To develop new strategies for analyzing your organization's data, learn from the following ETL examples.
ETL is a three-step process that extracts data from various sources, transforms the data to fit the target system's schema, and loads the transformed data into the destination system.
By understanding ETL examples, you can explore different types of data integration solutions that could help improve your business processes.
In this guide, we'll explore several examples of ETL and discuss how you can utilize ETL tools to manage your business data more effectively.
While ETL is a typical process used by many businesses, the specific pipelines used can vary depending on the industry and the type of data sets being processed.
In this section, we'll explore examples of ETL pipelines across different industries to provide a more comprehensive understanding of how ETL is used to automate workflow.
Many tools are available to help businesses extract, transform, and load their data.
Each tool facilitates the ETL process and can be used for data management in various industries.
ETL is an indispensable process for various industries, helping to collect, cleanse, and convert data into formats suitable for analysis, reporting, and informed decision-making. Let's look into real-life examples of ETL.
E-commerce businesses rely on CRM and sales data to drive decision-making and improve the customer experience.
ETL allows businesses to gain insights into customer behavior, preferences, product availability, pricing, e-commerce analytics, and trends.
ETL does this by storing data from online retail systems like WooCommerce in a single data warehouse and combining information to give e-commerce owners clarity.
They then leverage this data to optimize product recommendations, listings, promotions, and pricing strategies and identify opportunities for growth and optimization.
In short, e-commerce ETL ultimately leads to increased sales, customer satisfaction, and profitability.
In the healthcare industry, securing patient data is often scattered across multiple systems and platforms, making it difficult for healthcare providers to comprehensively understand a patient's medical history and uphold HIPAA compliance.
Data engineers use ETL to consolidate patient data from various source systems, such as electronic health records, medical imaging systems, and laboratory information systems.
Healthcare providers can, therefore, gain a complete picture of a patient's health, improve diagnosis and treatment decisions, and enhance patient outcomes.
The data pipeline can help ensure that patient data is accurate, up-to-date, and easily accessible --- a must for effective healthcare delivery.
Transaction data is essential for managing risk, detecting fraud, and complying with regulations.
ELT extracts transaction data from financial platforms like Recurly or Zuora and transforms it into a standardized format that can be loaded into a data warehouse, machine learning algorithm, or other analytics platforms upon validation.
The extracted data can include information about deposits, chargebacks, transfers, and other financial transactions.
The consolidated data gives financial institutions insights into customer behavior and helps them identify potential risks and detect fraudulent activity in real time.
Social media platforms generate vast amounts of user behavior and engagement data, which can be leveraged to improve user experience, drive engagement, and increase revenue.
ETL is used to extract user data from various social media platforms---such as Facebook, Twitter, and Instagram---and transform it into a standardized format for data analysis.
Marketing data from end users can include information about user demographics, interests, behavior, engagement, and interactions with ads and content.
Social media companies like Sprout Social gain insight into user behavior and preferences and how they can improve user experience, personalize content and advertising, and drive engagement and revenue.
The manufacturing industry generates data from numerous sources, such as inventory management systems, production lines, and supply chain partners.
ETL ingests raw data from these disparate sources, transforms it into a standard format, and loads it into a data warehouse or data lake.
This large volume of data includes information about inventory levels, production rates, quality control metrics, product attributes, pricing, and supply chain performance.
Manufacturers gain insight into operational performance and can identify bottlenecks and inefficiencies and improve production efficiency and data quality.
The logistics industry depends on timely and accurate arrival time projections to ensure efficient supply chain management.
ETL is critical in achieving this goal by extracting business intelligence data from tools like ShipHero, such as shipping manifests, weather reports, and traffic data.
Once this data is extracted, data analytics are performed.
The transformed data can include information about shipping routes, expected delivery times, potential delays, and other factors impacting supply chain performance.
Logistics companies can gain insight into supply chain performance, identify potential issues before they arise, and improve delivery accuracy and efficiency.
ETL consolidates data from student information systems, learning management systems, assessment tools, and other educational sectors. It can align metadata and schema for better insights.
This provides insights into student performance, engagement, and progress.
ETL can streamline large amounts of data, including student demographics, attendance records, test scores, and grades.
With this big data, educators can identify at-risk students, evaluate the effectiveness of instructional strategies, and tailor their teaching approach to meet the needs of individual students.
Renewable energy systems --- such as solar and wind turbines --- are equipped with sensors that collect data on their performance, including energy output and efficiency.
ETL can extract this sensor data, transform it into a usable format, and load it into a staging area for analysis.
This analysis provides insights into the performance of renewable energy systems, identifies areas for improvement, and ultimately increases energy production and efficiency.
ETL can also help optimize maintenance schedules, reduce downtime, and improve the overall sustainability of renewable energy systems.
