If you’re working with data warehouses and data integration, chances are you’re familiar with the phrase “ETL.” ETL stands for extract, transform, and load. It’s a three-step data integration process used to by organizations to combine and synthesize raw data from multiple data sources into a data warehouse, data lake, data store, relational database or any other application. Data migrations and cloud data integrations are common use cases for ETL.
ETL moves data from one or more sources to a destination like a database, data warehouse, data store, or data lake in three distinct steps. Here’s a quick summary:
Extraction is the first phase of “extract, transform, load.” Data is collected from one or more data sources and held in temporary storage where the next two steps of ETL are executed.
During extraction, validation rules are applied to test whether data conforms to its destination’s requirements. Data that fails validation is rejected and doesn’t continue to the next steps of ETL.
In the transformation phase, data is processed to make its values and structure conform consistently with its intended use case. The goal of transformation is to make all data fit within a uniform schema before it moves on to the last step of ETL.
Typical transformations include formatting dates, resorting rows or columns of data, joining data from two values into one, or, conversely, splitting data from one value into two.
Finally, the load phase moves the transformed data into a permanent, target database, data warehouse, data store, or data lake — on-premises or in the cloud. Once all the data has been loaded, the extract, transform, load (ETL) process is complete.
Many organizations regularly perform ETL to keep their data warehouse updated with the latest data.
Traditional or legacy ETL is designed for data located and managed completely on-premises by an experienced in-house IT team whose job is to create and manage in-house data pipelines and data bases.
As a process, it generally relies on time-consuming batch processing sessions that allow data to be moved in scheduled batches, ideally when traffic on the network is reduced. Real-time analysis can be hard to achieve. To extract the necessary analytics, IT teams often create complicated, labor-intensive customizations and exacting quality control. Plus, traditional ETL systems can’t easily handle spikes in large data volumes, often forcing organizations to choose between detailed data or fast performance.
Cloud ETL, also known as modern ETL, extracts both structured and unstructured data from any data source type whether they’re in on-premises or cloud data warehouses, then consolidates and transforms that data and loads it into a centralized location where it can be accessed on demand.
Cloud ETL is often used to make data readily available for analysts, engineers, and decision makers across a variety of use cases within an organization.
ETL (extract transform load) and ELT (extract load transform) are two different data integration processes that use the same steps in a different order to help with different data management functions.
Both ELT and ETL extract raw data from different data sources like an enterprise resource planning (ERP) platform, social media platform, Internet of Things (IoT) data, spreadsheet, and more. With ELT, raw data is then loaded directly into the target data warehouse, data lake, relational database, or data store. This allows data transformation to happen as required. It also lets you load datasets from the source. With ETL, after data the is extracted, it is then defined and transformed to improve data quality & integrity and is subsequently loaded into a data repository where it can be used.
If you’re creating data repositories that are smaller, need to be retained for a longer period, and don’t need to be updated very often, then ETL is the way to go. If you’re dealing with high-volume datasets and big data management in real-time, then ELT would be best for your use case.
Check out the “What are the differences between ETL and ELT?” article for an in-depth overview.
While the terms ETL pipeline and data pipeline are sometimes used interchangeably, they really shouldn’t be as there are fundamental differences in what they describe.
A data pipeline is used to describe any set of processes, tools, or actions used to ingest data from a variety of different sources and move it to a target repository. This can trigger additional actions and process flows within interconnected systems.
With an ETL pipeline, the transformed data is stored in a database or data warehouse where the data then can be used for business analytics and insights.
ETL data pipelines are categorized based on their latency. The most common forms of ETL pipelines employ either batch processing or real-time processing.
Batch processing is used for traditional analytics and business intelligence use cases where data is periodically collected, transformed, and moved to a cloud data warehouse.
Users can quickly deploy high-volume data from siloed sources into a cloud data lake or data warehouse and schedule jobs for processing data with minimal human intervention. With ETL in batch processing, data is collected and stored during an event known as a “batch window” to more efficiently manage large amounts of data and repetitive tasks.
Real-time data pipelines enable users to ingest structured and unstructured data from a range of streaming sources such as IoT, connected devices, social media feeds, sensor data and mobile applications. A high-throughput messaging system ensures the data is captured accurately.
Data transformation is performed using a real-time processing engine like Spark streaming to drive application features like real-time analytics, GPS location tracking, fraud detection, predictive maintenance, targeted marketing campaigns, or proactive customer care.
The increased processing capabilities of cloud data warehouses and data lakes have shifted the way data is transformed. This shift has motivated many organizations to move from ETL to ELT. This isn’t always an easy change.
