Data extract, transform, load (ETL) is a process of copying data from one or more sources into a target system which is usually designed to represent the data differently from the source(s). ETL processes are used for data warehousing, data integration, and data migration projects (Figure 1).
Data extraction involves extracting data from homogeneous or heterogeneous sourcesData transformation methods often clean, aggregate, de-duplicate, and in other ways, transform the data into properly defined storage formats to be queried and analyzed.Data loading represents the insertion of data into the final target repository, such as an operational data store, a data mart, or a data warehouse.
ETL processes commonly integrate data from multiple applications (systems and sources), perhaps developed and supported by different vendors or hosted on separate computer hardware. The separate systems containing the original data frequently are managed and operated by different teams. For example, a cost accounting system may combine data from payroll, sales, and purchasing.
There is always a need for source-to-target data mappings before ETL processes are designed and developed. Logical data maps (usually prepared in spreadsheets) describe relationships between the starting points and the ending points of an ETL system.
Figure 1: Representative ETL process
Reasons for using ETL in Data Integration, Data Migration, Data Warehousing Projects
When used on an enterprise data warehouse DW project, the result provides deep historical and a current context of data for the organization.
By offering a consolidated view, the result of and ETL process makes it easier for business users to analyze and report on data relevant to their enterprises.
ETL tools and processes have evolved over many years to support new integration requirements for streaming data, big data (ex., social media, the Internet of Things (IoT), event logging), self-service data access, and more.
Differences Between ETL and ELT
ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) differ primarily in the sequence of data transformation and loading. ETL is traditionally preferred for handling smaller datasets or when complex transformations are necessary before loading the data into the target system.
ETL Process
Data extraction from source systems
Thorough data cleansing and deduplication before loading
Clean, transformed data loaded into target system
ELT Process
Data extraction from source systems
Raw data loaded directly into target database
Data transformed within the target system as needed
On the other hand, ELT is more suitable for larger datasets, particularly when the speed of loading is a priority. In ELT, raw data is loaded directly into the target database, such as a data lake or relational database, and transformed post-loading.
This approach can lead to faster initial data loads, allowing data engineers and data scientists to work on real-time data analysis while transforming data as needed for specific use cases.
ETL allows for thorough data cleansing and data deduplication before loading, which is ideal for ensuring accurate data analysis and sensitive data handling.
ELT typically leverages the power of modern databases or data warehouses to transform large amounts of structured and unstructured data after it has been stored. This method is particularly useful when working with streaming data or data pipelines that require real-time access.
Choosing between ETL and ELT depends on factors such as data size, transformation complexity, and the need for real-time processing.
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Data Quality Management in ETL Processes
Data quality management forms the cornerstone of successful ETL implementations. As organizations increasingly rely on data analytics for decision-making, maintaining high data quality throughout the extraction and transformation process becomes crucial. Data profiling, an essential first step, helps identify anomalies, patterns, and relationships within data streams before processing begins.
Effective data quality management encompasses several key components, including:
Data Validation Checks:
Verify data completeness and accuracy
Identify missing values and inconsistencies
Ensure data meets predefined business rules
Validate data types and formats
Quality Monitoring:
Implement continuous quality checks during data processing
Track data quality metrics across transformations
Monitor data streams for real-time quality assessment
Document quality issues and resolutions
Poor data quality can significantly impact business operations, leading to:
Flawed analytics and incorrect insights
Compromised decision-making processes
Reduced efficiency in customer relationship management
Increased operational costs
Modern ETL processes often incorporate data virtualization and advanced profiling techniques to enhance quality control. When consolidating data from multiple sources, organizations must establish robust validation frameworks that ensure data quality before loading into the target data warehouse. This approach helps maintain data integrity while enabling efficient data aggregation and analysis.
For cloud data warehouse implementations, automated quality checks become particularly important as data volumes grow. Organizations should establish clear data quality KPIs and regularly assess their data management practices to ensure optimal results from their ETL processes.
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Implementation of Data Quality Controls
The process of data extraction and transformation requires robust quality controls to ensure optimal outcomes. When organizations convert raw data from various sources, implementing comprehensive data management practices becomes essential. These practices help identify data quality issues before they impact downstream data analytics processes.
