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ETL: meaning, operation and benefits
In a data-driven world, companies need data to understand what is going on inside and outside their business, make informed decisions, and improve their operations and market presence.
Data is the necessary fuel for the engine of innovation that is increasingly closely linked to paths of digitization.
But having data is not enough.
What matters is their quality and the ability to know how to use them and derive value from them, all the more so in a context, such as the current one, in which data come from a disparity of different sources and a great heterogeneity of formats.
The first step that a data-driven reality must therefore take is to make them effectively usable for all business intelligence activities.
And that is where ETL comes in, a three-step process (extraction, transformation and loading) that makes data effectively available for analysis and processing activities.
Data is the necessary fuel for the engine of innovation that is increasingly closely linked to paths of digitization.
But having data is not enough.
What matters is their quality and the ability to know how to use them and derive value from them, all the more so in a context, such as the current one, in which data come from a disparity of different sources and a great heterogeneity of formats.
The first step that a data-driven reality must therefore take is to make them effectively usable for all business intelligence activities.
And that is where ETL comes in, a three-step process (extraction, transformation and loading) that makes data effectively available for analysis and processing activities.
What is ETL and what it is used for
ETL, literally Extract, Transform, Load, is a true data integration process that actually consolidates data from different source systems into a data warehouse, data lake or other target system with the purpose of improving data access.
An ETL pipeline collects data from one multiple source systems or databases, converts the extracted data into a single format or structure, and transfers it to the target database or centralized repository.
In summary, the ETL pipeline makes data ready for analysts and decision makers, saving developers time and reducing errors associated with manual processing of datasets.
It is a key process for data-driven companies, which can therefore
An ETL pipeline collects data from one multiple source systems or databases, converts the extracted data into a single format or structure, and transfers it to the target database or centralized repository.
In summary, the ETL pipeline makes data ready for analysts and decision makers, saving developers time and reducing errors associated with manual processing of datasets.
It is a key process for data-driven companies, which can therefore
- Centralize data across the organization, managing it in a unified location that enables better cross-functional collaboration.
- Standardize data that comes from multiple sources into different formats.
This is a critical step in order to then gain valuable insights from one’s information assets. - Perform massive data migrations-for example, when migrating to a new ERP-without loss or decrease in quality, eliminating the need to manually transfer data between systems or databases.
How the ETL process works
As mentioned earlier, an ETL pipeline consists of three phases:
- Extraction
- Transformation
- Loading
Let’s look at them in detail.
Extract / Extraction
In this first stage, data are collected from several databases or sources.
ETL tools extract or copy raw data from multiple sources and store them in a staging area, i.e., an intermediate storage area for temporary data storage.
The extraction phase can take place in different modes:
ETL tools extract or copy raw data from multiple sources and store them in a staging area, i.e., an intermediate storage area for temporary data storage.
The extraction phase can take place in different modes:
- Full extraction: in this case the entire data is extracted from the source and inserted into the data pipeline.
This is an approach that is used when the system cannot identify data changes and requires keeping a copy of the last extract in order to verify new and changed records.
Because this approach involves high data transfer volumes, it is recommended that it be used only for small tables. - Incremental extraction: in this case, each time an extraction process is performed, only new data and data that have changed since the last time are collected
- Source-driven extraction or on update notification: in this case, the original merge notifies the ETL system when the data changes, triggering the ETL pipeline to extract the new data.
Transform / Transformation
As data are collected from multiple sources, they can be in various formats, including semi-structured and unstructured data.
At this stage, the extracted data are converted or structured into the correct form desired.
This phase is critical for entering the collected data into the target database.
This phase involves a series of actions on the data sets.
At this stage, the extracted data are converted or structured into the correct form desired.
This phase is critical for entering the collected data into the target database.
This phase involves a series of actions on the data sets.
- It starts with basic cleaning, that is, converting the data into a suitable format.
Here possible errors can be removed, source data can be mapped to the target format, and data format revision actions can be performed, converting character sets, units of measure, and date and time values into a consistent format. - You can also merge or merge RDBMS or SQL tables or, where you want to summarize all rows within a group defined by one or more columns, apply aggregation functions such as mean, minimum, maximum, median, percentile, and sum.
- Similarly, where only a subset of data is needed, relevant data can be filtered out, discarding others.
- At this stage a check can be made on duplicate data to see whether the same records are duplicated in error or are legitimate and if so arrange for their deduplication, as well as a check can be made on possible errors that have occurred in manual data entry.
