ETL: much more than a connector
ETL is a process that integrates the different types of data in a company. It is a key tool for digital business transformation.
Extraction, Transformation and Loading is the process of integrating data from multiple applications (systems), converting it into a single format or structure, and then loading the data into the destination, often a data warehouse.
This process is essential for data analysis, business intelligence, but also for automation, error elimination and overall efficiency gains.
While it is considered an indispensable tool in companies with a wide range of data sources and formats, small businesses today also take advantage of its benefits.
Choosing the right tool to do so is important to ensure the success not only of the specific action, but also of the overall business objectives and efforts.
Connect your business management software to your Online Store
Conecta HUB is the ETL of Conecta Software. It communicates with the management solution you use to invoice today, and creates and maintains the products, prices, stock, offers, etc. of your Online Store.
ETL and new technologies
This tool gained popularity in the 1970s due to the growing need to store different types of business information. With the advent of data warehouses in the 1980s and 1990s, its popularity continued to grow. Since then, technologies have evolved and today provide levels of knowledge and intelligence not previously thought possible.
In this time, the nature of ETL has changed. There is a growing list of options. The pressure of digital transformation and the competitive and agile environment has driven greater data quality and value. This is due to the application of artificial intelligence and machine learning (machine learning) to the data. Therefore, ETL is an important part of any data architecture.
ETL and Artificial Intelligence in the Digital Transformation
ETL has become more complex and necessary in a world of disparate data sources, complex data mergers, and a diversity of data and use case based applications.
What is an ETL?
ETL stands for Extract, Transform and Load, and refers to the process of transferring data from one location to another. In addition to migrating data from one database to another, it also converts (transforms) databases into a single format that can be used at the final destination.
Collect data from multiple sources. At this point, the data is often from multiple and different types of sources
Extract is the process of collecting data from all required data sources. Data sources come in many shapes and sizes, from RDBMS systems to file-sharing APIs or from public to private sources or from paid to free data sources.
Data sources can
- contain personally identifiable information or company intellectual property
- be unordered, unstructured or structured and well described
- generate data at varied frequencies or produce data constantly through data streams
- support pull data mechanisms or push data mechanisms in a synchronous or asynchronous manner
This means that the extracted part of the ETL tool must be extremely flexible, robust and malleable to withstand the diversity of data sources and variations in data extraction procedures and protocols.
Data architectures must be able to connect to multiple data sources in parallel and extract data so that it is available for further processing without affecting the recoverability of other extraction processes.
Examples of data sources
Convert the recently extracted data into the correct form so that it can be placed in another database. This process is crucial to ensure that data from one database or application can be used by other applications and databases.
Some key functions are:
- Standardization to a consistent set of search values
- Cleaning by validity checks to remove or change problematic data
- Transposition, usually by denormalization and reorganization in a dimensional model to optimize reporting
- Creation of substitute keys that are new values applied to similar data from different source systems
The first type of transformation process is the determination and qualification of various data as being of high quality, complete and acceptable. In this case, the system must ensure that the various data points are complete, adhere to the expected schema, and do not contain data that is unreadable or damaged and inconsistent. Another type of data quality check uses past data patterns associated with a data set to determine if there have been unexpected changes in the data just received compared to past arrivals. If such changes are noted, the quality of the data may be flagged as suspect.
The second type of transformation process ensures that the data are considered adequate according to the business quality requirements of the intended data analysis. Here, the data is inspected and analyzed for completeness from a business-relevant perspective, and if key elements that are needed to drive business workflows are found to be missing, the data is flagged as suspect.
The third type of transformation process ensures that the data is processed to take the form required by the business purpose of the data analysis. Here, data can be aggregated, grouped, filtered, sampled, processed through algorithms to produce a transformed data set that is ready to support the intended business case.
Because the same data can be used for multiple business cases, transformations are often one-to-many in nature, and one data set is transformed several times across multiple business logics to produce multiple transformed data sets.
When data is written to the target database or data warehouse
This process is an integral part of business intelligence because it means that data from multiple sources can be brought together in a way that provides important information; regardless of the original format or location. To succeed in this effort, one begins with data mapping, where the relationship between the source establishes instructions on how the data should be transformed before it reaches the designated location.
ETL loading has undergone significant changes in focus, especially with the advent of multi-lingual storage, where storage is designed to better leverage the specific data scenario, whether it be analysis, search, alerts, visibility, etc. In data architectures, the same data can be loaded, in parallel, into multiple different types of storage technologies to drive end-user and customer applications as needed and as required by the business.
In loading architectures, it is important that the system can transmit and load data simultaneously in multiple technology stacks, without damaging or affecting the strength and quality of other parallel loads.
How an ETL improves business processes
In the digital age, we take immediate access to data for granted. To turn data into information, and that is informed decision making, we need tools. An ETL provides a deep historical context that allows companies to make better decisions without incomplete information. In addition, it provides the following:
- The ability to digest large amounts of data from multiple sources, especially in large quantities and when there are complex rules and transformations
- Improved accuracy for reports and audits
- Improved productivity of data professionals due to the reuse and coding of processes needed to move data, without the need to write more code for each task
- Operational resilience due to built-in error handling functionality in tools
- Clearer visual flow and self documentation because the tools are based on the Graphical User Interface (GUI)
- High return on investment due to cost savings, with some studies showing a 5-year return on investment of 112% when combined with the right data storage solution
- A basis for emerging integration requirements for data transmission and the like
Does my business need an ETL?