A Data Quality Implementation Framework:
Source Data Assessment
Analyze relevant data sources and their characteristics
Document expected data values and formats
Establish baseline quality metrics
Profile incoming data streams for anomalies
Transformation Quality Gates
Monitor data processing at key checkpoints
Validate data derivation results
Verify data splitting and aggregation accuracy
Ensure proper data based calculations
Target System Validation
Confirm successful loading into the target data warehouse
Verify data consistency across consolidated sources
Validate referential integrity
Monitor performance metrics
Organizations implementing these controls typically see improved data quality across their entire data processing pipeline. This systematic approach particularly benefits customer data management, where accurate information directly impacts customer relationship management effectiveness.
For cloud data warehouse implementations, automated quality controls become essential components of the ETL workflow. These controls help ensure that consolidating data from multiple sources maintains integrity while meeting business requirements. Regular monitoring and adjustment of these controls help organizations maintain high-quality data assets that support reliable analytics and decision-making processes.
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Steps for ETL Processes
ETL processes are composed of three separate but crucial functions often combined into a single programming tool that helps in preparing data and in the management of databases.
After the discovery and recording of source data, carefully designed ETL processes extract data from source systems, implement data quality tasks/consistency standards, conform data so that separate sources can be used together, and finally deliver data in a presentation-ready format so that application developers can build applications and end users can make decisions.
Discovering Source Data Some or all of the source systems may have been identified during project data-modeling sessions, but this cannot be taken for granted. Normally, only the key source systems are identified during the project data-modeling phase. It is up to the ETL team to drill down further into the data requirements to determine every source system, table, and attribute required in the ETL processes. Identifying the required data sources, or systems-of-record, for each element/table is a challenge that must be solved before moving to data extracts.
Extracting Source Data The data extract phases represent extractions from source systems to make all of it accessible for further processing. The main objective of this phase is to retrieve all the required data from the source systems with as little time and resources as possible.
Types of data extractions:
Full Extractions – many source systems cannot identify to users which data was changed since the last extraction. Therefore a full extraction of all data is necessary each time changed data is needed from those individual sources. A full extract requires maintaining a copy of the last extract in the same format to identify changes when a later extract becomes available. The ETL team is responsible for capturing data-content changes during the incremental loads after an initial load.
Update Extractions – when source systems can provide notifications that specific data has been changed and further identify each change, this is the easiest way to extract the data. Most data sources provide a mechanism to identify changes so that database replication can be supported.
Incremental Extractions – Some source systems are unable to provide notification that an update has occurred, but they can identify which records were modified, and provide an extract of only those records. During subsequent ETL steps, the system needs to identify changes and propagate them down. One of the drawbacks of incremental extraction is that it may not be possible to detect deleted records in source data.
Modern data processes often include real-time data – for example, web analytics data from a large e-commerce website. For this case, data cannot be extracted and transformed in large batches: the need arises to perform ETL on streaming data. This means that as client applications write data to the data source, the data should be treated, transformed, and saved immediately to the target data store.
Transforming Data Data extracted from sources is often the result of transactions and therefore not usable in target databases in that form. Much of such source data needs to be cleansed, deduplicated, aggregated, or otherwise transformed. This a key step where the ETL process adds value and changes data such that insightful application reports can be generated.
Data that does not require any transformation in an ETL process is referred to as direct move or pass through data.
During the transformation phase, ETL processes can perform customized operations on data. For instance, if the user wants sum-of-sales revenue, which is not in the database, it can be added. Alternatively, if the first name and the last name in a table are in different columns, it is possible to concatenate them before loading.
Transformation steps apply a set of rules to convert the data from the source to the target. This includes converting any measured data to the same dimension (i.e., conformed dimension) using the same units so that they can later be joined. Transformation steps also require joining data from several sources, generating aggregates, generating surrogate keys, sorting, deriving new calculated values, and applying advanced validation rules.
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Examples of ETL Data Transformations
ETL data transformations are the process of converting data from one format or structure into another format or structure. Data transformations can be simple or complex based on the required changes to the data between the source (initial) data and the target (final) data.
Data transformations typically are performed via a mixture of manual and automated steps. Tools and technologies used for data transformation can vary widely based on the format, structure, complexity, and volume of the data being transformed.
