SQL, or Structured Query Language, has a long history from the 1970s. It was designed to work with relational database operating systems (RDBMS) and simplify data operation tasks. The development of SQL can be attributed to IBM, particularly its San Jose Research Laboratory.
IBM’s experimenters, under the leadership of Edgar F. Codd, played a vital part in this development. They formulated a fine model for representing and handling data in an irregular format, known as the relational model.
In this article, we will explore the new and updated features introduced in SQL Server 2022 (version 16. x).
The importance of the Azure Synapse link for SQL is as follows:
With this advantage, Synapse Link for SQL gives us a competitive edge in the market by enabling us to stay ahead and make informed choices promptly.
Azure Synapse Link for SQL utilizes “change feed technology” to capture new or altered data from Azure SQL Database and SQL Server 2022. It operates like a smart system, recognizing changes and sending only updates to Azure Synapse Analytics' SQL pool.
This seamless process doesn't interrupt daily activities, allowing uninterrupted real-time analytics. It enables obtaining insights while ensuring smooth operations—a game-changer for organizations managing real-time analytics alongside everyday tasks, harmonizing data insights and daily operations effortlessly.
This remarkable feature in Azure Synapse Analytics eases monitoring data updates without the hassle of complex data transfer setups. When changes occur in Azure SQL Database or SQL Server 2022, it streamlines the transfer process (ETL), freeing you from intricacies.
It lets you focus on analysis and reporting while it ensures seamless, efficient work—providing access to the latest data effortlessly, making your workflow smoother and more productive.
The Managed Instance Link facilitates on-premise SQL server connection to Azure SQL Managed Instance—a secure, real-time data replication service.
It ensures constant database accessibility and minimal loss during failures, creating distributed availability groups between servers and supporting single and multiple-node systems seamlessly. Secure VPN or Azure ExpressRoute establishes on-premise network linkage to Azure, using authentication certificates for key exchange.
Up to 100 different or identical SQL server sources can link to one Azure SQL-managed instance, with connection control managed by the instance. Similarly, a single SQL server can connect to an Azure Managed instance, streamlining data integration and ensuring robust accessibility.
The following are new updates in SQL:
In today’s world, where databases are very important for critical business operations, the capability that assures uninterrupted database operation is very crucial. The database administrators must find ways to handle system failure and maintenance windows. There are special techniques that allow resuming and pausing critical tasks like ALTER TABLE and ADD CONSTRAINTS.
Importance of Resumable SQL Operations
Minimizing Downtime
When you add constraints with ALTER TABLE, it can lock tables, affecting ongoing changes. Admins carefully plan maintenance tasks, using pause/resume operations to minimize disruptions in vital business environments.
Handling System Failures
System crashes are unpredictable and jeopardize data consistency in SQL operations. Resumable actions, such as pausing ALTER TABLE during failure and resuming after recovery, uphold data integrity by seamlessly continuing from the point of interruption.
Start the ALTER TABLE operation with the RESUMABLE option, specifying the desired constraint.
Example:
During the operation, if you need to pause it (e.g., for maintenance or system updates), you can do so using the following statement:
After the pause, you can resume the operation when it's convenient. This can be done using the following statement:
The window function, combined with the partition and order by clauses in SQL, enables partitioning data into subsets and arranging rows within those subsets, influencing the behavior of the function.
This window is customizable with PARTITION BY to group data, ORDER BY to sort data, and ROW to define the range of rows the window function works on. It helps you perform advanced calculations on specific parts of your data with precision.
Example:
In this query, we're selecting columns ProductName, SaleDate, and
SaleAmount from the “Sales” table.
The ROW_NUMBER() function is used to assign a unique row number to each row.
We specify PARTITION BY ProductName, which means that the row numbering resets for each unique product name. In other words, it assigns row numbers separately for each product
We use ORDER BY SaleDate to determine the order of rows within each partition, so the rows are numbered by sale date, from the earliest to the latest.
The AS RowNumber alias gives a name to the new column that holds the row numbers.
The result of this query will give you a list of sales data with a unique row number for each sale within each product category.
