How Automation and No-Code are Driving Modern Data Warehousing

Investment in data warehouses is rapidly rising, projected  to reach $51.18 billion by 2028 as the technology becomes a vital cog for enterprises seeking to be more data-driven by using advanced analytics.

Data warehouses are, of course, no new concept. They started out as big databases that contained all sorts of data that IT heads thought could prove useful at some point. And they were right – except that ‘useful’ has become ‘critical’, leaving traditional data warehouse architectures unable to meet analytics requirements.

Modern data warehouses bear little relation to past generations of static decision-support systems. Frontrunner organizations across vertical sectors have been quick to identify this shift, and are rapidly making modern data warehouses central to their decision-making.

By consolidating and enriching data assets from disparate sources across the enterprise, these next-gen warehouses allow businesses to deploy advanced analytics – the autonomous (or semi-autonomous) examination of data using cutting-edge techniques such as machine learning and complex event processing. Advanced analytics reveal deeper insights, and can make recommendations and predictions.

This transformation can be achieved through added functionality that enables data to be extracted and integrated in innovative ways, and a no-code design ethos that turns data warehouses into analytics powerhouses. End-users become freed-up to take charge of their ‘data destiny’ without reliance on tech specialists to help them achieve their business goals.

More data, more demanding

“As data analytics have come to the forefront of data management, data warehouses have been transformed in line with the need for agility, scalability and iterative development processes,” explains Ibrahim Surani, CEO at Astera Software. “Cloud data warehouses, for example, now provide a robust set of features and scalability, including auto-tuning, scalability-on-demand and performance that meets the most demanding workloads.”

Data warehouses must also automate the integration of diverse data streams of unprecedented volume, variety and velocity on a single platform. This increasingly complex environment has necessitated a move to accelerated loading techniques that are an order of magnitude faster than anything known to past-generation warehouses.

Only modern data warehouses can handle integrations for this data at speed and scale.

This is most evident in the move from extract-transform-load (ETL) models to extract-load-transform (ELT) approaches. In this scenario, transformations are performed by the core compute capacity of the data warehouse itself, rather than moving data to a separate staging server for processing.

The latest-generation data warehouse automation platforms, such as Astera Centerprise, leverage zero-code design, automating many levels of functionality, and putting power into the hands of business units.

With code-free ETL/ELT pipeline generation, users can take data from its source to its target warehouse with simple drag-and-drop actions. Adding further agile data modelling functionalities into the product allows models to be updated and redeployed, enabling data architectures to evolve continuously to meet user needs.

Simplifying analytics workflows

By removing the necessity for programming and complex SQL coding, these platforms empower domain and data experts to build and manage data warehouses. Surani adds:

“This feature eliminates the various handoffs that take place in a ‘waterfall’ approach. That of itself justifies the adoption of no-code tools.”

By enabling rapid prototyping and reviews, Centerprise’s no-code approach supports a continuous feedback loop that facilitates fast time-to-market, while ensuring that the resulting product always meets user needs.

“No-code tools provide a unified environment to build a data warehouse using a visual, point-and-click user interface,” adds Surani. “With their focus on user experience and productivity, these tools lower the skills requirements for building and managing a data warehouse. From being complex data stores accessible only to a few experts, data warehouses are now available to a much broader user base.”

The evolution of data warehouses is following a standard continuum, so businesses can expect more benefits, Surani predicts.

“Any enterprise technology curve starts out in the technological domain, then moves down to semi-technical and power users, before becoming directly available to ordinary users,” he explains.

“We can fully expect data integration, warehousing, analytics and visualization tools will evolve to be as useable as office productivity tools – and still provide the power and flexibility that today is known chiefly to data scientists and engineers.”

To learn more, visit: https://www.astera.com/products/centerprise-data/

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