The Resilience of SQL: Why It Remains Indispensable in Data Science
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Chapter 1: The Enduring Relevance of SQL
In recent years, there was a prevailing notion that while SQL remained essential for managing relational databases, it was being overshadowed in data analytics by languages like R and Python, as well as by user-friendly tools. Additionally, traditional relational databases faced competition from NoSQL alternatives, presenting challenges for SQL enthusiasts.
Section 1.1: The Unwavering Need for SQL
The key reason SQL continues to thrive is quite straightforward: relational databases are still critical for structured data management. This type of database remains the most efficient solution available. Modern data warehouses, such as Google BigQuery and Amazon Redshift, still rely on SQL for their operations.
SQL developers need not worry about job security. Data Engineers incorporate SQL in their data pipelines, while self-service BI users utilize it to construct dashboards. This trend is likely to grow. Moreover, there is a rising demand for product owners and project managers with SQL expertise, enabling them to make data-driven decisions within their organizations.
Subsection 1.1.1: SQL's Expanding Horizons
Section 1.2: SQL in Machine Learning Applications
The applications of SQL are broadening significantly. For instance, it can now be utilized for developing machine learning models. Data Scientists have long relied on SQL to pull data from databases, but they can now apply it to create AI use cases. Google BigQuery ML exemplifies this by allowing users to create, train, and deploy models using SQL.
The approach known as "Bringing the Compute to the Data" or "Bringing Machine Learning/Algorithms to the Data" is becoming increasingly popular. Major providers like Google, Amazon, and Microsoft are integrating database and data warehouse services with machine learning, eliminating the need to transfer data to other platforms or systems.
Chapter 2: The Future Landscape of SQL
This video explores the resurgence of migrations-based deployments in SQL, highlighting its ongoing significance in modern development practices.
In this short clip, we examine SQL literacy and how its relevance continues to thrive in the face of emerging technologies.
Summary
Despite the myriad of trends and programming languages emerging in the data landscape, SQL remains a crucial skill. The introduction of machine learning services has only solidified its importance. If you're interested in how business professionals and managers can leverage these tools, further insights can be found here.
Sources and Further Readings
[1] Google, What is BigQuery ML? (2020)