For an instructor lead, in-depth look at learning SQL click below.
Introduction
Data quality is an integral aspect of data analytics and making business decisions. Inaccurate or poor quality data can lead to misleading results and wrong business conclusions. In this blog, we delve into the importance of SQL data cleansing in Databricks to ensure data quality.
What is Data Cleansing?
Data cleansing, also referred to as data cleaning, involves identifying and correcting or removing errors or inconsistencies from datasets. This process is crucial, especially in large databases where manual verification is impractical.
SQL Data Cleansing in Databricks
Databricks is a unified data analytics platform that provides a collaborative space for data science and engineering. SQL is a standard language understood by most relational databases, and it can be used effectively for data cleansing in Databricks.
Removing Duplicate Entries
|
1 2 3 4 |
SELECT DISTINCT * FROM table_name; |
In the above code, the keyword DISTINCT is used to eliminate duplicate rows from the result set returned by the SELECT statement from the table ‘table_name’.
Correcting Data Types
|
1 2 3 4 |
ALTER TABLE table_name ALTER COLUMN column_name TYPE new_data_type; |
This code snippet allows you to alter the data type of a column in the ‘table_name’ table. This is especially important when the data type of a column wasn’t set correctly during the table creation phase.
Checking Data Quality
Ensuring data quality demands more than just cleansing. It’s equally significant to carry out checks on your data regularly. The following example illustrates how.
Verifying NULL Values
|
1 2 3 4 5 |
SELECT * FROM table_name WHERE column_name IS NULL; |
This SQL statement selects all rows in ‘table_name’ where ‘column_name’ is NULL. This is an effective way to track missing data in your database.
Conclusion
SQL provides many more functionalities that aid in data cleansing and also in validating data quality. The same can be implemented in Databricks for effective data analytics. Ensuring the accuracy of your data is vital for the success of any data-driven decision-making process. Start finding and correcting data inaccuracies in your databases today!