"What are the differences between NoSQL and SQL databases?" is a question you've undoubtedly heard if you've taken part in a lot of interviews. You will gain a thorough understanding of these distinctions and when to pick one over the other from this essay.

As a software developer, you may have already worked with various SQL databases such as T-SQL, PostgreSQL, MySQL, and others. What's the first thing you've likely noticed? The "rules" or the "schema," of course. SQL databases have highly structured data models. You create tables with defined columns and rows, strictly following a predetermined schema. Breaking this structure means violating the fundamental principles of SQL. Tables, columns, rows, and data types form the essential building blocks for organizing your data.

On the other hand, when you work with NoSQL databases (non-relational databases) like Azure Cosmos DB, Aurora, or MongoDB, you have the flexibility to frequently modify your data model. NoSQL databases don't enforce a rigid structure. They provide an "elastic" schema, allowing you to store data in various formats. Instead of sticking to the traditional table representation, you can use document-based, key-value-based, graph-based, or column-based models, among others.

For relational databases, developers usually apply normalization (such as first normal form, second normal form, etc.) to ensure a clear and balanced data structure. As a result, relational databases often rely heavily on multi-table JOINs, aggregations, and complex relationships to retrieve data. However, when datasets become large, it can be challenging and inefficient to manage and retrieve data quickly from relational databases.

Unfortunately, relational databases aren't inherently designed to handle massive volumes of data. They follow a "scale-up" approach, meaning they require more resources such as RAM, CPU, and GPU to handle increased data.

NoSQL databases, however, are designed for "scaling out." This means you can distribute and handle data across multiple servers without negatively impacting performance. Many people associate "NoSQL" with "Big Data," often using these terms interchangeably. Indeed, you can consider the term "NoSQL" a buzzword frequently associated with solving big data challenges.

Behind NoSQL Lies the 3V Principle

• Volume
• Velocity
• Variety

Let's examine each of these elements individually to understand their importance.

Volume refers to handling massive datasets, reaching terabytes, petabytes, and beyond. Thanks to the "scale-out" design, NoSQL databases comfortably manage vast amounts of data without issues. SQL databases, by comparison, often struggle with such extensive data sets due to limitations in hardware scaling and structured data constraints, making them less efficient for extremely large data scenarios.

Velocity is about throughput—handling massive amounts of simultaneous requests quickly and efficiently. NoSQL databases excel at processing high-velocity data streams, which is crucial for applications like social media feeds, real-time analytics, IoT applications, and more. SQL databases may experience bottlenecks due to their rigid schemas and transaction overhead, slowing down performance in high-throughput situations.

Variety emphasizes schema flexibility. You can utilize any of the schema forms mentioned previously or even choose a schema-less approach entirely. This schema flexibility means NoSQL databases can easily accommodate rapidly evolving data requirements, different data formats, and unstructured or semi-structured data like images, videos, and sensor data. Conversely, SQL databases are best suited for structured and consistent data that doesn't frequently change.

Let's explore more internal details between them.

• Transactions and ACID Compliance: SQL databases generally offer strong consistency and ACID (Atomicity, Consistency, Isolation, Durability) compliance. NoSQL databases often sacrifice strict ACID compliance for scalability and flexibility, adopting eventual consistency models.
• Complex Queries and Reporting: SQL databases excel in executing complex queries, and multi-table joins, and providing extensive reporting capabilities. NoSQL databases might require additional processing layers or specialized query mechanisms for complex analytical queries.
• Scaling Approaches: SQL databases typically scale vertically (adding more resources to a single server), while NoSQL databases scale horizontally (adding more servers), providing more flexibility and efficiency for handling large datasets.

Understanding these differences and key characteristics will help you choose the right database solution based on your specific requirements. The best measure for your application is your context. The application context defines which one is perfect for you.

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Imagine you have a large database that stores customer orders, and you need to keep another system like a reporting dashboard — updated in real-time. Instead of repeatedly scanning the entire database for changes, which is inefficient, you can use Change Data Capture (CDC). CDC is a technique that tracks changes made to a database and ensures they are captured and passed on to other systems efficiently. It helps keep data in sync without causing a heavy load on the database.

