Benefits of No-Code Tools to the SQL Data Engineer
- John Morrell
- July 6, 2022
The market for no-code tools is growing dramatically. Whether in mobile development, application development, digital transformation, process re-engineering, or other areas, no-code is gathering momentum in many different markets.
And now you have options for no-code tools in your data stack, particularly for data transformation. While no-code data transformation tools benefit the non-programming analyst community, they also offer advantages and benefits to the SQL development-focused data engineering teams.
Let’s explore how a data engineer and data engineering team would benefit from no-code data transformation.
The Role and Value of the Data Engineer
Online career-building site Coursera defines the role of the data engineer as “the practice of designing and building systems for collecting, storing, and analyzing data at scale.” Typical tasks for a data engineer are items like acquiring datasets that align with business needs, transforming data into useful, actionable information, and building, testing, and maintaining database pipelines, among other things.
An important and unique skill data engineers bring to the table is their ability to program data pipelines and transformations in SQL combined with languages such as Python and Scala, which Coursera lists as the top skill to hone in data engineering. But it is important to recognize that the true value of the data engineer is not that they can code SQL, Python, and Scala. This unique and valuable skill has been necessary because the available tools in their data stack have required those skills.
The true value data engineers bring to the table is their knowledge of data, what they do with the data, and how effectively they deliver data to the business. To be successful in their jobs, data engineers need to have unique knowledge and skill in:
- Having domain knowledge about various datasets, how they should be used, and delivering this knowledge to the business,
- Optimally structuring, shaping, and managing data for the business that meets their unique analytics needs, and
- Running data operations at scale to ensure the business gets a constant flow of data to execute effectively.
Essentially, a data engineer is a lynchpin that ensures the organization gets more value from its data treasure troves. As such, data engineering teams require tools that allow them to focus on the true value they bring to the organization.
Benefits of No-code Data Transformation
Some of the benefits of no-code data transformation tools are similar to those found in any no-code tool. This includes agility, easier testing and maintenance, greater reuse, and the elimination of communication gaps.
Specifically for no-code data transformation, the benefits are:
- Much greater agility and speed in analytics cycles by eliminating lengthy coding processes for data transformation so data engineers can turn projects around in a fraction of the time versus coding.
- The testing and troubleshooting of data transformation models become much faster and easier through interactive data profiles and testing automation for each time dataflows are executed.
- Through the underlying platform embedded in no-code tools and integration with cloud data warehouses such as Snowflake, maintenance and updates are faster and much easier
- Via an interactive, searchable catalog of data transformation models, data engineers can gain greater reuse of models making dataflows more efficient and further accelerating new projects.
- Communication gaps and requirements misalignments are eliminated through collaborative processes with the analytics community and business stakeholders.
- Through rich, catalog-like data documentation, knowledge about the data can be offered and shared among data engineers and the analytics community to increase data literacy and proper use of the data.
It is also important to recognize that the ideal no-code toolset for data transformation does not ignore SQL. In fact, a good no-code tool would also offer a SQL notebook-style interface and the no-code one. This allows data engineers to drop down and write SQL on cases where the no-code operations cannot effectively or optimally express the data model at hand. All models, whether no-code or written in SQL, are managed and operated with the data transformation platform, therefore gaining the cataloging, reuse, and collaboration benefits.
Focusing on the True Value of the Data Engineer
So, what’s in it for the individual data engineer and the overall data engineering team? If no-code tools reduce the need for their unique SQL, Python, and Scala tools, does no-code reduce their value to the organization?
Absolutely not. In fact, no-code tools help data engineers and the data engineering team deliver more value to the organization and allow them to focus on the true value they bring to the organization that we discussed above.
The data engineering team will deliver more value by executing analytics projects faster, maintaining models more effectively, and delivering more projects to the business that drive greater analytics adoption. Through collaborative communication with the data engineer’s stakeholders, the analytics community, projects and processes will go more smoothly. In addition, the increased productivity of the data engineering team will also help keep overall data engineering costs stable.
But the real benefit to the data engineer is that they can spend far more time on tasks where they truly add value to the business, including:
- Using their domain knowledge about various datasets to help the analytics community better understand how the data should be used and delivering this knowledge to the business,
- Further using their unique knowledge of the data, data engineers can properly structure, shape, and manage the data for the business to their analytics needs, and
- Efficiently and reliably running data operations at scale, ensuring that the business can get data on time as needed so they can make fast and sound business decisions.
Through no-code tools, data engineers deliver all the knowledge and skill they have around data and its applicability to analytics and the business. Hence, organizations get much greater use and value from their data.
Many programming-centric professionals, including data engineers, are wary of no-code tools. They often may feel that no-code tools will diminish the need for their unique programming skills.
But in fact, data engineers should embrace no-code tools to let them focus on the true value they bring to an organization: their unique knowledge of the data, how to shape and bring it together, and delivering a continuous flow of data to the business. No-code tools will also accelerate each analytics project and fuel the delivery of more data to the business, making their business stakeholders much happier.
Datameer enables data engineers and analysts to mix and match SQL with no-code operations to transform data into trusted, analytics-ready data in Snowflake for analytics, AI, and ML. The multi-persona UI brings together your entire team – data engineers, analytics engineers, analysts, and data scientists – on a single platform to collaboratively transform and model data. Catalog-like data documentation and knowledge sharing facilitate trust in the data and crowdsourced data governance. Datameer is native to Snowflake to keep data secure, and leverage Snowflake’s scalable compute and storage to keep cloud costs low.
Are you interested in learning more about Datameer and how it can deliver richer data documentation, agility, and collaboration for the “T” in your modern ELT data stack? Please visit our website or Sign up for your free trial today!