AI-DBA Software Inc.’s Post

In the 2.0 release of AI-DBA for SQL Server, there is a greater emphasis on workload predictions and analysis.

Did You Know... The total global datasphere is projected to hit 175 ZB by 2025 (per IDC)? And that it’s unstructured data that is driving the majority of the growth—about 90% according to estimates? The challenge is, that unstructured data is likely dispersed across locations, and it can be hard to discover, ingest and process at high performance—especially as those datasets grow exponentially for AI. The key is to implement a data management strategy to bring AI anywhere on data everywhere. Enter PowerScale—the highly flexible enterprise file storage system purpose-built to unlock your unstructured data potential. As the leading scale-out NAS platform, it’s no wonder PowerScale is consistently ranked as a market leader by industry analysts and customers alike. Computacenter and Dell Technologies can help lower your total-cost-to-own and power numerous use cases including file shares, Hadoop analytics, safety and security, and much more. Want to know more about what PowerScale can do with your data? DM me for info! #computacenter #delltechnologiespartner #powerscale #aiforeveryone #winningtogether #powerfulpartnerships

The enterprise intelligence layer is rapidly changing. The previous layer was made up of data warehouses, data marts, and BI tools. It was mostly rules-based; we defined the rules and the intelligence strictly followed them, answering each question precisely as we had specified. That is the world in which we grew up, and it is the world to which most analysts are used to. However, the new variant is very different from its predecessor. The new intelligence layer (GenAi) doesn't just follow rules—it possesses broader capabilities. It can reason, analyze, consult, collaborate, use tools, make decisions and even self-reflect. Consequently, it behaves very differently, and in all the best ways. I predict that this new layer of intelligence will transform our relationship with technology; we will no longer see it as merely a blunt instrument but more as an intelligent coworker. It will also change our entire technology stack top-to-bottom. Think GPUs vs. CPUs, Vector Databases vs. Relational stores, Unstructured Data Management vs. ETL/ELT etc etc. Don’t get me wrong, The Data Lakes, Warehouses and Marts aren’t going anywhere soon; but their role in the enterprise is very likely to change from the primary source of intelligence for everything, to something lesser. The question I keep pondering is “How will enterprises manage this structural change without significant self disruption?” Historically, change management and internal disruption have not been the hallmarks for most large enterprises. #llm #genai #intelligencelayer #ai #relationaldatabases #vectordatabase #intelligence #EnterpriseIntelligenceLayer #datawarehouses #changemanagement #data #BusinessIntelligence

𝐇𝐨𝐰 𝐭𝐨 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞? Here are the top ways to improve database performance: 1. Indexing Create the right indexes based on query patterns to speed up data retrieval. 2. Materialized Views Store pre-computed query results for quick access, reducing the need to process complex queries repeatedly. 3. Vertical Scaling Increase the capacity of the database server by adding more CPU, RAM, or storage. 4. Denormalization Reduce complex joins by restructuring data, which can improve query performance. 5. Database Caching Store frequently accessed data in a faster storage layer to reduce load on the database. 6. Replication Create copies of the primary database on different servers to distribute read load and enhance availability. 7. Sharding Divide the database into smaller, manageable pieces, or shards, to distribute load and improve performance. 8. Partitioning Split large tables into smaller, more manageable pieces to improve query performance and maintenance. 9. Query Optimization Rewrite and fine-tune queries to execute more efficiently. 10. Use of Appropriate Data Types Select the most efficient data types for each column to save space and speed up processing. 11. Limiting Indexes Avoid excessive indexing, which can slow down write operations; use indexes judiciously. 12. Archiving Old Data Move infrequently accessed data to an archive to keep the active database smaller and faster. Reference: Grokking the System Design Interview - https://lnkd.in/giwyzfkT —- 📌 Follow Abdul Rahim Roni for more such content: If you like this and want to get more AI tools, tutorials and news, join Cool Tech Gadgets - my newsletter that helps you get smarter about Microsoft and AI and Google and technology: 👉 https://lnkd.in/grn4UX-8 #systemdesign #databases #InterviewTips #SoftwareDevelopment

In case you somehow missed it: last week we presented #Oracle Database 23ai and Exadata Software 24.1ai. With over 300 new functionalities and over a 1000 enhancements, these releases transform how you manage your data, whether it is for Application Development, AI/ML, Analytics and more. #oracledatabase #artificialintelligence #generativeAI #machinelearning #exadata #zfsstorage

With the continuous innovation in Artificial Intelligence, it is essential to keep your database systems up to date and streamlined. Our recent AI integration for SQL databases incorporates RAG, Vector Search, and Admin Automation features - a stride towards powerful, data-driven decision making. Intrigued about how this could revolutionize your business operations? Watch this insightful video for a complete walkthrough: https://lnkd.in/ecFT422q Always remember, leveraging technology is no longer an option, but a necessity in today's fast-paced digital world. Be part of this change, and let's pioneer the future together. #AIIntegration #SQLdatabases #RAG #VectorSearch #AdminAutomation

Machine learning workflows often require efficient data storage and retrieval mechanisms, especially when dealing with high-dimensional data. Vector databases provide an ideal solution for these needs. #machinelearning #vector #database #datastorage #software https://lnkd.in/gnPgjrGt

Simplifying data integration and accelerating indexing for retrieval augmented generation (RAG) applications, the #OneLake files indexer aims to enhance your interaction with #AzureAISearch. Learn more about the preview! #AzureAI #MSBuild #MSFTAdvocate

https://lnkd.in/gfWfKYtM There’s no AI without data. Because Oracle databases hold most of the world’s mission-critical data, Oracle Database 23ai with AI Vector Search is a natural place for organizations to start their AI journey.  #oracledatabase23ai #oracle

Simplifying data integration and accelerating indexing for retrieval augmented generation (RAG) applications, the #OneLake files indexer aims to enhance your interaction with #AzureAISearch. Learn more about the preview! #AzureAI #MSBuild #MSFTAdvocate