Why Does CDC Matter?

• Reduces System Load: Instead of checking all records, CDC tracks only what has changed.
• Ensures Data Synchronization: Keeps different databases or applications in sync with real-time updates.
• Enhances Performance: Speeds up data processing and reduces unnecessary computations.
• Supports Real-Time Analytics: Enables event-driven architectures and live dashboards.

Different Types of Change Data Capture
There are multiple ways to implement CDC, and the right approach depends on your system’s needs. Let’s look at the common types:

1. Trigger-Based CDC
This method uses database triggers, which are special rules that get executed when data changes. Whenever a row is inserted, updated, or deleted, the trigger captures this change and logs it in an audit table.

When to Use:

• If your database does not support log-based CDC.
• When you need to maintain a detailed history of changes

Pros:

• Works even if your database doesn’t have built-in CDC features.
• Provides a complete history of data changes.

Cons:

• Can slow down database operations since triggers add extra processing.
• Increases database complexity with additional tables and logic.

2. Log-Based CDC
This approach reads the database transaction logs — the records of every change made to the database. Instead of modifying the database structure, it monitors changes at the system level.

When to Use:

• When you need real-time CDC with minimal performance impact.
• When dealing with high-volume transactional databases.

Pros:

• Has the least impact on database performance.
• Efficient for handling large data volumes.

Cons:
Requires access to database transaction logs, which not all databases allow.
More complex to set up and configure.

3. Timestamp-Based CDC
This method relies on a timestamp column (like “LastUpdated”) to identify changed records. When a query runs, it fetches all rows where the timestamp is newer than the last sync.

When to Use:

• If your tables already have a “Last Updated” timestamp column.
• When you need a simple CDC method without extra database configurations.

Pros:

• Easy to implement.
• No need for additional infrastructure.

Cons:

• Requires timestamps to be updated accurately, or changes might be missed.
• Not ideal for real-time processing, as it relies on scheduled queries.

4. Table Differencing (Snapshot-Based CDC)
In this approach, periodic snapshots of the entire table are compared to detect differences. Changes are identified by comparing the current state to a previous snapshot.

When to Use:

• When other CDC methods are not feasible.
• When working with small datasets where performance impact is minimal.

Pros:

• Works with any database, even those without CDC support.
• No need to modify the database structure.

Cons:

• Requires scanning the entire table, which can be slow.
• Not suitable for large datasets or real-time updates.

5. Hybrid CDC
A combination of multiple CDC methods to balance performance and accuracy. For example, log-based CDC might be used for real-time changes, while timestamp-based CDC acts as a fallback.

When to Use:

• When handling complex architectures with different data sources.
• When optimizing for both real-time and periodic data updates.

Pros:

• Offers flexibility to choose the best method per use case.
• Can improve reliability and accuracy.

Cons:
Requires a more complex setup and maintenance.

Conclusion
Choosing the right CDC method depends on factors like performance needs, database capabilities, and update frequency. Log-based CDC is preferred for real-time, high-volume systems, while timestamp-based CDC is a quick solution for simple use cases. Trigger-based CDC is useful when detailed change tracking is required, and table differencing can be a last resort when no other options are available. By selecting the right CDC approach, businesses can keep their data synchronized efficiently, enabling faster decision-making and better performance across applications.

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In SQL Server, a lot goes on behind the scenes when you execute a query or invoke a stored procedure. We'll use a special visual model to explain how SQL Server handles both.

The Four Primary Steps Each Query Takes
Whether it's an Normal SQL query or a stored procedure, SQL Server follows the same general flow.

• Parsing: SQL Server checks for typos and turns your query into a tree structure it can understand.
• Algebrizing: It links your query to actual tables and columns, and checks for things like permissions.
• Optimization: SQL Server builds a plan for how it thinks the query should run choosing indexes, join types, etc.
• Execution: It runs the plan and returns the results.

The Plan Cache: SQL Server’s Memory Trick
To save time, SQL Server stores execution plans in memory (called the plan cache). If a query or procedure has already been run, SQL Server can reuse that plan instead of making a new one.

