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What is ETL?

ETL stands for extract, transform and load. ETL is a type of data integration process referring to three distinct, but interrelated steps used to synthesize raw data from its source to a data warehouse, data lake or relational database. ETL loads data first into the staging server and then into the target system, whereas extract, load and transform (ELT) loads data directly into the target system. (Learn more about the differences between ETL and ELT.)

ETL allows businesses to move data from multiple sources, consolidate and transform that data into a single, centralized location to support how the organization wants to consume it, and then make it available to the users who can make decisions based on that data.

What does ETL accomplish?

The three verbs in extract, transform and load each describe a process in moving data from its source to a formal data storage system (most often a data warehouse). Here’s a quick summary:

  • Extract: The extraction process is the first phase of ETL, in which data is collected from one or more data sources and held in temporary storage where the subsequent two steps can be executed. During extraction, validation rules are applied to test whether data has expected values essential to the data warehouse. Data that fails the validation is rejected and further processed to discover why it failed validation and remediate, if possible.
  • Transform: In the transformation phase, the data is processed to make values and structure consistent across all data. Typical transformations include things like date formatting, resorting rows or columns of data, joining data from two values into one, or, conversely, splitting data from one value into two. The goal of transformation is to make all the data conform to a uniform schema.
  • Load: The load phase moves the transformed data into the permanent, target database – whether on-premises or in the cloud. Once loaded, the ETL process is complete. Although, many organizations regularly perform ETL to keep their data warehouse updated with the latest data.

What are the different types of ETL data pipelines?

Data pipelines are categorized based on customer use cases. Two of the most common types of pipelines are batch processing and real-time processing. Depending on how and where you want to transform the data, ETL solutions can be of one of two types:

  • Batch processing pipelines: A batch process is primarily used for traditional analytics use cases where data is periodically collected, transformed and moved to a cloud data warehouse for conventional business intelligence use cases. Users can quickly mobilize high-volume data from siloed sources into a cloud data lake or data warehouse and schedule the jobs for processing it with minimal human intervention. With batch processing, users collect and store data during an event known as a batch window, which helps manage a large amount of data and repetitive tasks efficiently.
  • Real-time processing pipelines: Real-time data pipelines enable users to ingest structured and unstructured data from a wide range of streaming sources, such as IoT, connected devices, social media feeds, sensor data and mobile applications, using a high-throughput messaging system that ensures the data is captured accurately. Data transformation happens in real time using a real-time processing engine such as Spark streaming to drive real-time analytics use cases such as fraud detection, predictive maintenance, targeted marketing campaigns and proactive customer care.

Benefits of ETL

It is essential to ingest, format and prepare data to load it in your target data storage system. When creating a data warehouse, it is common for data from disparate sources to be brought together in one place or a central repository to be analyzed for patterns and insights. ETL tools also work in combination with other data integration tools, are optimized for various data management use cases – such as data quality, data governance, virtualization and metadata.

Learn why you need to adopt ETL data integration to make your data useful:

