SQL Server lifecycle and considerations for enterprises
SQL Server is one of the most versatile databases which enterprises trust for their database workloads. It’s a traditional Online Transactional Processing (OLTP) database and over the years enterprises across different industry verticals like financial sector, healthcare, media and entertainment, manufacturing, insurance etc. have built plethora of applications using SQL Server. Every few years, Microsoft releases a new version of SQL Server (like 2014, 2016, 2017, 2019, 2022 editions) with new feature enhancements which make the product more secure, more compliant and with a performant database engine coping up with the growing needs of enterprise data. I’ve spent several years in the core SQL Server product team and can proudly vouch the rigorous testing’s which are done on the product prior to any release. SQL Server engineering and product teams have been known across the industry for their decades of engineering excellence in delivering such a robust engine impacting millions of customers worldwide.
Each version of SQL Server is backed by a minimum of 10 years support, which includes five years in mainstream support (includes functional, performance, scalability, and security updates), and five years in extended support (only security updates). For customers who are nearing their 10 years on a particular version they choose to either migrate to the cloud into Azure SQL, or to an Azure Virtual Machine for free extended security updates, or upgrade to a more recent version of SQL Server or purchase and extended security updates subscription with Microsoft. Enterprise customers typically choose to remain in n-1 or n-2 (n being the latest version) version of the product and prior to the 10 years end of life has to choose one of the options mentioned above. Several enterprise customers for their critical workloads and for business reasons need to remain on-premises and cannot move to the cloud. For them, they are tasked with migrating to the latest version of SQL Server along with upgrading their physical hardware. Recently July 9th, 2024, marked the end-of-life support for SQL Server 2014. On-premises customers will need to move to a recent version of SQL Server and also upgrade the necessary hardware to meet the system requirements. This involves significant cost and planning for enterprises.
Customers have built applications on SQL Server and most of these applications demand some form of reporting and Machine Learning capabilities on the data stored in SQL Server. Customers use SQL Server Machine Learning Services, launched in SQL Server 2016 with R support and 2017 with Python support to run any ML capabilities within the SQL Server database instances. However, when using the ML services the R or Python code is wrapped within an sp_execute_external_script stored procedure in T-SQL and customers miss getting any IntelliSense and debugging capabilities. I’ve seen instances where data scientists query the SQL instances and pull the data outside SQL Server to create their ML models and then store these ML models as binary object within SQL Server and then score against it. In this approach, the moment the data is pulled outside SQL Server the trust boundaries of the data are lost and customer data is potentially exposed to more surface areas for attack.
Now in 2024, we see a new advent of workloads where enterprise customers are trying to enable GenAI capabilities over their databases. Enterprises are either trying to improve efficiency for their customers to find information correctly or improve the overall experience of their applications. For outwards facing use cases, customers want to have capabilities like enterprise search on their data and replacing current drop downs and filters in their applications to just providing a simple search like experience for their customers to ask questions in natural language and get responses from their databases.
From both at my time in Microsoft and Amazon, I’ve seen BI teams being randomized with constant questions which leadership team asks on the data, and every time a new ad-hoc report gets created and enterprises end up creating hundreds of reports wasting both time and resources. We observe internal facing use cases where customers ask ad-hoc questions over their database instances and replacing manually created reports over their SQL instances in SSRS and PowerBI with asking questions in natural language. Imagine if enterprises had a natural language search bar for leadership to ask questions on their database instances which showed them all the results across thousands of tables.
In Tursio, we are turning SQL Servers into GenAI machines. Enterprise customers running SQL Server instances anywhere — on-premises (yes you heard, right !) and in the cloud can get an in-situ GenAI solution using Tursio. Tursio can be deployed entirely on-premises (without any cloud connectivity) where enterprise customers can ask questions in natural language and get responses from within their databases. All the data modeling happens inside SQL Server instances and there is zero data movement. None of the data ever leaves your SQL Server. Tursio understands the ontology of the data and as the underlying data changes the models are constantly refreshed providing customers with the accurate and updated results from the database whenever the question has been asked. Enterprises can invoke the same search bar using a simple Rest API endpoint from within their applications. Tursio tries to look beyond just answering questions which enterprises are asking but what value they are seeking once they get the answer — Are customers trying to predict demand? Are customers trying to find anomalies? Are customers trying to forecast? Are they trying to classify? Customers using the Tursio platform get predictive insights from the data allowing them to effectively make business decisions faster and improve time to value and all within 3 seconds. Customers can define their own KPI’s and Tursio constantly learns and fine tunes the data models providing accurate results from the data models created.
If you are a SQL Server customer and want to turbo charge your applications with GenAI capabilities without your data ever leaving SQL Server, feel free to drop a note below. In addition to SQL Server and Azure SQL, Tursio platform also supports additional databases and data warehouses like Microsoft Fabric, AWS Redshift, Snowflake, Google BigQuery, Teradata, PostgreSQL, MySQL etc. Here are some teaser screenshots of bringing generative AI to your data:
Example 1. Enterprise Search Questions using Tursio
Example 2. Understanding business KPIs using Tursio
Example 3. Analytical Questions using Tursio