• Raw SQL Query: SQL Server saves each unique query. Even small changes (like a space or value) create a new plan.
• Stored procedures: These are compiled once and reused, so they’re better at plan reuse.

Inside the SQL Server Engine
Let’s break down the key parts involved in running queries.

• Plan Cache: Stores execution plans.
• Buffer Pool: Stores data pages from disk in memory so SQL Server can access them quickly.
• Execution Engine: The part that actually runs the query plan and processes the data.

Normal SQL query and stored procedures both use all these pieces, but stored procedures often make better use of caching.

Examples
Raw SQL Query.
SELECT * FROM Orders WHERE CustomerID = 'PETER';

Equivalent Stored Procedure.
CREATE PROCEDURE sp_GetOrdersByCustomer
    @CustomerID NVARCHAR(5)
AS
BEGIN
    SELECT * FROM Orders WHERE CustomerID = @CustomerID;
END;

Running this many times with different values.

• The Normal SQL query version creates multiple plans.
• The stored procedure reuses the same plan with different values.

Side-by-Side Execution Flow

Note: Unless there are schema updates or recompilation triggers, stored procedures bypass parsing, name resolution, and optimization after the initial execution.

Key Differences

Conclusion

Normal SQL queries and stored procedures both help us get data from the database, but they work differently behind the scenes. While both stored procedures and standard SQL queries assist us in retrieving data from the database, their inner workings differ. Every time a standard query is executed, it undergoes parsing, verification, and planning. This is done only once using a stored process, which also saves the plan for later usage, making it faster. When you need to execute the same logic repeatedly or want greater control and security, stored procedures are preferable. Writing better, faster, and safer SQL code is made possible by understanding how both are handled.

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What is a SQL Server?
Microsoft's SQL Server solution is intended for data management and storage. Imagine it as a digital filing cabinet that is extremely well-organized. Any software or application that requires information sends a request to SQL Server to extract the information. Because of its large data processing capacity, it may be applied to both small and large projects.

A Brief History of SQL Server
SQL Server was first brought to the market by Microsoft back in 1989. This was developed at that time in cooperation with another company named Sybase. Since then, it has gone through lots of changes. Each new version brought several enhancements. Here are some of the significant ones.

• SQL Server 2000: This version made SQL Server more web-friendly by including the support for XML and HTTP.
• SQL Server 2005: Provides a utility called SQL Server Management Studio, which provides easier database management. SQL Server 2008: Some new features introduced were the enhancement of management tools and the introduction of new data types to work with. SQL Server 2012: Its powerful features were AlwaysOn Availability Groups, which keep your databases running, and improved security.
• SQL Server 2016: Extended its functionality to support operation on Linux systems and added real-time data analytics.
• SQL Server 2019: Added big data clusters, among other advanced features, to handle even more complex data needs of the users.
• SQL Server 2022: The latest release, whose primary focus is on the speed, security, and manageability of the instances. It has added features like Accelerated Database Recovery.

As of February 2024, the following versions of SQL Server are supported by Microsoft:

• SQL Server 2014
• SQL Server 2016
• SQL Server 2017
• SQL Server 2019
• SQL Server 2022

Key Features of SQL Server

• High Availability: Ensure that your data is always available with advanced features like Always On Availability Groups.
• Security: It secures your data by providing it with advanced security attributes, including encryption and row-level security.
• Performance: Equips you with the tools that can make your database more rapid and effective.
• Scalability: Can handle more volumes of data and a large number of users at the same time.
• Integration: Augments well with reporting and data-processing-like, like SQL Server Reporting Services-SSRS and SQL Server Integration Services-SSIS.
• Cloud Support: This can be deployed on the cloud for the management of data across more than one environment; one such example is Microsoft Azure.

Management Studio for SQL Server (SSMS)
One tool for managing SQL Server is called SQL Server Management Studio, or SSMS for short. You can set up, monitor, and administer your databases with ease thanks to its incredibly user-friendly interface. In actuality, the importance of SSMS for all SQL Server users—from developers to database administrators—cannot be overstated.

How to Connect to SQL Server?
Connecting to SQL Server is the first step to managing your databases. Here’s a quick guide on how to do it.