If you need a data warehouse that combines data from various sources, you will need a way to get that data to the warehouse. This data can provide you with a consolidated view of many different processes and ultimately drive better decision making.
In the following cases, ETL will be particularly useful when
- there are multiple source systems that need to be integrated
- these source systems are in different formats
- the process must be repeated, either daily, hourly or at another specific frequency
- you need to make the best use of the prefabricated warehouses
A wholesaler that opens up ecommerce as a new sales channel. An ETL will synchronize your ERP with your online store, allowing you to integrate this sales channel as one more.
A clothing brand that is distributed by several different retailers with several different platforms and systems. All data arrives in different formats and is then made consistent and usable for BI tasks through an ETL tool.
A global logistics company that processes hundreds of thousands of shipments with data from almost every source imaginable; making clear visibility almost impossible. An ETL solution eliminates redundancy and inconsistencies in data, leading to improved data quality.
A financial service provider who is transferring to a solution ERP due to the overhead costs associated with the existing agreement. But all your existing data must be successfully migrated to the new system, regardless of where or how it is started. ETL tools can cleanse and store data more efficiently than any other method.
Choosing an ETL solution
ETL tools have evolved over the years to include a wide range of capabilities and configurations. Many come in cloud-based versions, providing a greater degree of scalability, availability and security, with lower infrastructure costs.
There are some criteria that can help evaluate potential ETL tools. It is important to decide which of these will be the most essential for business needs. However, in general terms, some of the most important factors to consider include
Depending on the needs of the business, the importance of certain features over others will vary. Everyday tasks, such as data conversion, record merging, filtering, grouping and merging data, must be included with any tool. Some come with the ability to perform more advanced tasks, such as web methods, index reconstruction, matrix management, and unstructured data processing.
Any ETL solution must be able to connect to Excel, SharePoint, FIX, Salesforce, Hadoop, FTP and others. Without this functionality, the processing power of the tool is irrelevant as it will not be usable. However, it should be noted that all tools can be connected to a database / RDBMS, but only some have native client drivers that allow higher performance compared to ODBC.
FLOW OF WORK
The key is to be able to create effective workflows to organize and connect all these tasks. Some of the most important workflows to establish include
- restriction (criteria)
- grouping and
- loop (repeat)
Being able to understand how an ETL package is executed is essential: this includes how long it takes, when it started (and ended), who started the progress, whether it was successful or not, and in case of failure, what error message was received. Running also includes the ability to run at predetermined times, restart in the event of an error, and limit the duration of the run.
Again, this is where the needs of the business will greatly affect the decision. For those who need more capacity, many ETL tools include features such as mass loading or search table caching, to name a few.
This can mean anything from being able to configure packages to run at the same time, to setting the alert frequency, and creating different users and setting their permissions.
The value of each of these criteria to each other will vary depending on the size of the business, the objectives for the data and other similar factors.
BY ETL TOOL CATEGORIES
Another way to choose which ETL tool best fits a need can be through four distinct categories, each with its own specific characteristics:
- Tools Open Source ETL. As their name suggests, they are open source and free to use, making it possible for smaller businesses to be within their reach. Since they are tools designed through a general approach, customization is often necessary to adapt them to more specific objectives, which requires specialized consulting.
- Tools ETL Cloud. The cloud offers certain advantages such as high flexibility and pay-per-use when choosing ETL tools offered as a service.
- Tools ETL Enterprise. These tools refer to proprietary products, which offer a large number of built-in functionalities and support for connection to a large number of data sources. In addition, these types of ETLs are preferred by large companies as they can cope with payments due to their high cost.
- Tools Custom ETL. These types of ETLs are tailor-made and specific developments for a particular company or even for a particular project.
Importance of knowing how to choose an ETL
Choosing an ETL that fits a company's needs is not easy. If your ERP has its own solution, however limited it may be, there is a danger of not even getting the chance to choose. Hence the importance and advantage of standardised solutions rather than tailor-made tools.
According to a study conducted by Brain & CompanyWith the new "Smarter" system, companies with optimized analytical processes can make decisions five times faster than their competitors.
It is clear that knowing how to choose an ETL tool is a complex decision, due to the future impact it will have on the company. However, the correct choice can benefit companies in the following aspects:
- Decision-making takes less time and is more accurate.
- It provides automated support to data analysts.
- It offers speed in data processing.
- It offers the possibility of obtaining data from any type of source.
- It offers the possibility of filtering only those data relevant for analysis.
The main suppliers of ETL
Leading technology companies have developed tools with incredible functionality to meet the needs of a wide range of organizations. However, a number of growing technology companies are beginning to offer even more features and capabilities for specialized needs and requirements.
- Oracle Data Integrator (ODI)
- SAP Business Objects Data Services (BODS)
- Microsoft SQL Server Integration Services (SSIS)
SAS Data Management
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