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Databricks SQL Data Lakes have radically changed the way we approach data analytics. They allow us in managing and analyzing extensive datasets efficiently, thanks to the combination of on-demand querying, automated lifecycle management, and enterprise-level security. By harnessing the capabilities of Apache Spark, Databricks SQL Data Lakes offer the potential to process petabytes of data and conduct analyitcs seamlessly. In this blog post, we’ll explore some basic SQL commands that can be used for managing and querying data in a Databricks SQL Data Lake environment.
Creating Tables
Let’s start by creating a simple table that will hold our data:
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CREATE TABLE Employees ( ID INT PRIMARY KEY, Name NVARCHAR(50), Age INT, Address NVARCHAR(255), Salary DECIMAL(18, 2) ); |
Inserting Data
Once the table is created, you can insert data into it:
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INSERT INTO Employees (ID, Name, Age, Address, Salary) VALUES (1, 'John', 30, '1234 Main st', 50000.00); |
Selecting Data
To view the data held inside your table, you can execute a SELECT query:
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SELECT * FROM Employees; |
Aggregating Data
Aggregate functions such as COUNT, SUM, AVG, MAX, and MIN can give you useful insights about the data. Here’s how to find the average salary:
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SELECT AVG(Salary) FROM Employees; |
Deleting Data
The DELETE statement is used to delete existing records in a table:
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DELETE FROM Employees WHERE ID = 1; |
Conclusion
With SQL commands such as the ones above, managing large-scale analytics becomes a more manageable task with Databricks SQL Data Lakes. While the examples given are fundamentally basic, they can provide the basis for more complex queries and data manipulations. So keep practicing, keep querying, and conquer your data!