• Choose Your Tool: You can use SSMS, Azure Data Studio, or even programming languages like C#, Java, or Python.
• Get Your Connection Info: You’ll need the server name or IP address and your login details.
• Using SSMS
  • Open SSMS.
  • Enter the server name and your authentication method (like a username and password).
  • Click "Connect."
• Using a Programming Language
  • Install the necessary libraries or drivers.
  • Use a connection string to link to SQL Server. Here’s an example in C#.

using System;
using System.Data.SqlClient;

class Program
{
    static void Main()
    {
        // Connection details for the SQL Server
        string connectionString = "Server=myServerAddress;Database=myDataBase;User Id=myUsername;Password=myPassword;";

        // Creating a connection to the database
        using (SqlConnection connection = new SqlConnection(connectionString))
        {
            try
            {
                // Try to open the connection
                connection.Open();
                Console.WriteLine("Successfully connected to SQL Server!");
            }
            catch (Exception ex)
            {
                // If something goes wrong, show the error message
                Console.WriteLine("Error: " + ex.Message);
            }
        } // Connection gets automatically closed here
    }
}

Test the Connection: Once connected, you can run queries or perform database operations to ensure everything is working correctly.

A Look at the SSMS Interface

• SSMS is very user-friendly and will enable you to conveniently manage SQL Server databases. Some key components are.
• Object Explorer: On the left-hand side, it provides a tree view of your databases and server objects.
• Query Editor: Centre, this is where you write and execute your SQL queries.
• Properties Window: This usually appears at the bottom right and contains details about the currently selected object.
• Solution Explorer: Solution Explorer is used to organize our SQL projects and scripts. Registered Servers: Under one instance, we can manage multiple SQL Server instances. Template Explorer: This comes with ready-to-use templates of SQL statements for several routine actions.

Conclusion
SQL Server is a tremendous, robust platform for working with data, from small projects to the enterprise-level business. Throughout the years, it increased its functionality by adding a bunch of neat features that helped people store and protect their data, and work with it. With tools like SQL Server Management Studio, database management has become pretty straightforward. Whether you are starting to work with SQL Server or need to know a bit more about what it can achieve, it will provide all you could want to handle your data well. With the basics now explained to you, you're ready to explore more and make the most of SQL Server.

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SQL provides various numeric functions that help perform mathematical operations on numeric data. These functions are useful for calculations, rounding, and other numerical transformations.

Common Numeric Functions

• ABS(): Returns the absolute value of a number.
• CEILING(): Rounds a number up to the nearest integer.
• FLOOR(): Rounds a number down to the nearest integer.
• ROUND(): Rounds a number to a specified number of decimal places.
• POWER(): Returns the value of a number raised to a given power.
• SQRT(): Returns the square root of a number.
• EXP(): Returns the exponential value of a number.
• LOG(): Returns the natural logarithm of a number.
• LOG10(): Returns the base-10 logarithm of a number.
• RAND(): Returns a random float value between 0 and 1.
• SIGN(): Returns the sign of a number (-1, 0, or 1).
• PI(): Returns the value of PI (3.14159265358979).
• DEGREES(): Converts radians to degrees.
• RADIANS(): Converts degrees to radians.
• MOD(): Returns the remainder of a division.
• TRUNCATE(): Truncates a number to a specified decimal place.

Example Usage of Numeric Functions
1. Using ABS() Function
SELECT ABS(-15) AS AbsoluteValue;

2. Using CEILING() and FLOOR() Functions
SELECT CEILING(4.3) AS CeilValue, FLOOR(4.7) AS FloorValue;

Output

3. Using ROUND() and TRUNCATE() Functions
SELECT ROUND(123.456, 2) AS RoundedValue, TRUNCATE(123.456, 2) AS TruncatedValue;

Output

4. Using POWER() and SQRT() Functions
SELECT POWER(5, 3) AS PowerValue, SQRT(25) AS SquareRoot;

Output

5. Using MOD() Function
SELECT MOD(10, 3) AS ModResult;

6. Using PI(), DEGREES(), and RADIANS() Functions
SELECT
    PI() AS PiValue,
    DEGREES(PI()) AS DegreesValue,
    RADIANS(180) AS RadiansValue;

Output

When to Use Numeric Functions?

