ETL (extract, transform, load) is defined as a data integration solution that combines data from several sources to create one consistent data repository, which can then be loaded into a storage system such as a data warehouse. This article covers the meaning, tools, and testing process of ETL.
Table of Contents
What Is ETL?
ETL (extract, transform, load) is a data integration solution that combines information from several sources to create one consistent data repository, which can then be loaded into a storage system such as a data warehouse.
Overview of ETL Process
As centralized data repositories and data warehouses increased in popularity just before the turn of the millennium, companies developed specialized tools for loading data into them. They introduced the first ETL solutions in the 1970s.
Before the advent of cloud computing, data was typically stored and transformed in on-premise data repositories. Since then, ETL has served as data integration and loading process for computation and analysis. With time, it has become the primary data processing methodology for data warehousing.
Today, ETL serves as the foundation for data analytics processes and machine learning (ML). This solution leverages preset business rules to cleanse and organize data to address business intelligence requirements such as monthly reporting. Additionally, cutting-edge ETL solutions can carry out advanced analytics to enhance end-user experiences and back-end workflows.
Business intelligence operations can fall apart due to invalid or inaccurate data, primarily because such information can lead to harmful business decisions. For instance, faulty data analytics can lead to poor decision-making regarding customer experiences, such as attempting to convert leads at the wrong funnel stage. Inaccurate data storage and processing can also lead to compliance issues.
ETL addresses these business challenges by enabling seamless data integration. At its core, ETL works by ‘extracting’ data from isolated or legacy systems, ‘transforming’ the data to cleanse it, improve its quality, establish consistency, and make it compatible with the storage destination. It then ‘loads’ it into the target data store.
ETL processes are famous for making higher data volumes available through business intelligence solutions. More significant amounts of enterprise data being made available from a higher number of data stores lead to the generation of more comprehensive informational overviews for business applications. ETL is usually a long-term operation better suited for processing smaller data volumes over a more extended period than large databases in a single go.
ETL tools boost data quality and help facilitate more in-depth analytics. After being subjected to the transformation procedure of ETL, data ends up cleaner, more accurate, and more useful for business intelligence and other enterprise applications.
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ETL Process
Business intelligence and software development personnel rely on ETL to set up IT processes to access data-driven insights from disparate sources.
Once the ETL testing process is completed, data from numerous databases is made available in one location–such as a data warehouse–for the programmatic analysis and discovery of business insights by humans or machines.
ETL centralizes information storage, giving analysts improved data access while minimizing data silos. But how do these data integration solutions achieve this feat?
Let’s take an in-depth look at the ETL process:
1. Extracting data
Enterprises rely on data from numerous sources for producing business intelligence and training machine learning models. This data is often subject to various analysis methodologies and complicated strategies. As such, it must be capable of traveling without obstruction among enterprise systems and applications.
The first step toward seamless data transfer is an extraction from the different data sources. These sources may include legacy data warehouses, siloed databases, cloud or hybrid environments, on-premise servers, mobile devices, analytics tools, or similar systems.
For instance, an enterprise may use ETL to extract data from transactional applications such as enterprise resource planning (ERP) platforms, customer relationship management (CRM) programs, or an Internet of Things (IoT) setup for collecting data from factory floors or production lines.
Once volumes of structured and unstructured data are extracted from these sources, they are integrated into a single repository and validated automatically, with invalid data being either highlighted or removed. Data engineers can undertake this process manually, too; however, this might lead to human errors and is also a time-intensive endeavor. ETL tools build a more reliable and efficient workflow by automating the extraction process.
The extracted data can exist in numerous formats, including NoSQL, relational databases, flat files, JSON, and XML. Therefore, they are first moved into the staging area for transformation. Not using a staging area and transferring data from disparate sources directly into the data warehouse can corrupt all the data. While ETL solutions typically offer a rollback feature, executing it is often a longer and more tedious process than simply ensuring effective transformation in the first round.
2. Transforming data
Once the data extraction is complete, it is time to transform it. The ETL solution applies a set of functions or rules to the extracted data intending to convert it into one standard format and prepare it for deployment.
This process generally involves some combination of the following steps:
- Filtering, where only specific attributes are loaded into the data warehouse.
- Cleansing, where null values are replaced with specified default values and predefined terms are replaced with preset values. This step resolves missing values and inconsistencies in the data. For instance, the United States, the U.S.A., the US, and America are all replaced with ‘USA’ during this step.
- Standardizing, where formatting rules are used to make the dataset more consistent.
- Joining, where multiple attributes are integrated to create one value.
- Splitting, where a single attribute is separated into multiple attributes.
- Deduplication, where redundant data is either flagged or deleted.
- Verification, where unusable information is deleted, and any detected anomalies are highlighted.
- Sorting, where tuples are sorted based on a key attribute or other attributes. In this step, data is classified based on type.
