What Is ETL?
In this article, we're going to learn about the revised definition of ETL in this, the age of cloud, SaaS and Big Data. Read on for more!
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Extract, transform, load (ETL) platforms - software that extracts information from databases, re-formats/transforms it, and loads it into a data warehouse - 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, the 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.
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 ETLs, 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.
Published at DZone with permission of Garrett Alley, DZone MVB. See the original article here.
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