ETL stands for "Extract, Transform, Load", and is the common paradigm by which data from multiple systems is combined to a single database, data store, or warehouse for legacy storage or analytics.
ETL platforms have been a critical component of enterprise infrastructure for decades. But the advent of cloud, SaaS, and big data has produced an explosion in the number of new data sources and streams, spiking demand for correspondingly more powerful and sophisticated data integration. Real-time ingestion, data enrichment, the ability to handle billions of transactions, and support for structured or unstructured data from any source, whether on-premise or in the cloud, have all become requirements for today’s enterprise data-integration solutions. Further, these tools must be scalable, flexible, fault-tolerant, and secure — all things that old-school, on-premise solutions simply cannot deliver.
A brief history of ETL
ETL emerged in the 1970s, when large enterprises began to aggregate and store information from multiple sources with different data types, such as payroll systems, sales records, inventory systems, and so on. The need to integrate this data naturally followed, paving the way for the development of ETL.
The data warehouse came into vogue in the 1980s. This type of database could integrate data from multiple sources. The problem was that many data warehouses required vendor-specific ETLs. So it wasn’t long before many enterprises ended up with multiple ETLs, none of them integrated.
As time went on, the number of data sources and types — along with the number of ETL vendors — increased dramatically. Accordingly, prices decreased to a level that allowed ETL to become a viable solution for the mid-market, helping companies build modern, data-empowered businesses.
How the ETL process works
Imagine a retailer with both brick-and-mortar and online storefronts. Like any company, the retailer needs to analyze sales trends across its entire business. But the backend systems for these storefronts are likely to be separate. They may have different fields or field formats (such as day-month-year dates vs month-day-year dates). They may use systems that can't "talk" to each other. This is where ETL comes in. It extracts the relevant data from both systems, transforms it to meet the format requirements of the data warehouse, and then loads it into the data warehouse.
Here's a breakdown of the three phases:
Extraction is the process of retrieving data from one or more sources — online, brick-and-mortar, legacy data, Salesforce data, and many others. After retrieving the data, the ETL loads it into a staging area and prepares it for the next phase.
Transformation is a critical function, because it's what paves the way for data integration. Like the previous example of a retailer with different channels, transformation may involve reformatting. But sometimes there are other types of transformation involved in this step: for example, computation where currency amounts are converted from US dollars to Euros.
Loading involves successfully inserting the incoming data into the target database, data store, or data warehouse.
Learn more about the ETL process.
While ETL is a powerful tool for managing your data, it is not without its challenges. That said, a number of headaches can be alleviated with proper planning. Below are a few of the most prevalent ETL challenges.
Challenge # 1. Scaling. There are a number of ETL tools with excellent capacity to handle large volumes of data, but scaling with ETL tools can be challenging. Suppose your ETL tool moves data in batches. Will you be able to move the data in batches if the size of your business doubles? Or, if you have to add new data sources, some ETL tools require you to reload your data once you make a change, which doesn’t scale over time.
Challenge #2. Transforming data correctly. This process can be tricky to get right. Getting data into one place, using formats that are compatible without loading duplicates or incompatible data types, corrupting the data, or simply missing critical data requires careful planning and testing.
Challenge #3. Handling diversity of data sources. The volume of data a thriving company may want to analyze continues to grow exponentially. These days, you may want to analyze data from unstructured sources, real-time sources, S3 buckets, flat files, CSVs, streaming sources, etc. It can be challenging bringing these systems together, especially since some of these systems are best handled in different ways: large volumes of RDBMS data may be best transformed in large batches, while streaming sources might be best transformed continuously.
Types of ETL Tools
Depending on how you want to transform your data, and where you want to transform your data, different tools are appropriate. For a comparative list of ETL tools, see ETL Tools: A Modern list.
Batch. Batch processing ETL tools are designed to move large volumes of data at the same scheduled time, usually when network traffic is low.
Cloud native. These ETL tools are hosted in the cloud, where you can leverage the expertise and infrastructure of the vendor. In addition, they are optimized to work with cloud native data sources.
Open source. Open source ETL tools can be modified and shared because their design is publicly accessible, and free or lower cost than commercial alternatives.
Real time. These tools can process data in real time instead of processing the data in large batches. These tools are optimal for data that is streaming, or for data that is associated with time-sensitive decision making (such as data from a sensor that requires immediate action).
ETL Use Cases
ETL is commonly used for the following functions.
When companies move their data and applications from their premises to the cloud, this process is called cloud migration. Companies often do this to save money, make their applications more scalable, and secure their data. ETL is commonly used to move data to the cloud.
A data warehouse is a database where data from various sources are piped so that they can be collectively analyzed for business purposes. ETL is often used to move data to a data warehouse.
Machine learning is a method of data analysis that automates analytical model building. ETL can be used to move the data into a single location for machine learning.
Marketing data integration
Marketing integration involves moving all your marketing data — such as marketing, customer data, social networking data, and web analytics — into one place, so that you can analyze it. ETL is used to bring marketing data together.
IoT data integration
IoT stands for the Internet of Things and refers to the use of connected devices and systems that leverage data gathered by embedded sensors and actuators in machines and other physical objects, such as the sensor in a stoplight. ETL can help to move data from various IoT sources to a single place where you can analyze it.
Data replication takes data from your source databases — Oracle, MySQL, Microsoft SQL Server, PostgreSQL, MongoDB, etc. — and copies it into your cloud data warehouse. This can be a one-time operation or an ongoing process as your data is updated. ETL can be used to replicate the data.
Business intelligence is the process of analyzing data to enable executives, managers, and other stakeholders to make informed business decisions. ELT can be used to bring this data to one place so that the data is actionable.
The modern ETL: faster, more powerful, scalable
Traditional on-premise ETLs come bundled with a set of headaches. For example, they are often built in-house and so can quickly become obsolete or lack sophisticated features and functionality. They are expensive and time consuming to maintain. They support only batch (as opposed to real-time) processing and do not scale well.
In contrast, modern ETL tools, such as Alooma, can capture, transform, and store data from millions (or billions) of transactions across a wide variety of data sources and streams. This capability provides a wealth of new opportunities: analyzing historical records to optimize the sales process, adjusting prices and inventory in real time, leveraging ML/AI to create predictive models, developing new revenue streams, moving to the cloud, and more.
The modern ETL is:
- Format-agnostic and flexible enough to quickly and easily integrate new data sources
- Able to process massive amounts of data in real time, enabling lightning-speed analysis
- Easy to scale, because it leverages the elastic cloud
- Fully managed
Alooma: built for today's data-enabled enterprise
Alooma's enterprise ETL platform provides a format-agnostic, streaming data pipeline to enable real-time data processing, transformation, analytics, and business intelligence.
It goes beyond the traditional ETL to:
- Extract your data from hundreds of sources, including databases, SaaS applications, on-premise servers, cloud storage, APIs, SDKs, and custom sources.
- Transform and map your data in any way you want using the Alooma Code Engine and Mapper.
- Stream billions of events in real time, with millisecond latency.
- Load your data into Amazon Redshift, Google BigQuery, Snowflake, and other industry-standard data warehouses.
- Connect to your data in the ways that work for your unique environment. Access cloud and on-premise data together in a single source.
- Visualize your data flow in real time with Alooma Live.
- Secure your data with enterprise-grade technology. Alooma is SOC 2 Type II, HIPAA, GDPR, and EU-US Privacy Shield Framework compliant and supports OAuth 2.0. Data is encrypted in motion and at rest.
Finally, as a managed service, Alooma removes the stress of building and managing a data pipeline in-house.