  • Get deep historical context for business: When used with an enterprise data warehouse (data at rest), ETL provides deep historical context for the business by combining legacy data with data collected from new platforms and applications.
  • Efficiently ingest and synchronize data: Ingest and synchronize data from various sources such as on-premises databases or data warehouses, SaaS applications, IoT sources and streaming applications into a cloud data lake to deliver a single, consolidated view of the business.
  • Enable business intelligence from any data at any latency: Businesses today need to be able to analyze a range of data types – including structured, semi-structured and unstructured – from multiple sources, such as batch, real-time and streaming. ETL tools enable you to move or visualize data from different sources, which makes it easy to analyze, visualize and derive actionable insights from that data, identify new opportunities, and improve decision-making and future planning.
  • Democratize data for all users: ETL tools can transform raw data into highly transformed data with lineage and traceability throughout the data lifecycle, improving workforce productivity by empowering governed, trusted, self-service access for all data practitioners such as data scientists, data analysts, data engineers and report writers to each have different data needs.
  • Deliver clean, trusted data for decision-making: ETL tools are used for data cleansing, profiling, and auditing, ensuring data is trustworthy. ETL tools integrate with data quality tools, and ETL vendors incorporate related tools within their solutions, such as those used for data mapping and data lineage. By automating critical data practices and reducing the chance of mistakes, ETL tools ensure the data you receive for analysis is of the best quality possible and can deliver trusted insights for decision-making.
  • Migrate to the cloud: Cloud migration is a process where data and applications are moved from their premises to the cloud for increased scalability and security. ETL tools are commonly used to transfer data to the cloud. It helps extract the data from different sources, transform it into a format compatible with the new infrastructure, and then load it into the new systems or load it into a cloud data lake or data warehouse. An ETL tool removes duplicates, standardizes formats and synchronizes data, making it easier for all data practitioners and users to analyze and derive actionable insights from that clean data.
  • Gain a better understanding of your data asset through metadata: Metadata helps us understand the data lineage (where the data comes from) and its impact on other data assets in the organization. As data architectures become more complex, it’s vital to track how the different data elements in your organization are used and related.
  • Enable automated data pipelines: To effectively manage a modern data supply chain, you need to build data pipelines that automatically ingest, process, integrate, enrich, prepare, define and catalog; map data; and accelerate data delivery.
  • Replicate your database: ETL software is usually used to replicate data from various source databases, such as MySQL, PostgreSQL, Oracle, etc., and copies that data into an enterprise’s cloud data warehouse.
  • Operationalize AI and machine learning (ML) models: Machine learning requires the capacity to collect, manage and access large amounts of accurate and diverse data, the ability to create new features and train models, and the capability to deploy, monitor and update models in production. When implementing a data pipeline for data science workloads, you should not only ensure that data processing and transformation are robust, efficient and easy to maintain, but also feed the data pipeline with the latest data – and be able to handle large data volumes and data quality to address DataOps and MLOps practices for delivering faster results.
  • Achieve faster time-to-value: ETL enables context and data aggregations, so your business can generate higher revenue and save time, effort and resources. The ETL process ultimately helps you increase your ROI, and you can monetize your data by improving business intelligence.
  • Provides greater business agility by automating manual ETL: ETL can improve data professionals’ productivity because it automates the data gathering, transformation and consolidation processes that move data without requiring technical skills to write code or scripts – saving valuable time and resources.

Informatica’s ETL data integration solutions

As organizations embark upon and continue their data-driven digital transformation journey, they’re centralizing their data and analytics in cloud data warehouses and data lakes to drive advanced analytics and data science use cases. To do that, they need an enterprise-scale, cloud-native data integration solution to help them rapidly develop and operationalize end-to-end data pipelines and modernize legacy applications for AI.

Informatica offers the comprehensive, codeless, AI-powered cloud-native data integration that empowers any user to create their data pipelines across a multi-cloud environment consisting of AWS, Azure, GCP, Snowflake, Databricks, etc. Ingest, enrich, transform, prepare, scale and share any data at any volume, velocity and latency for your data integration, data warehouse and data lake, or data science initiatives.

ETL in various industries

Data integration is used in a variety of industries use cases to unify, transform and analyze the data to derive actionable insights to either increase operational efficiencies, improve customer loyalty, deliver omnichannel experiences, find new revenue streams or business models or to digitally transform enterprises. Let’s look at how different types of organizations can use data integration to improve their services and bottom line.

ETL data integration in healthcare

Healthcare organizations must take a holistic approach to managing data as an asset to realize the full potential of digital transformation. Delivering value-based care using reliable, trustworthy data accessible and universally understood provides the insights required for new models of care. Improved member, patient and provider experiences can lead to more engaged customers who achieve better outcomes. ETL is also used to accelerate clinical and business processes by synthesizing disparate data supporting operations, reporting and analytics.

ETL data integration in public sector

Public sector organizations need to use data in new ways, too. Tight budgets mean more efficiency is vital to providing services with limited available resources. Data integration makes it possible for government departments to make the best use of both data and funding.

ETL data integration in manufacturing

Manufacturing leaders need easy access to trusted, relevant data to make better business decisions and accelerate digital transformation. They require rich and trusted data to optimize operational efficiency, ensure supply chain transparency, resiliency and responsiveness, and improve omnichannel experiences while ensuring regulatory compliance.

ETL data integration in financial services

Financial institutions also need the power of data integration to better compete in today’s market. They need to understand who their customers are and how to deliver services that fit their specific needs. Financial institutions need trusted data that is transparent, holistic and protected to grow revenue, deliver personalized customer experiences, detect and prevent fraudulent activity, realize fast value from mergers and acquisitions while complying with new and existing regulations.

Get started with ETL

Informatica tools are easy to integrate and simple to use, whether you need single-cloud, multicloud or on-premises data integration. Experience Informatica’s Cloud Data Integration trial and see for yourself how broad out-of-the-box connectivity, codeless, prebuilt advanced transformations and orchestrations can help accelerate your data pipelines.