Typical cases may be typing errors that change a person’s age or place of residence.
In the latter case, that is, in the presence of address errors, geolocation APIs can be used to extract well-formatted location data and resolve the problem. - It is then possible to perform so-called “derivation” activities.
Here you apply business rules to the data to calculate new values from existing ones.
For example, you can convert revenue to profit by subtracting expenses. - Also at this stage, sensitive data can be protected to comply with data or privacy laws by adding encryption before the data are transmitted to the target database.
Load / Loading
At this stage, the now structured data are moved from the staging area and loaded into the target database or data warehouse.
However, this is a well-defined process that can be carried out in different ways.
However, this is a well-defined process that can be carried out in different ways.
- Full load: in this case, all data from the original source are transferred to the target data warehouse once the transformation phase is completed.
This is an action that is typically chosen the first time data is loaded from a source system to the data warehouse. - Incremental loading: just as in the extraction phase, the ETL tool stores the date of the last extraction, so that only records added after that date are loaded, calculating the difference between the target and source systems.
In the case of small volumes of data, this can be done in a streaming mode, that is, continuously.
This is a useful mode where it is important to monitor and process data streams to make more timely decisions.
In the case of large volumes of data.
A time interval is set and changes are collected and transferred in batches.
Benefits of ETL
As we have already mentioned, the ETL pipeline standardizes and automates the whole process of collecting raw data from multiple data sources, such as CRM, ERP, social media platforms, and so on, in different formats, including CSV, JSON, XML, and text files.
But let’s look in detail at the purposes for which this process is important.
But let’s look in detail at the purposes for which this process is important.
- Convert data to a common format. This is the first and most obvious benefit of implementing an ETL pipeline in a scenario where a data warehouse with data from multiple sources and in different formats is needed.
An ETL pipeline reduces the time, errors and consequently the costs associated with data management. - Unified view on data. You can use ETL and enable the combination and analysis of data from different sources to gain a more complete view of what is happening in the company.
- Automation ETL streamlines the repetitive tasks of migrating, moving, processing data by introducing greater efficiency into the entire process.
ETL takes the concepts of data utility, availability, consistency, integrity and security to a different level.
By creating a level of abstraction between the source and target systems, ETL can contribute to data governance while preserving data security and quality.
In fact, ETL promotes data democracy by increasing its accessibility and availability to all stakeholders without sacrificing the necessary levels of security.- Scalability As the volume and complexity of data increases, the importance of ETL becomes more apparent.
ETL pipelines can be scaled to ensure that the enterprise can continue to extract, transform and load large volumes of data. - Data Access In business intelligence strategies, timely access to integrated data is critical to support informed decision making.
With ETL pipelines, the data are already in a usable format and thus enable reports to be produced significantly faster. - Consistency ETL can help identify and correct errors, inconsistencies and other problems through data cleaning and transformation processes.
By improving the quality, reliability, accuracy and trustworthiness of data, it also improves decision making as a result - Reducing errors Manual data management brings with it quite a few error risks that can also significantly affect its reading and analysis.
ETL automates many stages of data lifecycle management, reducing errors and enabling IT organizations to work on quality data.
Evolution of ETL
It must be said that ETL practices, which originated at a different time in the history of enterprise computing, have undergone significant changes over the years and even more so with the growth of cloud adoption.
While traditional ETL started with manual processes that have been replaced over time by increasing automation, the advent of cloud computing has had a significant impact on ETL practices, enabling organizations to execute the pipeline in a more scalable and cost-effective manner.
Cloud ETL enables organizations to store and process large volumes of data in the cloud, without the need for on-premise hardware or software, rapidly increasing or decreasing resources as needed and maintaining those levels of security and compliance necessary to meet regulatory requirements.
And with the increasing prevalence of the cloud, there is a tendency to think of virtualization and ETL as being the same thing.
Both enable data access, integration and distribution, but they apply to different usage scenarios.
ETL is useful for physical data consolidation projects that involve duplicating data from the original data sources into a data warehouse or new database.
It is recommended for applications that perform data mining or historical analysis to support long-term strategic planning, but less so for applications that support operational decisions, which require more timeliness.
Data virtualization, on the other hand, abstracts, federates and publishes a wide variety of data sources.
The application queries the relevant data, performs the necessary joins and transformations, and delivers the results to users, without the latter being aware of the actual location of the data or the mechanisms needed to access and join it.
