New Features in SQL Server 2022 for Developers

SQL Server 2022 brings a comprehensive suite of features designed to enhance the development experience, improve performance, and strengthen security. By leveraging these capabilities, developers can create more efficient, scalable, and secure applications, positioning their organisations to effectively handle modern data challenges and requirements. The focus on intelligent processing, machine learning, data integration, and robust security aligns with the evolving landscape of application development and data management.

Here’s a detailed overview of the new features in SQL Server 2022 tailored for developers.

1. Intelligent Query Processing Enhancements

Overview: SQL Server 2022 continues to build on the Intelligent Query Processing (IQP) capabilities introduced in previous versions. The aim is to enhance query performance automatically without requiring significant code changes. This means that developers can achieve better performance for their applications without extensive rewrites.

Key Feature:

• Parameter Sensitive Plan Optimisation: This feature allows SQL Server to create and maintain multiple execution plans for the same query based on different parameter values. It identifies the most efficient plan based on runtime parameter values rather than relying solely on the static plan generated during compilation.

Example Scenario: Consider a financial application that retrieves transaction records based on different filters, such as dates and account numbers. With parameter-sensitive plan optimization, SQL Server will adaptively select the best execution plan based on the most commonly accessed account numbers or date ranges, leading to reduced execution times and better resource utilisation.

2. Built-in Machine Learning Services

Overview: SQL Server 2022 provides built-in support for machine learning services directly within the database engine. This integration allows developers to run R and Python scripts without the need for external tools.

Key Features:

• Direct Data Access: R and Python scripts can access SQL Server data directly, allowing for real-time analysis without the need to export data to another platform.
• Model Deployment: Developers can train and deploy machine learning models directly within SQL Server, making it easier to use these models in production applications.
• Scalability: Since machine learning is performed on the SQL Server instance, it can take advantage of the existing hardware resources, which may lead to improved performance for large datasets.

Example Scenario: A marketing department might use machine learning to predict customer churn. By training a model directly within SQL Server, they can continuously feed it fresh data from their transactional systems, enabling real-time predictions that can drive immediate business actions, such as targeted retention strategies.

3. SQL Server Ledger

Overview: SQL Server Ledger introduces a new feature that provides blockchain-like functionality, enabling developers to build applications with strong data integrity and immutability. This is particularly useful for industries that require stringent audit trails and data verification.

Key Features:

• Immutable Ledger Tables: These tables track all transactions in a way that prevents any modifications after they are committed, creating an audit trail that can be independently verified.
• Cryptographic Verification: Each transaction is cryptographically hashed, and the hashes are stored in the ledger, ensuring the integrity and authenticity of the data over time.
• Integration with Existing Systems: Developers can integrate ledger functionality with existing applications seamlessly, making it easier to adopt this technology without major architectural changes.

Example Scenario: In a healthcare application, patient treatment records can be stored in a ledger table. Each update to a patient’s treatment plan is recorded in a way that cannot be altered, providing a verifiable history that meets regulatory compliance and can be audited as needed.

4. Enhancements to Query Store

Overview: The Query Store feature is designed to help developers manage query performance by providing historical data on query execution, plan choices, and runtime statistics. The enhancements in SQL Server 2022 provide deeper insights and improved capabilities for tracking query performance over time.

Key Features:

• Query Performance Insights: The enhancements include improved reporting capabilities, allowing developers to visualize query performance trends over time and quickly identify any regressions.
• Automatic Plan Correction: SQL Server can automatically identify when a query's performance has degraded and revert to a previously optimal execution plan, reducing the need for manual intervention.
• Improved Visibility into Query Execution: Developers can see detailed information about query execution times, resource consumption, and plan changes, making it easier to diagnose performance issues.

Example Scenario: A web application might experience slowdowns after a new feature is deployed. Developers can use the Query Store to identify which queries have started to perform poorly, understand the reason behind the changes, and either optimise the queries or revert to a better-performing plan.

5. Azure Synapse Link for SQL Server

Overview: Azure Synapse Link provides a seamless integration between on-premises SQL Server databases and Azure Synapse Analytics, enabling developers to perform analytics on operational data without the need to move data to the cloud.

