Data Analytics, data science, machine learning has come a long way and are progressing at a break-neck speed. With constantly developing technologies, it is important to keep ourselves updated with newer skills from time-to-time. Regardless of all the array of tools, SQL remains an all-time favourite. Not just for analysis of data, it also has its wings spread out on ML. SQL has had tremendous usage in the past few years. It has several advantages over other programming languages.
What makes SQL so favourite
One of the reasons why SQL is still popular is because it is easily comprehensible and user-friendly. It runs in local systems, intranet and internet. Being open source, SQL databases for example, MySQL, RavenDB, MariaDB, PostGres can be used at low cost and with large communities. All vendors who develop DBMS use SQL.
SQL has rich transactional support. It can simultaneously handle large records and manage several transactions. SQL is highly secure. It can give specific permissions on views, procedures, and tables. It can create databases; inserting records into a database; run queries against it; create views; delete records; retrieve data; update records; create solid stored procedures in a database.
Machine Learning using SQL
As data engineering has moved to cloud, so has SQL evolved in its own way. Data scientists look up to the best BI tools and ML extensions that help in data preparation, model creation and model training. Usually, ML models require the data to be extracted from the database, then transformed and loaded into the BI system and then its loaded in the ML tools. Lastly, it is transported back to the BI tool for visualization.
Using SQL, we can leverage strong out-of-the-box analysis and run algorithms without extracting data from the database. Here, in-database ML or Machine Learning services come into play. Machine Learning Services is a feature in SQL Server that helps to execute Python or R scripts in-database. Besides paid versions, open-source packages are mostly pre-installed in the Machine Learning Services. This feature runs script where the data resides and eliminates the need to transfer data across servers.
With the help of in-database ML, anyone having SQL knowledge can work within the database, create models, run them, query them like database tables, etc. In-database ML helps in democratizing data by breaking down technical barriers and making the data accessible to data scientists or engineers who understand the data.
Data Preparation using SQL
The simplest way for retrieving data is by using SELECT clause. In this projection, we can filter the columns needed using WHERE and get to know how the actual data looks like. SQL views helps to decode the data complexity in a clean, secure and easy format. Another advantage of views is, it can mask or hide private data elements and provide to only those having access to the data.
Building Data Models
PostgreSQL queries can run on a variety of ML algorithms. Some of the popular machine learning algorithms can be written entirely in SQL code.
The most elementary technique being Linear Regression. The regr_slope() and regr_intercept() functions are used to estimate the gradient and intercept terms respectively.
In case of K-Nearest Neighbours where the objective is to classify the x-values using KNN algorithms, PostgreSQL’s point() data type and distance operator helps to calculate the distances between the data.
Similarly for K-means clustering, the unsupervised algorithm which does not require labelled data, one can make use of PostgreSQL’s geometric data types and operators to model the data. It also uses a recursive query to iteratively recalculate the centers of each cluster up to a maximum number of iterations.
Conclusion
In short, SQL is a massive technology that is developing and expanding in other data-centric fields. SQL query engines try to simplify the distributed systems by applying simple commands. Eventually, every organization requires a database to manage their customer’s data. So, there are numerous reasons why SQL will always be available for efficient analysis.