• Financial Calculations: Useful for interest rates, tax calculations, and rounding amounts.
• Data Analysis: Helps in statistical computations and mathematical transformations.
• Scientific Computing: Essential for performing complex mathematical calculations.
• Random Value Generation: Used for sampling, simulations, and random selections.

Advantages of Numeric Functions

• Simplifies mathematical computations in SQL.
• Enhances query efficiency by using built-in SQL functions.
• Provides precise and accurate results for calculations.

Numeric functions play a crucial role in SQL for performing various mathematical operations.

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Depending on the input values, scalar functions in SQL return a single value. Instead of working with sets of rows, these functions work with individual values.

Common Scalar Functions
LEN(): Returns the length of a string.
UPPER(): Converts a string to uppercase.
LOWER(): Converts a string to lowercase.
ROUND(): Rounds a number to a specified decimal place.
GETDATE(): Returns the current date and time.

Example Usage of Scalar Functions

1. Using LEN() Function
SELECT LEN('Hello World') AS StringLength;

2. Using UPPER() and LOWER() Functions
SELECT UPPER('hello') AS UpperCase, LOWER('WORLD') AS LowerCase;

Output

3. Using ROUND() Function
SELECT ROUND(123.456, 2) AS RoundedValue

4. Using GETDATE() Function
SELECT GETDATE() AS CurrentDateTime;

5. Using ABS() Function
SELECT ABS(-25) AS AbsoluteValue;

6. Using SQRT() Function
SELECT SQRT(49) AS SquareRoot;

7. Using SUBSTRING() Function
SELECT SUBSTRING('SQL Functions', 5, 9) AS SubstringResult;

8. Using REPLACE() Function
SELECT REPLACE('Hello SQL', 'SQL', 'World') AS ReplacedString;

Advanced Use of Scalar Functions
1. Combining Scalar Functions
SELECT UPPER(LEFT('advanced scalar functions', 8)) AS Result;

2. Using Scalar Functions in Computations
SELECT ROUND(AVG(Salary), 2) AS AverageSalary FROM Employees;

3. Formatting Dates Using Scalar Functions
SELECT FORMAT(GETDATE(), 'yyyy-MM-dd') AS FormattedDate;

4. Custom Scalar Function Example
CREATE FUNCTION dbo.Getfullname(@FirstName NVARCHAR(50),
                                @LastName  NVARCHAR(50))
returns NVARCHAR(100)
AS
  BEGIN
      RETURN ( @FirstName + ' ' + @LastName )
  END;

Usage
SELECT dbo.GetFullName('John', 'Doe') AS FullName;

Advantages of Scalar Functions

• Helps in data formatting and transformation.
• Improves code readability and maintainability.
• Enhances query flexibility with built-in SQL functions.

Scalar functions are essential for manipulating individual values in SQL queries.

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A roadmap that describes how a query will be run is called a SQL Execution Plan. It aids in SQL query analysis and optimization.

Execution Plan Types
Estimated Execution Plan: This illustrates how the query would function even if it were not run.
Actual Execution Plan: This displays runtime information along with the query's actual execution.

How to Get the Execution Plan?
Using SQL Server Management Studio (SSMS)
Estimated Execution Plan: Press Ctrl + L or go to Query > Display Estimated Execution Plan.

Actual Execution Plan: Press Ctrl + M or go to Query > Include Actual Execution Plan, then run the query.

Using T-SQL Commands
Estimated Execution Plan
SET SHOWPLAN_XML ON;
SELECT * FROM Users WHERE UserID = 1;
SET SHOWPLAN_XML OFF;

Actual Execution Plan
SET STATISTICS XML ON;
SELECT * FROM Users WHERE UserID = 1;
SET STATISTICS XML OFF;

Understanding Execution Plan Components

Tips to Optimize SQL Queries

Use Indexes: Create indexes on frequently used columns.
Avoid SELECT *: Retrieve only the required columns.
Optimize Joins: Prefer INNER JOIN over OUTER JOIN if possible.
Check Execution Plan: Avoid Table Scans and use Index Seeks.
Avoid Functions on Indexed Columns: Example: WHERE YEAR(DateColumn) = 2023 prevents index usage.