This is useful for tasks like identifying the first sale for each product or tracking the order in which sales occurred.
Here's a simplified example of what the result might look like:
Output:
In this result, you can see that each sale for a product is assigned a unique row number based on the sale date and is reset when a new product is encountered. This is just one example of how window functions can be used for more advanced and specific data analysis within your SQL queries.
Time series functions in SQL are used to work with data that changes over time. These functions help with tasks like time-windowing, aggregation, and filtering time-based data.
Below are some common time series functions in SQL, along with examples:
DATE_BUCKET() Function in SQL
Transform your sales data effortlessly! DATE_BUCKET() organizes sales into monthly buckets, simplifying analysis and granting your data a tidy, understandable makeover.
GENERATE_SERIES() Function in SQL
This function generates a series of values within a specified range. It's often used to create a sequence of dates or timestamps.
Example: Suppose you want to generate a series of dates for the next 7 days:
Output:
FIRST_VALUE() and LAST_VALUE() in SQL
Alright, imagine you've got a lot of rows in your “Orders” table. In which you have order IDs and their corresponding order dates.
So, you use FIRST_VALUE() to pluck out the very first order in your sorted list, just like snagging the front seat at a blockbuster movie. On the flip side, LAST_VALUE() grabs the last order in the lineup,
With these functions, you're not just staring at a pile of orders; you're pinpointing the first and the last rows of your data
Example using FIRST_VALUE():
Suppose you want to find the first order date for each order in the table.
Output:
Example using LAST_VALUE():
Now, let's find the last order date for each order in the table.
In this query, we're using LAST_VALUE() to retrieve the last order date within the entire result set, ordered by OrderDate.
The result will look like this:
Output:
In both examples, you can see how FIRST_VALUE() and LAST_VALUE() functions retrieve the first and last values within a specified window, providing valuable information for analyzing your data.
JSON functions in SQL revolutionize data handling by enabling structured storage and manipulation of JSON (JavaScript Object Notation) data within a relational database. With its powerful functions, SQL embraces flexibility, allowing seamless integration of semi-structured data, ushering in a new era of database adaptability.
ISJSON() Function in SQL
ISJSON() in SQL validates JSON strings, it returns 1 if the string is aligned with JSON standards. A deviation from the expected JSON structure results in ISJSON() returning 0.
Example: Let's say you have the following string.
First, you declare a variable @jsonString and assign it the JSON string you want to validate.
You can then use the ISJSON() function to check whether the @jsonString is valid JSON.
In this code, the ISJSON() function is applied to the @jsonString. The result is stored in a column named "IsValidJSON."
Output:
The 1 indicates that the string is indeed valid JSON.
JSON_PATH_EXISTS() in SQL
Imagine you have a JSON object at hand. Now, enter JSON_PATH_EXISTS()—your investigative tool for verifying the existence of a particular attribute within this digital profile, say, the crucial "email" field.
In a structured and systematic manner, JSON_PATH_EXISTS() scrutinizes the JSON hierarchy, determining whether the specified path, in this case, the path leading to "email," is present or not. Its response is clear-cut, delivering a bit value of 1 if the path is found and 0 if it eludes detection.
Example: Let's say you have a JSON object representing a user's profile:
In the code snippet provided, there is a strategic utilization of the JSON_PATH_EXISTS() function, focusing on the @jsonProfile JSON object. The targeted investigation involves scrutinizing the presence of a specific JSON path, precisely defined as email.
In this code, the JSON_PATH_EXISTS() function is applied to the @jsonProfile JSON object, and the path $.email is specified. The result is stored in a column named "EmailExists."
Output:
The 1 indicates that the JSON path $.email is present within the JSON object.
In essence, this report explores crucial updates in SQL, spotlighting how Azure Synapse Link revolutionizes integration and robust availability practices. It highlights how operations like ALTER TABLE make modifications seamless, while window functions and time series analysis offer deep insights. JSON functions adeptly handle the rise in semi-structured data, ensuring precision. Overall, it showcases SQL's flexibility, strength, and ongoing importance in efficiently managing and analyzing data.
