No-Code Queries Can Accelerate AI and Data Analytics
In this article, see how no-code queries can accelerate AI and data analytics.
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The low-code, no-code methodology is becoming highly sought-after throughout the modern IT ecosystem—and with good reason. Options that minimize manually writing code capitalize on the self-service, automation idiom that’s imperative in a world in which working remotely and doing more with less keeps organizations in business.
Most codeless or low-code approaches avoid the need for writing language-specific code and replace it with a visual approach in which users simply manipulate on-screen objects via a drag-and-drop, point-and-click interface to automate code generation. The intuitive ease of this approach — which is responsible for new standards of efficiency and democratization of no-code development — has now extended to no-code query writing.
No-code querying provides two unassailable advantages to the enterprise. First, it considerably expedites what is otherwise a time-consuming ordeal, thereby accelerating data analytics and AI-driven applications and second, it can help organizations overcome the talent shortage of developers and knowledge engineers. Moreover, it does so by furnishing all the above benefits that make codeless and low-code options mandatory for success.
The SQL and Data Warehouse Challenges
It’s remarkable how many lines of code are included in complicated SQL queries, not to mention the complexity captured in most data warehouses. The amount of time, effort, and energy required to understand these architectures is anything but trivial. Despite the in-roads made by newer technology approaches, SQL remains a mainstay for many organizations, even for certain data science use cases.
Manually coding a SQL query with complex joins can easily encompass reams of pages, taking days (and sometimes weeks) to get right. This option simply isn’t feasible in the modern world of low latent data within the AI, data-driven and Internet of Things applications that are rapidly gaining prevalence across industries.
Rapid Query Generation
Codeless querying resolves this vital temporal issue so organizations can focus on information gleaned from queries instead of simply writing them. It masks the underlying complexity of the query writing process via a visual interface that issues results in minutes instead of workdays and workweeks. Users can always view the generated code and modify it as necessary for their specific needs, but in many verticals like healthcare, financial services, or telecommunications, they won’t have to.
For example, medical practitioners can quickly look across a range of patient criteria (specific to demographic information, type of diagnosis, treatment options, and their sequence) with codeless querying to get a basic understanding of the best way to treat asthma patients with Type 2 diabetes for women under 25 via codeless querying. Without it, this undertaking would monopolize users’ time, which could negatively affect patients requiring care in the meantime.
A Tool for the Talent Shortage
Organizations have been contending with the dearth of talent in various programming languages for some time. Recent data indicates there were 330,000 more open computing jobs than computer science graduates joining the workforce a couple years ago. This shortage of talent for writing code extends to developers, data scientists, and data engineers. No code querying empowers non-technical users to create and run graph queries, similar to visual data modeling. Adopting no-code tools throughout the data landscape can help organizations overcome these talent gaps so the fast pace of innovation can continue.
Empowering the Business
No-code query tools can help firms surmount the talent shortage obstacle within data science and application development. In doing so, they democratize data analytics to include non-technical users without scarcely found degrees in data-intensive subjects. Thus, organizations can maximize the use of their existing talent structures by expanding the scope of work of a business analyst, for example, to include querying data for the data discovery process for analytics tools.
Additionally, they can substantially help the developers, data engineers, and data scientists they already have by enabling them to perform query writing tasks in a fraction of the time and at scale to amplify the value these precious human resources deliver to the enterprise.
The Codeless Query Proposition
Options for no-code and low-code querying are the postmodern answer to accelerate the speed at which complicated queries traditionally take while overcoming the lack of talent required to manually script languages and other data science necessities. They empower organizations to accomplish more with the resources they have in an automatable, self-service way.
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