Following are some of the most common types of data transformations:
Aggregation – Data elements are aggregated from multiple data sources and databases
Cleaning – Examples include changing nulls to 0 or “male” to “M” and “female” to “F”
Data validation – Simple or complex data validation; for example, if the first three columns in a row are empty then reject the row from processing
De-duplication – Identifying and removing duplicate records or duplicate columns
Derivation – Applying business rules to data that derive new calculated values from existing data; for example, creating a revenue metric that subtracts taxes
Filtering – Selecting only specific rows and columns
Integration -Assign each unique data element one standard name with one standard definition.
Joining – Linking data from multiple sources – for example, adding advertising spending data from across multiple platforms, such as Google Adwords and Facebook ads
Key restructuring – Establishing key relationships across tables
Splitting -Splitting a single column into multiple columns
Summary – Summarize values to obtain total figures which are calculated and stored at multiple levels as business metrics – for example, adding up all purchases a customer has made to build a customer lifetime value (CLV) metric
Loading Data Stores
Two primary methods are used to load data into a warehouse:
Full loads – entire data load to targets that takes place the first time a data source is loaded into the warehouse
Incremental loads –loading data that has changed (“delta loads”) between source and target at regular intervals. The most recent extract date is maintained so that only records added after this date are loaded.
The ETL phase loads the data into the end target — often a data warehouse . This process varies widely from one organization to another. Some data warehouses may overwrite existing information with cumulative information; updating extracted data is can be done real-time, daily, weekly, or monthly.
Other data warehouses (or even other parts of the same data warehouse) may add new data in a historical form at regular intervals — for example, hourly. To understand this, consider a data warehouse that is required to maintain sales records of the last year. This data warehouse overwrites any data older than a year with newer data. However, the entry of data for any one year window is made historically. The timing and scope to replace or append data are strategic design choices dependent on the time available and the business needs. More complex systems can maintain a history and audit trail of all changes to the data loaded in the data warehouse.
ETL Development Tools
Designing and maintaining the ETL process is considered one of the most challenging and resource-intensive tasks on a data warehouse project. Many data warehousing projects use ETL tools to manage this process.
ETL is much easier and faster to use when compared to the traditional methods of moving data, since they require writing conventional computer programs. ETL tools contain graphical interfaces which speed up the process of mapping tables and columns between source and target databases.
ETL tools can collect, read, and migrate data from multiple data structures and across different platforms, like a mainframe, server, etc. Also, ETL technology can identify “delta” changes as they occur, which enables ETL tools to copy only changed data without the need to perform full data refreshes.
Moreover, ETL tools often include ready-to-use operations like filtering, reformatting, sorting, joining, merging, and aggregation; they support transformation scheduling, version control, monitoring, and unified metadata management.
ETL Tools Overview and Automation
In recent years, a wide range of ETL tools have emerged, each designed to simplify and automate the complex data integration process. Open-source ETL tools, such as Talend and Apache Nifi, provide cost-effective solutions for organizations looking to minimize overhead. These tools allow businesses to process large datasets efficiently using batch processing or streaming data techniques, based on their needs.
Additionally, ETL automation tools generate the necessary commands and structures to ensure ongoing maintenance of a data warehouse while adhering to strict data privacy standards like GDPR and HIPAA. Automation also improves accuracy, reducing the risk of human error when managing incoming data. For industries handling sensitive or large volumes of data, ETL tools provide essential features like data filtering, data mapping, and the ability to work with both structured and unstructured data.
Batch Processing: Ideal for processing large sets of data at once.
ETL Automation: Reduces manual intervention and ensures compliance with privacy standards.
Data Privacy: ETL tools help meet compliance with regulations such as GDPR and HIPAA.
By automating ETL processes, organizations can ensure accurate data analysis and enhance their business intelligence capabilities.
Conclusion
A common problem organizations face is how to gather data efficiently and effectively from multiple sources, in multiple formats, then copy that data to one or more data stores. The destination or target may not be the same type of data store as the source, the formats are often different, and data needs to be revised or cleaned before loading it into its final destination.
Various tools, services, and processes have been developed over the years to help address these challenges. No matter which process is used, there is a frequent need to coordinate the work and apply some level of data transformation within the data pipeline. ETL has been a successful solution for a majority of organizations.
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