Let's dive into each stage of the ETL process and examine examples from various industries.
The first stage of the ETL process, data extraction, is crucial in integrating various types of data from disparate sources into a centralized data repository.
It is achieved through various extraction techniques such as API integration, web scraping, and database extraction.
Most examples of ETL data extraction involve data sources like SaaS apps and platforms like CRMs (e.g., Salesforce) or ERPs (e.g., Oracle).
Web scraping involves using specialized software to crawl websites and extract relevant data.
ETL systems can extract from internal databases using languages like SQL and Python. Common formats include XML and JSON.
Organizations can collect and analyze big data from multiple sources by combining various extraction methods, providing valuable insights for decision-making and reporting.
The second stage of the ETL process is data transformation, where data is standardized, filtered, and enhanced to make it more usable for analysis and reporting.
Data cleaning is an essential step in data transformation, involving the standardization of unstructured data formats, removing duplicates, and filtering irrelevant data.
Data aggregation is another form of data transformation, combining data to make it more easily digestible. For example, sales data from an e-commerce platform can be matched with traffic patterns from a site analysis tool.
Finally, data enrichment is a process that enhances data with additional information---such as geographic or demographic data---to provide deeper insights.
These transformation methods allow organizations to make data more easily accessible for analysis and reporting and drive better industry outcomes.
Data loading, the final stage of the ETL process, involves loading transformed data into a destination database or data warehousing for analysis and reporting.
Examples of data loading techniques in the ETL process include bulk, incremental, and merge loading.
Bulk loading involves loading large volumes of data at once. For example, importing archived spreadsheets into a modern data warehouse like Amazon Redshift.
An incremental data load process involves loading new data while maintaining existing data, like updating stored customer data with new shipping addresses without overwriting existing fields.
Merge loading involves combining data from different sources into a single data set.
ETL tools can be categorized into different types. These include:
Open-source vs. proprietary. Differ in terms of licensing, customization, and community support. Open-source is cost-effective but requires expertise, while proprietary can be plug-and-play but is more expensive.
Cloud-based vs. on-premise. Vary in their accessibility, scalability, and security. Cloud-based works with modern platforms like AWS, while on-premise tools work with legacy systems.
Code-based vs. GUI-based. Offer different levels of flexibility and ease of use for developers. Code-based is more flexible but requires expertise, while GUI-based is easier to use but can be limiting.
Batch vs. real-time. Differ in their ability to handle large volumes of data and data velocity. Batch is more affordable but slower, while real-time data processing is more costly but offers lower latency.
Some ETL tools are designed for specific use cases. For example, compare Segment and Talend; Segment only works with customer data, while Talend can work with data across departments.
Consider the type and volume of data to be processed, the data sources and targets involved, and the complexity of the transformation logic required.
This will help narrow the options and ensure the selected ETL tool meets the project's needs.
Look for tools that offer an intuitive user interface and require only the technical expertise you already have in your team.
The ability to scale up or down as needed is also important, particularly if the volume of data being processed is expected to grow over time.
Security is another critical consideration, mainly if the processed aggregate data contains sensitive or confidential information.
Look for tools offering robust security features---such as encryption and access controls---to protect the source data.
Some tools are offered as open-source software, which can be freely downloaded and used without licensing fees.
Other tools are proprietary and require a license, which can vary in cost depending on the vendor and the features included.
Consider the total cost of ownership, including licensing fees, maintenance and support costs, and any hardware or infrastructure requirements.
Look for tools that offer flexible pricing and licensing options---such as per-user or per-project pricing---to ensure the tool is cost-effective for the specific use case.
Look for tools that offer comprehensive support, including documentation, training resources, and on-premises technical support.
Check the vendor's reputation and history in the market, and look for customer reviews and ratings to understand how the tool performs in real-world scenarios.
This can help ensure that the selected tool has a track record of success and that the vendor is responsive and helpful in addressing any issues or concerns that may arise.
Most major ETL tools offer pre-built connectors to must-have data sources---such as databases, cloud storage platforms, and APIs---to streamline the data integration process.
If you have long-tail data sources, a solution like Portable can also help you use that data.
This can help ensure the selected tool can connect to the necessary data sources without requiring extensive custom development or integration work.
These ETL examples are just the beginning. As a part of the modern data stack, handling ETL jobs are crucial to scaling company growth.
ETL tools are useful to automate the process of extracting data from multiple sources, transforming it into a single format, and loading it into a target destination.
Working with an ETL solution like Portable will improve your organization's data quality. Portable ensures the information loaded into the target system is complete, accurate, and valid.
With over 300 long-tail connectors — and the option to develop custom connectors in a few days — you'll save time and resources on data integration tasks and make informed decisions quicker!