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It is obvious that humans alone cannot derive optimal value from all of the data assets available today.
A quick disclaimer, though, is that technology can’t do it either, at least not by itself.
It is a symbiosis: technology helps people on the one hand; and people who guide technology towards a set, value-oriented goal.
We can cite a good use case with one of the generative AI tools leading the buzz today, Chat GPT.
Sequoia Anatomy of a generative AI application reveals that as systems like GPT chat receive more data from users, the model learns and adjusts.
One way of saying this is, “we augment the systems and it augments us”…quite simplistic but profound.
This article will discuss one concept of augmentation: SQL visualization, its role in the rise of today’s data engineer, and five categories of SQL visualization tools.
Gartner’s Equation of Value for 2022 and Beyond predicted a surge in the rise of people and decisions.
This trend emphasizes that in 2022 and beyond, composable and human-augmented solutions are the technologies that will form the backplane of progressive AI-centric systems.
You can dive into the full report here.
It’s early 2023 and we’re seeing more data science tools for citizens and augmentation technology for the data analyst/engineer character.
So how does all of this relate to SQL visualization?
We will explore more of this in the next section.
So what is SQL?
To understand the visualization of SQL, we need to review its suffix, SQL.
SQL, or Sequel as it is commonly called, is a 43-year-old query language recognized as one of the most common languages used in database technologies today.
stack overflow survey cites that more than 50 percent of developers use SQL.
It’s safe to say that SQL works.
So…
SQL visualization is facilitating the adoption of SQL processing using graphical and visual components.
In the era of multi-cloud, hybrid, and cloud DW deployments and transitions, there is a growing need for SQL visualization tools to help enterprises optimize their processes and data stacks. This is partly due to the following:
new complexities
As organizations take steps to streamline their data processes by leveraging new technology channels and data sources, new challenges and complexities arise.
The need for shorter iterations
Data consumers now more than ever expect relevant, real-time context around data sets.
Analysts also expect to integrate data quickly and with a fast approach without resorting to IT.
Overcoming de-democratized data preparation
Stakeholders expect more empowerment to carry out ad-hoc analytics and self-service analytics without deep technical knowledge.
Shortage of data engineers
Starting in 2022, Tamr, a leading brand in the data domain, highlighted the increase in demand for data engineers and the corresponding scarcity of DE resources.
Companies that focus their strategy on data engineering resources may encounter drawbacks.
I like to segment the various SQL visualization tools into four groups, namely:
SQL flow visualization tools
SQL lineage/flow tools provide deep analysis of SQL syntax and produce visually intuitive lineage results.
Similar to JSON crack, which is used to visually explore the API output in JSON format, sqlflow by Gudusoft is an automated SQL lineage visualization tool.
Image by SQLFlow by Gudusoft
SQL schema visualization tools
These SQL toolsets allow you to visualize your database designs online, in a collaborative and interactive environment. An excellent example of such a tool is SqlDBM.
SqlDBM is a development platform that allows companies to create databases online without writing code. This allows developers to focus more on the database model and less on the syntax.
Image by SqlDBM
SQL query visualization tools
These SQL visualization tools are used primarily for data modeling, transformation, and dispute activities.
A recognized tool in this category is data lake.
data lake is an all-in-one solution for exploring, preparing, visualizing, and cataloging Snowflake insights. Datameer allows engineers and data analysts to modify data directly in Snowflake through a simple SQL or no-code interface.
Image from Datameer
SQL panel tools
Our typical data lifecycle starts with base SQL queries. Models, ? dashboards However, tools like SQL dashboard software are revolutionizing this paradigm, dramatically shortening the time to insight for ad-hoc analysis and prototyping.
An excellent example of such a tool is Dome.
Domo is visualization software that makes a direct connection to the data where it resides, feeding your vital metrics with up-to-the-minute data for quick analysis.
Dome Image
We have come to the end of my curated list of SQL visualization tools for the data engineer/analyst… I hope you found this useful.
If you’re a data analyst or an engineer working with a cloud analytics stack, I highly recommend trying any of these.
As for testimonials, I frequently use data lake for low-code transformation and, sqlDBM to facilitate database modeling in my snowflake environments. So I can vouch for your claims of increasing the “data engineer and analyst persona”.
If you have any recommendations outside of these categories, please mention them in the comments; We look forward to hearing your opinion.
Rev Antonio he is a senior BI analyst and tutor. She enjoys educating through writing related to business intelligence and business analysis.
