ETL Tools: Evaluating Tools for Cloud-Based ETL

Extract/Transform/Load (ETL) tools are the applications and processes used to turn raw data collected from transactions into clean information and actionable business intelligence.

In enterprise environments, petabytes of data move through ETL processes. Data ingested from sources like customer input, security interactions, application feedback, and more, then performing a series of tasks to transform this information before loading it into a working database or data warehouse.

Today much of the workload for ETL is performed in the cloud, where distributed resources and processing power constantly interact and evolve, with each transaction having immediate impact on business environment. To adapt, many organizations are leveraging the power of tools to take charge of ETL in the cloud.

Do You Need an ETL Tool?

ETL and ELT are the workhorses of modern big data environments. As the name implies, ETL performs three processes to turn disparate data streams into clean working data set:

  1. Extraction - Data is ingested from sources throughout the environment, including existing databases, application performance and anomaly reports, security events, and more.
  2. Transformation - The raw, extracted data is delivered to an interim staging area, where it is converted into usable formats by cleansing, qualifying and combining data.
  3. Loading - The transformed data is uploaded to a new home, usually another database, data warehouse, or data lake, where it can be mined for business intelligence and used to improve operations.

ETL performs these three steps sequentially, which is an important consideration as quantum jumps in virtual processing power have made it possible to reverse the second and third steps, delivering raw data to the final location and handling the transformation workload on site. This is known as ELT, and requires a different support architecture to implement, but many experts believe it’s the big data processing method of the future.

But a lot of this kind of coding can, and often is, done manually. At what point does an organization need to invest in a tool? And when is the investment really worth it? Here are a few questions your organization should answer:

  1. Is company data inconsistent? With data flooding enterprise environments each day, deriving actionable insight and quality data from multiple data sources can be an overwhelming challenge. ETL tools automate and integrate each step of the extract/transform/load process so developers and customers can interact with fresh data in real-time.
  2. Is the dev team bogged down with hand coding? Highly specific adaptations for business environments will always produce a need for hand coding, and the value of great development teams can never be overlooked. But in a modern cloud environment hand-coding can become overwhelming. The right tools instantly scale to roll out minor patches or global version updates across the environment, propagating changes and revisions automatically.
  3. Is compliance a concern? Today’s businesses are charged with adhering to ever-evolving security and compliance standards like HIPAA, PCI, and the new GDPR laws, which can cost unprepared companies up to four percent of global revenue in fines. The standards for storing and securing user data can be managed and updated centrally through ETL tools.
  4. Trying to improve slow delivery cycles? Modern continuous-delivery pipelines attempt to remove silos and integrate essentials like security and compliance on the fly, but inconsistent data is the arch enemy of developers. Maintaining one true source of information to work from opens the delivery pipeline and accelerates time-to-market cycles.
  5. Problems with SaaS? Increasing reliance on cloud-based, software-as-a-service solutions creates an extra layer of interdepencies between remote, onsite, and legacy data management. Handling these changes by reactive hand-coding will eventually become unsustainable, so ETL tools that visualize and automate the process greatly simplify management and reporting.

SMBs that still rely on on-premise server rooms and have relatively little throughput can probably manage without an ETL tool. But for larger organizations and those poised for rapid growth with leveraged cloud technologies, a tool-based approach for simplifying ETL is probably the best solution.

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Choosing the Right ETL Tool: 3 Key Features

Given the importance of big data and cloud computing in today’s IT landscape, choosing the right ETL tools to power your integration architecture is a critical consideration. Here are four things to look for in an ETL tool that can handle massive workloads and provide easy manageability.

  1. Open-source - Working with ELT or ETL involves the flow of data across many platforms and devices, so an open-source approach—that harnesses the power developers across the globe—delivers faster fixes and greater innovation. Open-source ELT tools have proven reliability and support some of the world’s biggest data tasks.
  2. Native - ELT/ELT tools should integrate and operate seamlessly. The right tool is easily configured to run in a stand-alone data center, a local-cloud hybrid environment, or as a cloud-hosted service. This flexibility gives data engineers the ability to use the tools on today’s network to help prepare for tomorrow’s environment.
  3. Unified - Given the limitless variety of data points colliding in a cloud environment, an ETL tool’s true power comes from its ability to ingest and display critical data in a unified, actionable format. A good tool interacts with all the applications, services, connectors, and more and brings them under a single interface, ending the need to cobble together products into a makeshift solution with many points of potential failure.

An ETL tool that provides these three features can be relied upon as a stable, affordable foundation on which to build a flexible approach to putting the power of big data to work.

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Talend: A Gartner Magic Quadrant Leader for Data Integration

Gartner Inc., a world leader in industry research and corporate consulting, issues annual reports that rank leaders across industries based on their abilities to meet specialized business challenges. Gartner measures companies in four key categories:

  • Leaders
  • Challengers
  • Niche players
  • Visionaries

In Gartner’s comprehensive report for 2017, Talend was again named a top tier leader in the Magic Quadrant for Data Integration Tools. Talend’s position improved from 2016, and the company remains the only one in that category that offers open-sourced solutions to power ETL tools and big data integration.

Taking the Next Steps with an ETL Tool

Talend is an industry leader with more than 15 years of experience proving ETL tools and data integration solutions for companies like Lenovo, Beachbody, Carhartt, health care organizations around the world, and more. With open-source solutions and a global support community, visual tools that reduce or eliminate hand-coding, and built-in measures for ensuring compliance, Talend’s ETL approach powers the biggest data jobs.

Learn more about what Talend can do for your organization, and download the free Open Studio for Big Data to get started on simplifying your ETL and ELT challenges.

| Last Updated: August 21st, 2018

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