In the next part, we will dive deeper into SQL execution plans, covering advanced topics like operator costs, parallelism, query hints, and execution plan caching, helping you gain a more comprehensive understanding of how SQL Server processes queries efficiently. Stay tuned!

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Statement of the Problem
Numerous database developers run into unforeseen inconsistencies while using SQL Server for calculations.  When the same mathematical phrase is evaluated differently, one typical problem occurs.  Take the following SQL Server code snippet, for example:

DECLARE @Number1 AS DECIMAL(26,7) = 0.9009000;
DECLARE @Number2 AS DECIMAL(26,7) = 1.000000000;
DECLARE @Number3 AS DECIMAL(26,7) = 1000.00000000;
DECLARE @Result  AS DECIMAL(26,7);

SET @Result = (@Number1 * @Number2) / @Number3;
SELECT @Result; -- 0.0009000
SET @Result = (@Number1 * @Number2);
SET @Result = (@Result / @Number3);
SELECT @Result; -- 0.0009009

In the first case, the output is 0.0009000, while in the second case, the output is 0.0009009. This divergence raises the question: Why are the results different when the same calculation is performed?

Explanation. Single Step Calculation
In the first approach, the entire expression (@Number1 * @Number2) / @Number3 is computed in a single step:

SQL Server first computes the product of @Number1 and @Number2, which equals 0.9009000.
Next, it divides that result by @Number3 (1000.00000000).

The result of this division is affected by how SQL Server handles precision and rounding for decimal operations. This might introduce slight inaccuracies, leading to the outcome of 0.0009000.

Multiple Step Calculation
In the second approach, the operations are separated into two distinct steps:

First, the calculation @Number1 * @Number2 is executed and stored in @Result. This retains the value of 0.9009000.
Then, the variable @Result is divided by @Number3 in a separate statement.

This step-by-step division allows SQL Server to apply different rounding and precision rules, which can sometimes yield a more accurate result of 0.0009009.

Conclusion
The difference in outputs can often be attributed to the varying treatment of precision and rounding during calculations:

• In a single-step calculation, SQL Server evaluates the entire expression at once, potentially altering precision during the process.
• In a multiple-step calculation, SQL Server retains more precision through intermediate results, leading to a different output.

Resolution
To achieve consistent results in SQL Server calculations, developers should consider controlling precision explicitly. For example, applying rounding can help standardize outcomes:
SET @Result = ROUND((@Number1 * @Number2) / @Number3, 7);

By managing precision and rounding explicitly, programmers can avoid discrepancies and ensure that their numerical calculations yield the expected results. Understanding these nuances in SQL Server can lead to more reliable and accurate database operations.

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A Memory-Optimized Table Variable: What Is It?
A unique kind of table variable that makes use of SQL Server's In-Memory OLTP engine is called a Memory-Optimized Table Variable. It is maintained in memory, which lowers tempdb contention and improves performance for workloads involving frequent data processing, in contrast to ordinary table variables or temporary tables (#temp).

Why is it Useful?
Memory-Optimized Table Variables provide.

• Faster performance: Avoids disk I/O by keeping data in memory.
• Tempdb contention reduction: Traditional temp tables and table variables rely on tempdb, which can be a bottleneck.
• Optimized latch-free concurrency: Uses memory-optimized data structures for ultra-fast access.
• Efficient for short-lived data: Ideal for scenarios where data exists only within a session or batch.

When to Use It?
You should use Memory-Optimized Table Variables when,

• Your queries experience tempdb contention.
• You perform frequent batch operations with temporary data.
• You need high-performance OLTP workloads with frequent inserts and lookups.
• You are working with stored procedures that rely on table variables.

Where to Use It?

• Stored procedures that process intermediate data.
• ETL workloads require temporary staging tables.
• High-performance transaction processing systems.
• Session-based data caching to avoid repeated database calls.

How to Use a Memory-Optimized Table Variable?
1. Enable Memory-Optimized Tables in the Database
Before using memory-optimized table variables, you must enable In-Memory OLTP.