Key Features:

• Near Real-Time Analytics: Developers can run analytics on data as it is ingested, allowing for timely insights that can influence business decisions.
• Simplified Data Pipelines: This integration allows for the creation of streamlined data pipelines, enabling developers to focus on analysis rather than data movement.
• Enhanced Reporting: Azure Synapse provides powerful analytical capabilities that can be directly applied to operational data, enhancing reporting and decision-making.

Example Scenario: An e-commerce company can use Azure Synapse Link to analyse customer behaviour in real-time. By connecting their on-premises SQL Server to Azure Synapse, they can quickly generate reports on customer purchasing patterns, allowing for immediate marketing adjustments based on current trends.

6. Enhanced Security Features

Overview: SQL Server 2022 introduces several new security enhancements designed to protect sensitive data and improve overall database security posture.

Key Features:

• Always Encrypted with Secure Enclaves: This enhancement allows certain operations to be performed on encrypted data without exposing it in plaintext, enhancing the security of sensitive information.
• Dynamic Data Masking Enhancements: These improvements offer developers more options for controlling how sensitive data is presented to users, allowing for greater flexibility in displaying data according to user roles.
• Security Monitoring Tools: New tools provide real-time alerts and recommendations for improving database security, helping developers maintain compliance with best practices.

Example Scenario: In a banking application, sensitive information such as customer account details can be protected using Always Encrypted. Developers can configure dynamic data masking to ensure that only authorised personnel can view sensitive data, thus enhancing customer trust and compliance with regulations.

7. SQL Server Management Studio (SSMS) Enhancements

Overview: The latest version of SQL Server Management Studio includes significant enhancements designed to improve the developer experience, making it easier to manage and develop SQL Server databases.

Key Features:

• Improved IntelliSense: Enhanced IntelliSense capabilities help developers write SQL queries more efficiently by providing better auto-completion and suggestions.
• Integrated Monitoring Tools: New monitoring features allow developers to track database performance metrics directly within SSMS, enabling faster troubleshooting and optimisation.
• User-Friendly Interface: A more intuitive user interface makes it easier for developers to navigate complex database structures and management tasks.

Example Scenario: A developer working on a complex SQL query can rely on the improved IntelliSense features to quickly find the right syntax and suggestions, reducing errors and speeding up the query development process.

8. Support for UTF-8 Encoding

Overview: With the introduction of UTF-8 encoding support, SQL Server 2022 allows developers to store and manipulate multilingual data more effectively, which is crucial for global applications.

Key Features:

• Flexible Data Storage: Developers can specify UTF-8 encoding when creating string columns, enabling efficient storage of various character sets.
• Reduced Storage Requirements: By using UTF-8 encoding, developers can save space compared to UTF-16 encoding, particularly for texts primarily in English or other Latin-based languages.

That's all for now.

New Features in SQL Server 2022 for DBAs

SQL Server 2022 brings a wealth of new features that empower DBAs to enhance database performance, security, and usability. By leveraging these capabilities, organizations can optimise their data management strategies, ensuring better compliance, improved efficiency, and advanced analytics capabilities. The introduction of intelligent query processing, enhanced security features, and support for serverless architectures positions SQL Server 2022 as a robust choice for modern data solutions.

Here’s a summary of the key features and their benefits:

1. Intelligent Query Processing Enhancements

Overview: SQL Server 2022 expands on the Intelligent Query Processing (IQP) capabilities introduced in SQL Server 2019. New features include:

• Parameter Sensitive Plan Optimization: This feature allows SQL Server to create multiple execution plans for a single query based on the specific parameter values used during execution, leading to better performance.

Case Scenario: In a scenario where a stored procedure is called with highly variable input parameters (e.g., a sales report for different regions), the parameter-sensitive optimisation ensures that SQL Server selects the best execution plan tailored to the input, reducing execution time and resource consumption.

