedures that include collecting data from various sources, transforming the data, and then storing it into a new single data warehouse, which is accessible to data analysts and data scientists to perform data science tasks, such as data visualization, ...

How do you create ETL? ›

Here are five things you should do when designing your ETL architecture:
  1. Understand your organizational requirements.
  2. Audit your data sources.
  3. Determine your approach to data extraction.
  4. Build your cleansing machinery.
  5. Manage the ETL process.

Is SQL Server an ETL tool? ›

The SQL Server ETL (Extraction, Transformation, and Loading) process is especially useful when there is no consistency in the data coming from the source systems. When faced with this predicament, you will want to standardize (validate/transform) all the data coming in first before loading it into a data warehouse.

Which ETL tool is faster? ›

Apache is one of the fastest and most secure marketing ETL tools available in the market today. Built on open source technology, Apache has been modified over time to provide seamless data integration and manipulation experience for its users.

Is ETL a technology? ›

ETL is a type of data integration that refers to the three steps (extract, transform, load) used to blend data from multiple sources. It's often used to build a data warehouse.

Is Excel an ETL tool? ›

In a lot of organizations, Excel is everywhere and Excel is everything. It is extremely important for modern ETL tool to work with Excel correctly. Of all Microsoft Office applications, Excel is one of the most important ones for all kind of businesses.

Which ETL tool is easiest? ›

Hevo – Recommended ETL Tool

Easy Implementation: Hevo can be set up and run in just a few minutes. Automatic Schema Detection and Mapping: Hevo's powerful algorithms can detect the schema of incoming data and replicate the same in the data warehouse without any manual intervention.

What is ETL data pipeline? ›

An ETL pipeline is a set of processes to extract data from one system, transform it, and load it into a target repository. ETL is an acronym for “Extract, Transform, and Load” and describes the three stages of the process.

What is extraction transformation and loading quizlet? ›

ETL (extraction, transformation, and loading) A process that extracts information from internal and external databases, transforms the information using a common set of enterprise definitions, and loads the information into a data warehouse.

How many ETL tools are there? ›

Types of ETL Tools. ETL tools can be grouped into four categories based on their infrastructure and supporting organization or vendor. These categories — enterprise-grade, open-source, cloud-based, and custom ETL tools — are defined below.

What is data mart with example? ›

A data mart is a subset of a data warehouse oriented to a specific business line. Data marts contain repositories of summarized data collected for analysis on a specific section or unit within an organization, for example, the sales department.

What you mean by ETL describe data extraction methods? ›

Data extraction is the first step in a data ingestion process called ETL — extract, transform, and load. The goal of ETL is to prepare data for analysis or business intelligence (BI). Suppose an organization wants to monitor its reputation in the marketplace.

What is ETL quizlet? ›

ETL is the process of Extraction, Transforming and Loading.

What is a primary key quizlet? ›

Primary key. A field, or collection of fields, whose values uniquely identify each record in a table (different ID numbers) - used to identify each record because there can't be more than one.

Which partition is used to improve the performances of ETL transactions? ›

To improve the performances of ETL transactions, the session partition is used.

Which software is used for ETL? ›

ETL Cloud Services. Amazon AWS, Google Cloud Platform and Microsoft Azure offer their own ETL capabilities as cloud services. If your data is already in one of these cloud platforms, there are a number of advantages to using their ETL services.

Which ETL tool is used most? ›

8 More Top ETL Tools to Consider
  • 1) Striim. Striim offers a real-time data integration platform for big data workloads. ...
  • 2) Matillion. Matillion is a cloud ETL platform that can integrate data with Redshift, Snowflake, BigQuery, and Azure Synapse. ...
  • 3) Pentaho. ...
  • 4) AWS Glue. ...
  • 5) Panoply. ...
  • 6) Alooma. ...
  • 7) Hevo Data. ...
  • 8) FlyData.

Is SQL Server an ETL tool? ›

The SQL Server ETL (Extraction, Transformation, and Loading) process is especially useful when there is no consistency in the data coming from the source systems. When faced with this predicament, you will want to standardize (validate/transform) all the data coming in first before loading it into a data warehouse.

What is the difference between OLTP and OLAP? ›

OLTP and OLAP: The two terms look similar but refer to different kinds of systems. Online transaction processing (OLTP) captures, stores, and processes data from transactions in real time. Online analytical processing (OLAP) uses complex queries to analyze aggregated historical data from OLTP systems.

What are types of data warehouse? ›

The three main types of data warehouses are enterprise data warehouse (EDW), operational data store (ODS), and data mart.

What is difference between data warehouse and data mart? ›

Size:a data mart is typically less than 100 GB; a data warehouse is typically larger than 100 GB and often a terabyte or more. > Range: a data mart is limited to a single focus for one line of business; a data warehouse is typically enterprise-wide and ranges across multiple areas.

What is ETL process example? ›

As The ETL definition suggests that ETL is nothing but Extract,Transform and loading of the data;This process needs to be used in data warehousing widely. The simple example of this is managing sales data in shopping mall.

What are the various types of data extraction in ETL? ›

Data in a warehouse can come from various places, and a data warehouse must use three different approaches to use it. Extraction, Transformation, and Loading are the terms for these procedures (ETL). Data extraction entails retrieving information from disorganized data sources.

What are the three common usage of ETL? ›

Major Use Case of ETL

Here are three of the main tasks ETLs can be used for: Data Integration. Data Warehousing. Data Migration.

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