ALTER DATABASE YourDatabase
ADD FILEGROUP MemoryOptimizedFG CONTAINS MEMORY_OPTIMIZED_DATA;

ALTER DATABASE YourDatabase
ADD FILE (NAME = 'MemOptData', FILENAME = 'C:\Data\MemOptData')
TO FILEGROUP MemoryOptimizedFG;

2. Declare a Memory-Optimized Table Variable
Unlike a regular table variable, you must use MEMORY_OPTIMIZED = ON.
DECLARE @MemOptTable TABLE
(
ID INT NOT NULL PRIMARY KEY NONCLUSTERED,
Name NVARCHAR(100) NOT NULL
) WITH (MEMORY_OPTIMIZED = ON);

3. Insert and Query Data Efficiently
INSERT INTO @MemOptTable (ID, Name) VALUES (1, 'SQL Server'), (2, 'DBA Expert');

SELECT * FROM @MemOptTable;

4. Compare with Traditional Table Variables
A traditional table variable.
DECLARE @TableVar TABLE (ID INT, Name NVARCHAR(100));

Uses tempdb, causing I/O overhead.
Suffers from locking and latching under high concurrency.

Memory-Optimized Table Variables

• Avoid tempdb entirely.
• Are lock-free and latch-free, making them 10x faster in some cases.

Real-Time Example: Improving Performance in a High-Traffic System

• Scenario: A banking application processes thousands of real-time transactions per second. Using traditional table variables slows down the system due to tempdb contention.
• Solution: By replacing standard table variables with Memory-Optimized Table Variables, the system eliminates tempdb bottlenecks, resulting in a 40% faster transaction processing time.

DECLARE @TransactionLog TABLE
(
TransactionID INT NOT NULL PRIMARY KEY NONCLUSTERED,
AccountID INT NOT NULL,
Amount DECIMAL(10,2) NOT NULL
) WITH (MEMORY_OPTIMIZED = ON);

Memory-optimized table Variables were introduced in SQL Server 2016 and are available in the following versions.
Compatible SQL Server Versions
SQL Server 2016 (13.x)
SQL Server 2017 (14.x)
SQL Server 2019 (15.x)
SQL Server 2022 (16.x)

Not Available In
SQL Server 2014 and earlier (Memory-optimized tables were introduced in 2014, but table variables were not supported as memory-optimized until 2016).

Best Practices & Recommendations
Enable In-Memory OLTP before using Memory-Optimized Table Variables.
Use them in stored procedures or batch processing scenarios for best performance.
Avoid using them for large datasets due to memory limitations.
Use Non-Clustered Indexes properly to avoid performance bottlenecks.
Test with different workloads before applying in production environments.

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Using ROW_NUMBER() and PARTITION BY (Preferred Approach)
The ROW_NUMBER() function assigns a unique row number to each record within a partition (group). We can use this to identify and delete duplicates while keeping only the required data.

Query Syntax
WITH CTE AS (
    SELECT
        *,
        ROW_NUMBER() OVER (PARTITION BY column1, column2 ORDER BY id) AS row_num
    FROM table_name
    WHERE condition  -- Apply filtering condition here
)
DELETE FROM CTE
WHERE row_num > 1;

Example
Consider a Customer table with duplicate entries based on Email.

Removing Duplicates While Keeping the First Entry.

;WITH CTE AS (
    SELECT ID
    FROM (
        SELECT ID,
               ROW_NUMBER() OVER (PARTITION BY NAME, Email, City ORDER BY ID) AS RN
        FROM Customers
        WHERE City = 'NY'  -- Only NY state filtering condition
    ) AS sub
    WHERE RN > 1
)
DELETE FROM Customers
WHERE ID IN (SELECT ID FROM CTE);

Explanation of the Query
Identifies Duplicates

• ROW_NUMBER() OVER (PARTITION BY Name, Email, City ORDER BY ID)
• Assign a row number (RN) for each duplicate group, keeping the first record (RN = 1).

Filters Out Duplicates (RN > 1): Only marks duplicate records where City = 'NY'.
Deletes Duplicate Records: Deletes only IDs from the CTE that have RN > 1
This syntax will be useful when we are joining more tables and deleting duplicates from one specific table.

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