2. SQL Server Ledger

Overview: The SQL Server Ledger feature provides blockchain-like capabilities, ensuring data integrity and immutability through ledger tables. This feature uses cryptographic methods to verify data integrity, making it ideal for applications requiring audit trails.

Case Scenario: For financial institutions that need to maintain accurate records of transactions, SQL Server Ledger enables the creation of a ledger table where each transaction is logged. Any modifications are tracked, ensuring an immutable record that can be audited for compliance purposes.

3. Enhanced Security Features

Overview: SQL Server 2022 includes several security enhancements:

• Always Encrypted with Secure Enclaves: This allows more operations to be performed on encrypted data without exposing it in plaintext, enhancing security for sensitive information.
• Dynamic Data Masking Enhancements: Provides more flexible masking options, allowing DBAs to control how sensitive data is displayed to different users.

Case Scenario: In a healthcare application, patient data can be stored securely using Always Encrypted. The application can perform queries and calculations on encrypted data without revealing sensitive information to unauthorised users.

4. Query Store Enhancements

Overview: The Query Store feature has been enhanced to provide deeper insights into query performance over time. It now includes capabilities for identifying and managing query performance regressions.

Case Scenario: DBAs can utilise the enhanced Query Store to track performance changes after application deployments. If a new release introduces slow-running queries, the Query Store can help identify regressions, allowing quick remediation by reverting to previously optimised execution plans.

5. Built-in Machine Learning Services

Overview: SQL Server 2022 integrates built-in support for machine learning services, allowing DBAs to run R and Python scripts directly in the database engine. This feature enables data scientists and analysts to perform advanced analytics without moving data outside the SQL Server environment.

Case Scenario: A retail company can analyse customer purchasing patterns using machine learning algorithms stored in SQL Server. DBAs can schedule these analyses to run during off-peak hours, optimizing resource usage while delivering timely insights.

6. Serverless SQL Database

Overview: SQL Server 2022 introduces serverless capabilities for databases, allowing automatic scaling and cost-effective resource usage based on demand. This feature is ideal for workloads that experience variable usage patterns.

Case Scenario: A startup that experiences fluctuating traffic on its web application can benefit from serverless SQL databases, which automatically scale up during peak traffic and scale down during low usage periods, ensuring cost-efficiency.

7. Enhanced Availability Groups

Overview: New features for Always On Availability Groups improve failover performance and management. This includes automatic failover for databases in availability groups without the need for manual intervention.

Case Scenario: In a mission-critical application, the DBA can configure availability groups with automatic failover capabilities, ensuring minimal downtime during server maintenance or unexpected failures, thus enhancing application reliability.

8. Improved Performance Insights and Monitoring Tools

Overview: SQL Server 2022 provides enhanced monitoring tools with better insights into system performance. Features like the Database Health Monitor offer real-time health checks and performance tuning recommendations.

Case Scenario: DBAs can utilize the Database Health Monitor to receive alerts and recommendations for performance tuning based on workload analysis, enabling proactive management and optimization of database performance.

9. Support for JSON and XML Enhancements

Overview: SQL Server 2022 introduces improvements to support for JSON and XML data types, including better indexing and querying capabilities.

Case Scenario: A content management system that relies on JSON documents can leverage the new indexing features to improve query performance on JSON fields, allowing faster data retrieval and reporting.

That's all for now.

Knowing and Protecting Your Data

Computer circuitry has crept into nearly everything we use nowadays, and almost all of which gather information from us and about us. As a result, the present is immersed in copious amounts of data, which is stored somewhere and, most of the time, not securely protected. 

Today we are almost entirely reliant on IT departments at work; practically all organisations depend on enterprise-wide applications to support numerous key business processes which create a mountain of information. Needless to say, IT infrastructure and business applications are increasingly — and usually, unnecessarily — more complex; complexity is the worst enemy of security — and sometimes, of performance too. As a consequence, we are losing more control of storage and, therefore, security. The more data we share, the bigger the security risk is. 

Companies always strive to successfully harness the power of data, but unfortunately, this is not the case when it comes to securely accessing data. There are laws about protecting information, but most people are either blissfully unaware of them or careless about them. Many of us know what could happen when our data end up in the wrong hands.