- Mapping, where data elements from multiple data models are combined.
- Augmenting, where data is pulled in from various sources.
- Custom rules can also be set up for enhancing data quality, ensuring accessibility, and meeting reporting requirements, among other business needs.
Transformation is the most critical step in the ETL process, as its primary goal is the improvement of data integrity. In this step, data is deduplicated, well-formatted, made completely compatible with its new destination and prepared for loading.
3. Loading data
The final task in the ETL process is the loading of data. At this stage, the data is transformed and ready to be loaded into the destination data store. The frequency of loading can differ based on the use case of the enterprise; loading can either occur at frequent intervals or be done after long, predetermined gaps.
Completing the loading stage leads to the delivery of secured data for sharing across internal and external teams with access to the destination database. Loading may also entail the deletion of the existing data at the destination.
Some ETL solutions use a loading pipeline, wherein a part of the data is extracted and sent for transformation. While that data is being transformed, one can extract new data, and when the transformed data is sent for loading, the next batch of extracted data is sent for transformation.
When all the transformed data is loaded simultaneously, it is known as a full load. Conversely, it is known as incremental loading when data is transformed and loaded at scheduled intervals.
During full loading, all transformed data is used to create a new and unique record in the destination data repository. Full loading is typically used for producing new databases or for research applications. Full loading of data leads to the frequent and exponential growth of datasets, which can result in databases that are difficult to maintain.
On the other hand, incremental loading is a less comprehensive approach to loading. This method makes databases easier to manage by comparing incoming transformed data with already created records. Additional records are only created if new unique data is loaded after transformation. Otherwise, the record remains unchanged after the loading is completed. This method is ideal for data warehouses that are limited in size, as it is less expensive to manage and maintain.
What is the ELT process?
ELT (extract, load, transform) is a variation of ETL. The data is extracted, loaded, and then transformed in this process. Businesses follow this sequence to load raw data onto a platform that can make changes to it. Generally, cloud-based databases can undertake the processing of data at scale.
ELT is useful for advanced analytics applications. For instance, storing data in a raw or partially processed form is helpful for research applications. Data scientists use ELT to upload data into a data lake, where the data is combined with information from disparate sources or leveraged for training predictive models. Modern data processing platforms allow quicker data transformation and minimize unrequired data movement in ELT.
The primary distinction between ELT and ETL is the order of data operations. ELT extracts data from the defined source and loads it directly into the destination data store without first passing it through the staging area for transformation. This allows for the raw data to be transformed post-loading as required.
ETL and ELT can integrate numerous data repositories; however, they have different applications, benefits, and challenges. ELT is the preferred data integration process for large, unstructured datasets that one must load straight from the source. It is also ideal for managing big data, as it does not require a lot of upfront planning for the extraction and storage of data.
Conversely, users must put more effort into defining the process before executing an ETL operation. For instance, identifying specific data points for extraction must be completed before the ETL process commences. Users must also construct the business rules for data transformations before ETL can start extraction.
ELT is more flexible, making it apt for usage with cloud databases. However, being a more recently established process means its best practices and applications are yet to be fully established.
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Top 3 ETL Tools
ETL tools are software applications created for carrying out the ETL process described above. Businesses leverage ETL tools to simplify enterprise data management and enhance data quality.
Let’s take a look at the top 3 ETL tools in 2022:
1. Azure Data Factory
Azure Data Factory is a serverless ETL tool that is fully managed and intuitive to use. It can connect all software platforms through 90+ connectors, including AWS, MongoDB, DB2, MySQL, Oracle, Sybase, SQL, SAP, and Salesforce. It allows users to enhance the overall efficiency of business processes through autonomous ETL that provides improved insights.
An essential application of Azure Data Factory is reposting SQL server integrations. It also offers pay-as-you-go pricing models that are more economically efficient for data management and workflow connection applications. Additionally, no-code options minimize the need for developer involvement and additional IT support, especially when getting started.
Azure Data Factory automatically scales storage and processing for fulfilling data integration requirements. Useful for both business and personal applications, it features a no-code GUI and a code-based IT interface. Enterprises primarily using a Microsoft environment will significantly benefit from implementing Azure Data Factory.Â
The standard technical support package for Azure Data Factory features round-the-clock access to support engineers over the phone and email. The support SLA is guaranteed for one hour. An additional advantage of Azure is the free online training available directly from Microsoft.
2. AWS Glue
Amazon Web Services Glue is an ETL service created to provide enterprises with comprehensive data overhaul functionality. This cutting-edge solution can comb through business data and transform it while preparing and cataloging data sets.Â
Described as ‘serverless ETL’ by AWS, Glue can be deployed within a few minutes. It offers a code-based option, and real-time data flows. This batch-oriented solution features an ETL pipeline that covers all data transformation and sharing angles, making it ideal for CRM applications.