While traditional ETL started with manual processes that have been replaced over time by increasing automation, the advent of cloud computing has had a significant impact on ETL practices, enabling organizations to execute the pipeline in a more scalable and cost-effective manner.
Cloud ETL enables organizations to store and process large volumes of data in the cloud, without the need for on-premise hardware or software, rapidly increasing or decreasing resources as needed and maintaining those levels of security and compliance necessary to meet regulatory requirements.
And with the increasing prevalence of the cloud, there is a tendency to think of virtualization and ETL as being the same thing.
Both enable data access, integration and distribution, but they apply to different usage scenarios.
ETL is useful for physical data consolidation projects that involve duplicating data from the original data sources into a data warehouse or new database.
It is recommended for applications that perform data mining or historical analysis to support long-term strategic planning, but less so for applications that support operational decisions, which require more timeliness.
Data virtualization, on the other hand, abstracts, federates and publishes a wide variety of data sources.
The application queries the relevant data, performs the necessary joins and transformations, and delivers the results to users, without the latter being aware of the actual location of the data or the mechanisms needed to access and join it.
Differences between ETL and ELT
In this process of ETL transformation, again driven by the spread of next-generation cloud data warehouses, we are beginning to talk about a new declination of ETL: ELT, Extract, Load, Transform).
What is ELT
As the acronym implies, ELT defines a data integration process that starts with transferring raw data from a source server to a data warehouse or data lake to a destination server and then preparing the information for use.
In this case, the pipeline starts with the extraction of data from one or more source systems.
In the second stage, the extracted data is loaded into the target database.
Finally, the data are transformed, that is, converted from the source format to the format required for analysis.
It is not easy to say which of the two approaches is preferable.
Basically, with ETL, the raw data are not available in the data warehouse because they are transformed before being loaded, whereas with ELT, the raw data are loaded into the data warehouse and the transformations occur on the stored data.
Generally, ELT is considered to be more useful for processing large data sets needed for business intelligence (BI) and big data analysis.
ETL is considered a preferable approach to ELT when extensive data cleansing is required before loading it onto the target system, when many complex calculations are required on numeric data, and when all source data comes from relational systems.
In this case, the pipeline starts with the extraction of data from one or more source systems.
In the second stage, the extracted data is loaded into the target database.
Finally, the data are transformed, that is, converted from the source format to the format required for analysis.
It is not easy to say which of the two approaches is preferable.
Basically, with ETL, the raw data are not available in the data warehouse because they are transformed before being loaded, whereas with ELT, the raw data are loaded into the data warehouse and the transformations occur on the stored data.
Generally, ELT is considered to be more useful for processing large data sets needed for business intelligence (BI) and big data analysis.
ETL is considered a preferable approach to ELT when extensive data cleansing is required before loading it onto the target system, when many complex calculations are required on numeric data, and when all source data comes from relational systems.
What to look for in an ETL tool
When evaluating the ETL that best suits your organization, there are a number of factors to consider.
It starts, of course, with functionality and features, with a focus on the ability to connect data from different sources, support for various data formats and the data transformation capabilities themselves.
You need to understand what features the solution offers in terms of data profiling and validation and the ability to handle complex workflows.
Scalability is another discriminating element: the tool must support large data sets and be able to handle increasing volumes of data, provide parallel processing and data loading optimization solutions.
Similarly, ease of use is not to be neglected, just as it is necessary to ensure that the tool integrates with existing infrastructure, databases, cloud platforms and analysis tools.
Compatibility with a wide range of data sources is essential for complete data consolidation.
Last come cost and licensing considerations, as well as the ability of the partner assisting the company in evaluation and implementation to offer support and assistance throughout the process.
It starts, of course, with functionality and features, with a focus on the ability to connect data from different sources, support for various data formats and the data transformation capabilities themselves.
You need to understand what features the solution offers in terms of data profiling and validation and the ability to handle complex workflows.
Scalability is another discriminating element: the tool must support large data sets and be able to handle increasing volumes of data, provide parallel processing and data loading optimization solutions.
Similarly, ease of use is not to be neglected, just as it is necessary to ensure that the tool integrates with existing infrastructure, databases, cloud platforms and analysis tools.
Compatibility with a wide range of data sources is essential for complete data consolidation.
Last come cost and licensing considerations, as well as the ability of the partner assisting the company in evaluation and implementation to offer support and assistance throughout the process.
