Extract, transform, and load (ETL) is the process data-driven organizations use to gather data from multiple sources and then bring it together to support discovery, reporting, analysis, and decision-making.
The data sources can be very diverse in type, format, volume, and reliability, so the data needs to be processed to be useful when brought together. The target data stores may be databases, data warehouses, or data lakes, depending on the goals and technical implementation.
During extraction, ETL identifies the data and copies it from its sources, so it can transport the data to the target datastore. The data can come from structured and unstructured sources, including documents, emails, business applications, databases, equipment, sensors, third parties, and more.
Because the extracted data is raw in its original form, it needs to be mapped and transformed to prepare it for the eventual datastore. In the transformation process, ETL validates, authenticates, deduplicates, and/or aggregates the data in ways that make the resulting data reliable and queryable.
ETL moves the transformed data into the target datastore. This step can entail the initial loading of all the source data, or it can be the loading of incremental changes in the source data. You can load the data in real time or in scheduled batches.
The transformation step is by far the most complex in the ETL process. ETL and ELT, therefore, differ on two main points:
In a traditional data warehouse, data is first extracted from "source systems" (ERP systems, CRM systems, etc.). OLAP tools and SQL queries depend on standardizing the dimensions of datasets to obtain aggregated results. This means that the data must undergo a series of transformations.
Traditionally, these transformations have been done before the data was loaded into the target system, typically a relational data warehouse.
However, as the underlying data storage and processing technologies that underpin data warehousing evolve, it has become possible to effect transformations within the target system. Both ETL and ELT processes involve staging areas. In ETL, these areas are found in the tool, whether it is proprietary or custom. They sit between the source system (for example, a CRM system) and the target system (the data warehouse).
In contrast, with ELTs, the staging area is in the data warehouse, and the database engine that powers the DBMS does the transformations, as opposed to an ETL tool. Therefore, one of the immediate consequences of ELTs is that you lose the data preparation and cleansing functions that ETL tools provide to aid in the data transformation process.
Traditionally, tools for ETL primarily were used to deliver data to enterprise data warehouses supporting business intelligence (BI) applications. Such data warehouses are designed to represent a reliable source of truth about all that is happening in an enterprise across all activities. The data in these warehouses is carefully structured with strict schemas, metadata, and rules that govern the data validation.
The ETL tools for enterprise data warehouses must meet data integration requirements, such as high-volume, high-performance batch loads; event-driven, trickle-feed integration processes; programmable transformations; and orchestrations so they can deal with the most demanding transformations and workflows and have connectors for the most diverse data sources.
After loading the data, you have multiple strategies for keeping it synchronized between the source and target datastores. You can reload the full dataset periodically, schedule periodic updates of the latest data, or commit to maintain full synchronicity between the source and the target data warehouse. Such real-time integration is referred to as change data capture (CDC). For this advanced process, the ETL tools need to understand the transaction semantics of the source databases and correctly transmit these transactions to the target data warehouse.
Data marts are smaller and more focused target datastores than enterprise data warehouses. They can, for instance, focus on information about a single department or a single product line. Because of that, the users of ETL tools for data marts are often line-of-business (LOB) specialists, data analysts, and/or data scientists.
ETL tools for data marts must be usable by business personnel and data managers, rather than by programmers and IT staff. Therefore, these tools should have a visual workflow to make it easy to set up ETL pipelines.
Learn about no-code data flow design
Data lakes follow a different pattern than data warehouses and data marts. Data lakes generally store their data in object storage or Hadoop Distributed File Systems (HDFS), and therefore they can store less-structured data without schema; and they support multiple tools for querying that unstructured data.
One additional pattern this allows is extract, load, and transform (ELT), in which data is stored “as-is” first, and will be transformed, analyzed, and processed after the data is captured in the data lake. This pattern offers several benefits.
ETL tools for data lakes include visual data integration tools, because they are effective for data scientists and data engineers. Additional tools that are often used in data lake architecture include the following:
The ETL process is fundamental for many industries because of its ability to ingest data quickly and reliably into data lakes for data science and analytics, while creating high-quality models. ETL solutions also can load and transform transactional data at scale to create an organized view from large data volumes. This enables businesses to visualize and forecast industry trends. Several industries rely on ETL to enable actionable insights, quick decision-making, and greater efficiency.
Financial services institutions gather large amounts of structured and unstructured data to glean insights into consumer behavior. These insights can analyze risk, optimize banks’ financial services, improve online platforms, and even supply ATMs with cash.
Oil and gas
Oil and gas industries use ETL solutions to generate predictions about usage, storage, and trends in specific geographical areas. ETL works to gather as much information as possible from all the sensors of an extraction site and process that information to make it easy to read.
ETL solutions can enable dealerships and manufacturers to understand sales patterns, calibrate their marketing campaigns, replenish inventory, and follow up on customer leads.
With the unprecedented volume and variety of data being produced today, telecommunications providers rely on ETL solutions to better manage and understand that data. Once this data is processed and analyzed, businesses can use it to improve advertising, social media, SEO, customer satisfaction, profitability, and more.
With the need to reduce costs while also improving care, the healthcare industry employs ETL solutions to manage patient records, gather insurance information, and meet evolving regulatory requirements.
Clinical labs rely on ETL solutions and artificial intelligence (AI) to process various types of data being produced by research institutions. For example, collaborating on vaccine development requires huge amounts of data to be collected, processed, and analyzed.
With Internet of Things (IoT) capabilities emerging so quickly, smart cities are using ETL and the power of AI to optimize traffic, monitor water quality, improve parking, and more.
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Digital transformation often demands moving data from where it’s captured to where it’s needed, and GoldenGate is designed to simplify this process. Oracle GoldenGate is a high-speed data replication solution for real-time integration between heterogeneous databases located on-premises, in the cloud, or in an autonomous database. GoldenGate improves data availability without affecting system performance, providing real-time data access and operational reporting.
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