Cryptology vs. Cryptography vs. Cryptanalysis. What's the Difference?

Although the words cryptology, cryptography and cryptanalysis are used interchangeably — strictly speaking — they mean different things. Nowadays, we only use the word cryptography for everything; it's indeed a catch-all for a broad range of intertwined topics. Today’s post not only aims to point out the differences among them but also to show their connections to each other.

To begin with, cryptology is the mathematics, algorithms, and the applications of formulas that underpins cryptography and cryptanalysis. The world of cryptology goes from basic foundations in cryptography (code-making) to modern algebraic cryptanalysis (code-breaking). So, cryptology is clearly divided into two major parts: cryptography and cryptanalysis; with strong connections to each other, which include cryptographic applications, types of cryptography and their algorithms, code-breaking techniques, information theory, number theory and mathematical applications to encrypt data and also break ciphers. 

Curious About Cryptographic Boolean Functions?

In order to have a good understanding of cryptographic Boolean functions, let's get started from scratch, that's, having a look at the very basic concepts. To begin with, all modern computers are composed of very basic logic circuits using very basic gates, called operators which only apply to binary numbers, in other words, 0 and 1. Each type of gate implements a Boolean operation. The finite field ${\mathbb F}_{2}=\{0,1\}$ is also called binary field, and it is of special interest because it is particularly efficient for implementation in hardware or on a binary computer. Using these gates, the rules of Boolean algebra may be applied to design circuits that perform a variety of tasks. For example, integrated circuits. Then, these circuits are all put together to build into more powerful modern computers. 

Exploring What a PhD Is Like

Some weeks ago, it was my pleasure, and honour, to be invited to participate as a panellist in the online event Explore What a PhD is Like organised by PhD Social Support Network from Loughborough University. The event aimed to give undergraduate and postgraduate students a summary of Doctoral Researchers’ lives and an idea of what it’s like to do a PhD at Loughborough University.

With so much confusing and contradictory information out there, it becomes a bit difficult to have a good understanding of many things before embarking on doing a PhD. So I decided to write down my answers for those of you who could not attend the event and are interested in doing a PhD. Because we were four panellists, I just answered some questions, but in this post, I share my answers to all questions, which mainly depend on my personal circumstances and experiences at Loughborough University — And I do hope to help clear some things up. In case that you have any other questions, please let me know in the comment section.

Don't Place the Blame on SQL Server

I have never worked for Microsoft, but SQL Server has given me a lot in terms of learning, community and opportunities, all these together have helped me do a great job as a Database Administrator (DBA) for many years. Since I started working with SQL Server nearly 15 years ago, I have heard a lot of complaints about SQL Server being nowhere near as good as Oracle. Much as I would have liked to ignore these fruitless discussions, I couldn't see the point of comparing products in such a compulsive way. Is it not true that our skills are more crucial than the technology itself? — Or perhaps some people just try to find something to blame. Whatever the case, I am convinced that we, as database professionals, are compelled to make the most out of any specific database technology. 

No matter what technology we are working with, we are at the wheel — technology is just a tool — so it is not the best to blame technology on the ground of one's inefficiency.

Cryptography is Everywhere

Historically, cryptography was used for secret communication by exclusive sectors only — such as governments, military and spies — since it was crucial and affordable to them. They have long been aware of the consequences of their messages falling into the wrong hands; therefore, this situation has motivated the development of techniques for disguising a message so that only the intended recipient can read it. 

The huge desire for secrecy led nations, kings, and queens to make all-out efforts to ensure the security of communications by inventing the best possible secret codes and ciphers. 

A lover in Victorian times

As the public also became aware of the need to protect personal messages of a highly sensitive nature, they also became comfortable with encipherment. They began to express their cryptographic skills in a variety of ways — for example, young lovers in Victorian England were often forbidden from publicly expressing their affection, and could not even communicate by letter in case their parents intercepted and read the contents. This resulted in lovers sending encrypted messages to each other via the personal columns of newspapers, more specifically, via the classified ads.