Businesses that use AWS processors and repositories, Amazon S3 storage services, and SQL databases will find AWS Glue an ideal ETL solution. It integrates seamlessly with other AWS processes and systems and offers APIs for third-party databases that are JDBC (JAVA) accessible, including MySQL, DB2, Sybase, Oracle, MongoDB, and Apache Kafka.
AWS Glue is capable of cleaning, validating, organizing, and loading data from numerous sources– static and streaming. Processing semi-structured data such as process logs and clickstreams (such as website hyperlinks) is also a key feature of AWS Glue. Its compatibility with SQL makes it popular, while job execution in Glue relies on either Python or Scala.
Scheduling ETL tasks based on an event or schedule and triggering tasks once the data becomes available are possible with Glue. The on-demand functionality of this tool allows for automatic scaling and accommodation for the required storage and processing resources. Users can also view runtime metrics while processes are active.
Billing for AWS Glue uses an hourly usage-based model, and one can make monthly payments. The first million objects and accesses stored are free. Like Azure, AWS provides access to complimentary online courses and certification programs.
3. Google Cloud Dataflow
Google Cloud Dataflow is a comprehensive, fully managed ETL tool. It is ideal for users seeking a complete solution through ApacheBeam Pipelines operating in the Google Cloud. Real-time insights and large-scale data processing are key features of Google Cloud Dataflow, as are auto-scaling, minimized latency, and programmability.Â
Google Dataflow is serverless and allows for the implementation of a streaming data pipeline within minutes. The other advantages of this tool are nearly limitless capacity, batch and real-time processing, real-time on-demand and scheduled modes, and scalable cost management through resource auto-scaling.
Google Cloud Dataflow can seamlessly integrate with the full suite of Google services. Proficiency in SQL databases, Java, and Python is a must for using this tool. Its cloud-based architecture allows Google Cloud Dataflow to scale automatically and accommodate any ETL job’s storage and processing requirements.
Technical support options start from Basic, which covers billing and payment support, and move up to Standard (unlimited technical support), Enhanced (swifter response times), and Premium (support from a dedicated representative). Pricing depends on usage.
Besides a free online tutorial on its Cloud Academy, Google offers paid in-depth training and certification programs for Dataflow.
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ETL Use Cases
The data strategies of today are more complicated than ever before. Digitalization and the prevalence of anything/everything as a service (XaaS) solutions give organizations access to a wide range of data sources. ETL is used to transform these large volumes of data into business intelligence that is accurate and actionable.
Before we explore ETL’s specific industrial use cases, let’s see what features of this solution make it ideal for data analytics and management, business intelligence, and machine learning.
- Data consolidation: The manual management of large amounts of data is a resource-intensive task and can lead to inefficiencies in digitalized business environments where every moment matters. ETL consolidates numerous data forms to provide a unified view that allows for more accessible aggregation, analysis, visualization, and understanding of big datasets.
- Long-term overview: ETL combines legacy enterprise data with information collected using the latest applications. This allows for a long-term overview of business data that includes both older datasets and more current information.
- Enhanced efficiency: ETL removes the need for manual data migration by automating the process. This frees development and technology teams to focus on innovation, as they do not have to spend time writing code for moving and formatting data.
Fundamentally, industries rely on ETL to take in data swiftly and reliably and prepare it for use by data scientistsOpens a new window and analysts. The creation of high-quality models via ETL is helpful in numerous verticals, including:
1. Automotive
Car manufacturers and dealerships leverage ETL solutions for deciphering sales patterns, fine-tuning marketing campaigns, restocking inventory, and connecting with leads.
2. Finance
Financial institutions gather high volumes of structured and unstructured data that are then used to gain insights into customer behavior. ETL is used to process the collected information and generates insights that help in risk analysis, improving online platforms, and optimizing financial services.
3. Governance
Governments worldwide are embracing technology such as 5G and IoT to create smart cities. The enormous quantities of data generated by these smart cities are processed using ETL and artificial intelligence (AI) for applications such as traffic optimization and water quality monitoring.
4. Healthcare and life sciences
The healthcare industry relies on ETL to manage patient records, collect insurance information, and adhere to regulatory obligations. Additionally, clinical laboratories leverage ETL augmented by AI for processing data generated during research. For instance, developing a new vaccine calls for collecting, processing, and analyzing large volumes of data.
5. Telecom
Telecommunications is one of the most data-intensive industries today, making it an ideal use case for ETL. Telecom providers use ETL for managing collected data and gaining insights from it. After processing and analyzing this data, the insights are used to enhance marketing, sales, customer satisfaction and ultimately, profitability.
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Takeaway
ETL is an ideal solution for enterprises to create efficient data pipelining and governance processes and to monitor and manage these processes. ETL is used for numerous cloud and big data applications across industries. This integration solution is expected to see increasing use in the AI and ML domains as well.
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