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The routine of storing data comes with its own complexities. With the increase in volume, optimizing the data archiving strategy becomes essential. In this blog post, we will talk about Databricks and how we can leverage SQL for efficient data archiving.
Understanding Data Archiving
Data archiving pertains to the process of moving data, no longer actively used but is required for record-keeping or future reference, into a storage that is cost-effective and efficient. It plays a vital role in optimizing the performance, maintaining regulatory compliance, extracting valuable business insights, and reducing costs associated with maintaining large volumes of data.
Why Databricks?
Databricks provides the Unified Data Analytics Platform that is a cloud-based service designed to make the process of big data analysis simpler and more collaborative. With Databricks, organizations can achieve high performance through its apache spark-based cloud environment, providing sophisticated ways of data archiving.
Writing SQL Code for Data Archiving in Databricks
Before we begin, ensure you have databricks CLI and SQL installed on your machine.
Step 1: Creating a table
Firstly, we’ll need to create a table with some data. Let’s create a Sales table with SalesID, Product, Quantity, and SaleDate.
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CREATE TABLE Sales ( SalesID INT PRIMARY KEY, Product VARCHAR (100), Quantity INT, SaleDate DATE); |
Step 2: Archiving old data
Let’s say we want to archive all sales records that are older than 3 years. We can achieve this by moving these records from the ‘Sales’ table to the ‘Sales_Archive’ table. Here is the SQL command to do it:
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INSERT INTO Sales_Archive SELECT * FROM Sales WHERE SaleDate < DATEADD(year, -3, GETDATE()); DELETE FROM Sales WHERE SaleDate < DATEADD(year, -3, GETDATE()); |
The first command moves the records to the ‘Sales_Archive’ table and the second command deletes these records from the ‘Sales’ table.
Step 3: Automating the process
To automate the archiving process, we can create a stored procedure and schedule it to run at fixed intervals. Here is how you can do it:
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CREATE PROCEDURE Archive_Sales_Data AS BEGIN INSERT INTO Sales_Archive SELECT * FROM Sales WHERE SaleDate < DATEADD(year, -3, GETDATE()); DELETE FROM Sales WHERE SaleDate < DATEADD(year, -3, GETDATE()); END GO |
Conclusion:
In conclusion, archiving your data properly can save resources and provide a more efficient environment for data analytics. Databricks, in association with SQL, can streamline this process, making it more effective and easy to handle.
Stay tuned for more blog posts on efficient data handling! Happy Coding!