Since the dawn of big data, the ETL (extract, transform, and load) process has been the heart that pumps information through modern business networks. Today, cloud-based ETL is a critical tool for managing massive data sets, and one that companies will increasingly rely on in the future. The reason is simple: in today’s competitive landscape, data is like blood — without enough of it, you’ll die.

ETL — A Brief Introduction

ETL is the global standard for processing large amounts of data. ETL distributes the process across a set of linked processors which operate over a common framework (such as Apache Hadoop.) The ETL process includes three distinct functions:

  • Extract. During the extraction process, raw data is pulled from an array of sources including databases, network appliances, security hardware and software applications, and others. This is streaming data rushes through digital networks and is collected in near real-time.
  • Transform. In the transformation phase of the ETL process, rivers of information are channeled into usable data for business. At the same time, the ETL engine reduces data volume by detecting and eliminating duplicate data. The data is then standardized and formatted for later use and/or analysis. Finally, data is then sorted and verified before it is passed on to the next phase.
  • Load. The last stage of the ETL process deposits the data into the desired destinations. These include analysis tools, databases or lakes, cold network repositories, or other applicable uses.

In relative terms, ETL has been around for ages. But the way it has been used to turn raw data into business intelligence hasn’t just evolved with the times — it’s also helped pave the way for cloud technology.

Traditional ETL — Locally Sourced

Before fiber optics and globally distributed cloud resources were developed, ETL processes were managed locally. Imagine a large, noisy computer room in which a technician or two wander among stacks of computers and network racks verifying connections.

In the late 1970s, the value of databases exploded when the tools used to standardize (or transform) data into common formats became widely accessible. Some of the most important ETL projects of this era included

  • Research facilities sharing large volumes of scientific data
  • Early collaborations in what became the World Wide Web, the forerunner of the modern internet
  • The standardization of a communications protocol (TCP/IP) from which most modern data and telecommunications evolved
  • The ancestor of modern digital marketing technologies that aggregate consumer data and tailor advertisements to specific demographics

For much of ETL’s history, the process was performed locally, or physically, near the scientists and analysts who used it. Data streamed into secure facilities through a system of cables and was extracted via simple algorithms. The data was then transformed into a standardized or “clean” format, and loaded into databases where humans could manipulate and learn from it.

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This approach laid the foundation for many of the technology and communication options we know today. Despite its importance, traditional ETL had some serious limitations. In the days before miniaturization, the cost of the ETL process and the need for massive amounts of storage were often cost-prohibitive. In addition, maintaining all of this valuable data in a single location brought the added risk of catastrophic loss through natural disaster, theft, or technological failure.

Flash forward to 2018. Cheap data storage options, fiber networks, and ever-increasing processor speeds guarantee three things about data:

  • The amount of data flowing through modern businesses will continue to grow exponentially
  • The value of that data will continue to climb
  • The computing power needed to process all that data — and the challenge of putting it to the right business use — means that ETL in the cloud will play a vital role in big data of tomorrow.

The Move to the Cloud

As national and global networks evolved in both speed and capacity, the need to store mountains of data at local sites gradually declined.

Technologist Brian Patrick Eha has traced the evolution of internet speed and the impact of the cloud on data transfer. According to Eha, in 1984 a relatively fast dedicated data line might be able to reach transfer speeds of 50 kilobits per second (Kbs). By 2013, commercially available fiber optic connections increased that throughput to as much as 1 gigabyte per second. This dramatic change in speed, along with the proliferation of cheap, replaceable storage, were the catalysts that transformed ETL from a local, expensive, and cumbersome process to what we know of today as cloud-based ETL.

According to a 2018 report from IDG, almost three quarters of businesses now operate partially or fully in the cloud, and that number will exceed 90 percent by 2020.

Cloud ETL

Today, ETL processes are taking place in the cloud, alongside technologies such as application development, eCommerce, and IT security. Cloud-native ETL follows the familiar three-step process, but changes the way the steps are completed.

The Apache Hadoop framework became the road over which cloud-based ETL developed. Hadoop distributes the computing processes, which means that data from divergent sources can be remotely extracted, transformed via a network of computing resources, and then transformed for local analysis.

Cloud-native ETL depends on shared computing clusters. These may be geographically scattered around the globe, but through Hadoop operate as individual, logical entities that share the work of massive computing tasks. The ETL tasks once executed next door or in the basement are now processed over scattered clusters via cloud interfaces.

Most remarkably, all this can happen at orders of magnitude faster than traditional, on-site ETL. Companies still using ETL in an on-premise or hybrid environment are already falling behind in a key competitive category: speed.

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This cloud process produces analytics screens that are often familiar to traditional ETL professionals, who can use reliable tools to search and mine the data as they did in years past. The Apache Software Foundation is the world’s biggest open-source community for developing and supporting ETL and the tools that make it usable to humans.

But the sheer volume of datasets in play today, and the rate at which they constantly grow, is producing new challenges to getting useful, highly customized business intelligence from traditional ETL tools. More and more companies are turning to data management platforms to meet their unique ETL needs.

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Talend: the Managed Solution for Cloud ETL

Since 2005, Talend has been helping top organizations tackle ETL and other data integration challenges with hosted, user-friendly solutions. With Talend Open Studio for Data Integration and the Talend Data Management Platform, developers and analysts can work with nearly unlimited data sets from every commonly used used format to harness the power of ETL and other technologies upon which modern cloud business depends.

But far from being a tech geek’s toyland, Talend makes real-time, manageable ETL and associated tasks accessible to users who depend on current, trusted business intelligence to make smart decisions. From sales to shipping to customer service, modern business interactions must be fast, efficient, and cost-effective, and Talend’s ability to deliver the necessary data to the right people can bring huge improvement to any organization.

The Talend suite of solutions for big data addresses one of organizations’ most common pain points: the shortage of skilled developers. With Talend, automated, GUI-launched processes reduce the need for hand-coding to specific instances, making ETL management and data mining faster and more efficient.

Most importantly, the open-source Talend platform continues to scale at the speed of big data, ensuring that even the most demanding and specific data needs are met with relative ease.

Start your free trial today to find out why some of the most successful organizations in the world have chosen Talend to liberate their data from legacy infrastructures with an ETL integration platform built for the cloud.