ETL mappings have grown robust enough to support complexity in data types, data sources, frequency, and formats. Successfully converting these mappings into a format that supports ELT requires an enterprise data platform capable of processing data and supporting pushdown optimization without breaking the front end. If the platform can’t generate the ecosystem or data warehouse specific code necessary, developers often end up hand coding the queries to incorporate advanced transformations. This labor-intensive process is costly, complicated, and frustrating. That’s why selecting a platform with an easy-to-use interface that can handle replicating the same mappings and run in an ELT pattern is a must.
When used with an enterprise data warehouse (data at rest), ETL provides historical context for your business by combining legacy data with data collected from new platforms and applications.
ETL helps you transfer your data to a cloud data lake or cloud data warehouse to increase data accessibility, application scalability, and security. Businesses rely on cloud integration to improve operations now more than ever.
Ingest and synchronize data from sources such as on-premises databases or data warehouses, SaaS applications, IoT devices, and streaming applications to a cloud data lake to establish a 360 view of your business.
Businesses today need 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 make it easier to derive actionable insights from your data, so you can identify new business opportunities and guide improved decision-making.
Use ETL tools to transform data while maintaining data lineage and traceability throughout the data lifecycle. This means all data practitioners ranging from data scientists to data analysts to line-of-business users have access to reliable data, no matter their data needs.
AI and ML-based ETL automates critical data practices, ensuring the data you receive for analysis meets the quality standard required to deliver trusted insights for decision-making. It can be paired with additional data quality tools to guarantee data outputs meet your unique specifications.
Technical people aren’t the only ones who need ETL. Business users also need to easily discover data and integrate it with their systems, services, and applications. Infusing AI in the ETL process at design time and run time makes this easy to achieve. AI and ML enabled ETL tools can learn from historical data and suggest the best reusable components like data mappings, mapplets, transformations, patterns, configurations, and more for the business users’ scenario. The result? Increased team productivity. Plus, automation makes policy compliance easier since there’s less human intervention.
AI-based ETL tools enable time-saving automation for onerous and recurring data engineering tasks. Gain improved data management effectiveness and accelerate data delivery. Automatically ingest, process, integrate, enrich, prepare, map, define, and catalog data.
Cloud ETL tools let you efficiently handle the large data volumes required by data pipelines used in AI and ML. With the right tool, you can drag and drop ML transformations into your data mappings. This makes data science workloads more robust, efficient, and easier to maintain. While AI powered ETL tools also let you easily adopt continuous integration/continuous delivery (CI/CD), DataOps, and MLOps to automate your data pipeline.
ETL is used to replicate and auto sync data from various source databases such as MySQL, PostgreSQL, Oracle, and others to a cloud data warehouse. To save time and increase efficiency, use change data capture to automate your ETL process to only update the datasets that have changed.
Teams will move more quickly as ETL reduces the effort needed to gather, prepare, and consolidate data. AI-based ETL automation improves productivity because it lets data professionals access the data they need where they need it without needing to write code or scripts — saving valuable time and resources.
While ETL is implemented to share the data between a source and target, technically the data is copied, transformed, and stored in a new place. This can affect pricing and resources depending on whether you’re using on-premises ETL or cloud ETL.
Businesses using on-premises ETL have already paid for the resources. So, pricing doesn’t matter as much since their data is stored on location with an annual budget and capacity planning. But in the cloud, the storage cost is recurring, usage-based, and goes up every time you ingest and save the data somewhere else. This can strain your resources if you don’t have a plan.
When using cloud ETL, it’s important to evaluate cumulative storage and processing costs, and data retention requirements to ensure you’re using the right integration process for each use case. This way, you can optimize costs with the right cloud-based integration pattern and data retention policies.
ETL is an essential component in data integration projects across a variety of industries. Organizations may use ETL to ultimately increase operational efficiencies, improve customer loyalty, deliver omnichannel experiences, and find new revenue streams or business models.
Healthcare organizations use ETL in their holistic approach to data management. By synthesizing disparate data across their organization, healthcare businesses are accelerating clinical and business processes while improving member, patient, and provider experiences.
Public sector organizations use ETL to surface the insights they need to maximize their efforts working under strict budgets. Tight budgets mean more efficiency is vital to providing services with limited available resources. Data integration makes it possible for governmental departments to make the best use of both data and funding.
Manufacturing leaders transform their data to optimize operational efficiency, ensure supply chain transparency, resiliency, and responsiveness, and improve omnichannel experiences while ensuring regulatory compliance.
Financial institutions use ETL in their data integrations to access data that is transparent, holistic, and protected to grow revenue, deliver personalized customer experiences, detect & prevent fraudulent activity, or realize fast value from mergers and acquisitions while complying with new and existing regulations. They need to understand who their customers are and how to deliver services that fit their specific needs..
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