
How to drive trusted decisions without changing your current data infrastructure.
Learn more about DataOS® in our white paper.
As enterprises modernize their data architecture, they have to make challenging decisions about legacy systems. Not all organizations are ready to offload their ETL pipelines. Unless decision-makers are also tech experts, they may not realize that ETL pipelines continue to exist within their data stack. However, digital transformation requires a comprehensive look at what’s going on underneath the hood, so let’s explore the humble ETL pipeline and its place in the modern data world.
ETL stands for “Extract, Transform, Load.” An ETL pipeline enables data analysis behind-the-scenes and offers a repeatable process to unify data from multiple sources. It operates in three stages:
ETL pipelines have been in use since the 1970s and have changed a lot in the way businesses derive value from data. If an organization has been around for a few decades, there’s a good chance ETL pipelines are present in the data stack.
Originally, ETL pipelines changed data analysis because they automated much of the mundane work required to retrieve and scrub data. Most data fell into a structured model originating in databases and operational systems designed for data like this. ETL pipelines were formatted for specific users and allowed IT to avoid tedious code-writing just to query data.
Data today isn’t so structured. Organizations are harvesting data from social media, images, voice recordings…nothing is “neat” about data anymore. It’s bigger and messier and only becoming more so as people live out their lives online.
ETL pipelines can’t keep up. They’re too rigid and don’t transfer well from one set of users to another or from one type of data to another. They require a heavy investment from IT and slow down insights needed for decisions today, not months from now.
Some organizations replaced ETL with ELT, where data loading and transforming are decoupled to reduce those bottlenecks. However, even ELTs — while more efficient than ETL pipelines — still have one key obstacle for a truly transformed data stack: they don’t facilitate self-service for the nontechnical user.
ETL and ELT pipelines require technical know-how to build, maintain, and troubleshoot, but companies need everyone in the organization to become data stakeholders. Departments don’t have time to wait months to receive data-backed insights to make decisions. They need access to data now, and they need the support to build reports and queries without overloading IT with requests.
A data operating system like DataOS from The Modern Data Company (Modern) offers a new way to manage data. It’s a connective tissue for an entire data ecosystem, uniting tools and data sources from all over the enterprise. Nontechnical users can search for the data they need using a simple, Google-like approach and work directly with data without making copies or moving it.
Without data movement, enterprises experience fewer risks in security and can implement organization-wide governance policies that remain consistent no matter the user. For example, the marketing department can view critical marketing data with columns of sensitive information removed through a marketing Data Lens. Legal departments can see all columns and rows through their own Data Lens.
Both departments can accomplish their own data transformation through a right-to-left engineering approach that abstracts data complexity — the system removes the need for specialized technical knowledge to build the right pipelines. Users drag and drop the functions they need, and DataOS takes over to create exactly the right thing.
ETL pipelines were a significant improvement over hand-coding, but DataOS takes data analysis to the edge with a comprehensive, connective, and consistent data solution. Business users gain insights in near real-time because they’re taking ownership of the data process themselves. Organizations can finally free data for all stakeholders safely and efficiently, and IT no longer needs to build tedious ETL or ELT pipelines.
To see it work specifically for your organization and to understand the scope of what DataOS can do for your organization’s data, download our latest white paper: “A Modern Data Strategy for Enterprises”.
Be the first to know about the latest insights from Modern.
In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions, we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Remember to read part one if you need a quick refresher. ...
Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. Each has unique advantages and drawbacks, and the right...
What is a data operating system? On the surface, it's an operating system designed specifically for managing and processing large amounts of data. It typically provides a scalable and flexible infrastructure for storing, processing, and analyzing big data and should...
Prevention and early intervention are essential to building an effective healthcare approach that supports patients from start to finish. The critical component of this approach is predictive analytics — analyzing big data gathered from patients, consumers, and...
Technical debt is something that many companies are aware of and are attempting to address. It is a big enough issue that several of our recent blog posts (Lessons in Technical Debt from Southwest Airlines, Start Paying Down Your Technical Debt Today, and A Better Way...
Data Mesh + Patient360: A Modern Revolution for Healthcare DataHealthcare organizations are sitting on a treasure trove of customer data. Operationalizing that data makes it actionable and usable, helping improve services, costs, and patient outcomes. However,...
The Modern Data Company BriefThe Modern Data Company is radically simplifying data architecture with its paradigm-shifting data operating system, DataOS. We're replacing overwhelm with composability, reinventing governance, and connecting legacy systems to your newest...
DataOS® – The Fastest Path from Data to DecisionDataOS is the world's first fully-integrated data operating system designed to move from companies from data to decision in weeks instead of months. Discover what makes DataOS different from the competition and how...
Not Getting Value from Your Data Transformation? Fix itImplementing customer lifetime value as a mission-critical KPI has many challenges. Companies need consistent, high-quality data and a straightforward way to measure CLV. In the past, organizations have struggled...
DataOS® Solution:AI/ML 70% of AI initiatives fail and teams spend the vast majority of their time simply prepping data for platforms, leaving very little left over for gaining insights and driving business value. But an AI/ML platform powered by DataOS can achieve...