Blair Roper’s Post

𝐀𝐈-𝐃𝐁𝐀 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐬 𝐒𝐐𝐋 𝐒𝐞𝐫𝐯𝐞𝐫 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐰𝐢𝐭𝐡 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬   We are excited to announce a new AI-DBA feature tailored for high-performance SQL Server administrators. Our latest feature integrates predictive analytics to identify potential bottlenecks, providing vital insights and proactive solutions. "AI-DBA’s predictive analytics is a breakthrough for SQL Server management, enabling proactive issue resolution and optimal performance," said Sales & Partnership Director Fabio Alexandre Vieira.   𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: * 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑣𝑒 𝐵𝑜𝑡𝑡𝑙𝑒𝑛𝑒𝑐𝑘 𝐼𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛: Forecasts potential issues with estimated dates/times, blockers, and impacted resources (memory, CPU, IO). * 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 𝑂𝑝𝑡𝑖𝑚𝑖𝑧𝑎𝑡𝑖𝑜𝑛: Proactively address issues to enhance database performance. * 𝑃𝑟𝑜𝑎𝑐𝑡𝑖𝑣𝑒 𝑀𝑜𝑛𝑖𝑡𝑜𝑟𝑖𝑛𝑔: Real-time alerts and actionable insights for swift problem resolution. Example Predictions: * 𝑀𝑒𝑚𝑜𝑟𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛: Detects significant increases, offering recommendations for optimization and potential hardware upgrades. * 𝐼𝑂 𝑆𝑡𝑎𝑙𝑙𝑠: Anticipates storage performance issues, ensuring efficient write operations. #sqlserver #microsoft #dba #ai #predictiveanalytics Special thanks to HAMID J. FARD, Ava B. Abshuri, and the AI-DBA Team for this new exciting feature!

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

Exploring the power of data lakes in machine learning. Read more in an article by Rebecca Day. #Datalake #IPS #IPS®IDL #IntelligentDataLake #Machinelearning #ipssystems

𝐃𝐚𝐭𝐚𝐋𝐚𝐤𝐞 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 Building an efficient Data Lake is a game-changer for any organization aiming to harness the power of big data! It provides a scalable, centralized repository for all your structured and unstructured data. Best practices for Data Lake management include: ✅ 𝐃𝐚𝐭𝐚 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Implement proper partitioning and cataloging for easier access.  ✅ 𝐃𝐚𝐭𝐚 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞: Enforce security, compliance, and access control policies. ✅ 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Use appropriate storage tiers and formats like Parquet or ORC. ✅ 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: Seamlessly connect with analytics and machine learning tools. Mastering these practices ensures your Data Lake remains a valuable asset, driving innovation and business intelligence at scale. -------------------------------------------------- 𝐎𝐮𝐫 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬 👉 Specialized in building the production grade LLM applications and NLP based systems  👉 Expertise to deliver the most advanced,  integrated and full lifecycle of cloud solutions -------------------------------------------------- Follow QuantumEdgeX LLC for more such content. #DataEngineering #DataLake #BigDataInnovation

Takeaway points from this certification: Data Lake House + Generative AI = Databricks Data Intelligent platforms Databricks Data Intelligence Platform support open-source technologies. Databricks is built on Apache Spark, an open-source distributed computing framework. This allows users to leverage the extensive Spark ecosystem, including libraries for machine learning, graph processing, and stream processing. By utilizing open-source technologies, organizations can take advantage of a vibrant community and a wide range of tools and resources. It provides a unified analytics environment, allowing users to perform data engineering, data science, and business intelligence tasks within a single platform. This integration eliminates the need for data movement between different tools and platforms, reducing complexity and improving productivity. Users can seamlessly transition from data exploration and transformation to advanced analytics and machine learning, all within the same environment. Databricks Data Intelligence Platform offers robust data governance capabilities. With features like fine-grained access control and centralized identity management, organizations can ensure that data is accessed and modified only by authorized users. This helps maintain data privacy and compliance with regulations such as GDPR or HIPAA. #databricks

𝐆𝐫𝐚𝐩𝐡 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐌𝐚𝐫𝐤𝐞𝐭 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐚𝐧𝐝 𝐅𝐮𝐭𝐮𝐫𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐅𝐑𝐄𝐄 𝐒𝐚𝐦𝐩𝐥𝐞: https://lnkd.in/d87g_SJf Did you know that the 𝐠𝐫𝐚𝐩𝐡 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐦𝐚𝐫𝐤𝐞𝐭 is projected to surpass $11 billion by 2030? With businesses racing to harness complex relationships in real-time, graph databases are becoming the backbone of AI, fraud detection, and recommendation engines. Unlike traditional databases, graph databases store and process data in a way that mirrors human thought—through relationships. That’s why tech giants, financial institutions, and healthcare providers are rapidly adopting this technology. Investors, take note! The demand for connected data analytics is skyrocketing, making this market a prime space for innovation and growth. 𝐊𝐞𝐲 𝐏𝐥𝐚𝐲𝐞𝐫𝐬: ·       Amazon Web Services (AWS) ·       Hewlett Packard Enterprise ·       IBM ·       Microsoft ·       Neo4j ·       Progress MarkLogic ·       Oracle 𝐀𝐜𝐜𝐞𝐬𝐬 𝐟𝐮𝐥𝐥 𝐫𝐞𝐩𝐨𝐫𝐭: https://lnkd.in/dNvV-iF2 Are you exploring investments in the graph database space? Let’s discuss the opportunities! Drop your thoughts in the comments. #GraphDatabase #Investing #AI #BigData #TechTrends

Quobyte Introduces High-Performance File Query Engine for Large-Scale Storage Clusters and AI Workloads https://lnkd.in/dDXX3xSY #AITech365 #DataManagement #FileQueryEngine #news #Quobyte #storageplatform #StorageSolutions #systemmetadata

At MinIO, our simplicity is what sets us apart. This is particularly true at scale. The explosive growth in computing power due to #AI advancements has significantly influenced the data landscape, ushering us into the era of #exascale data infrastructure. In the case of MinIO, we solve the challenges of large scale data infrastructure for dynamic workloads. That is AI/ML, that is SEIM/threat hunting data lakes, that is HDFS replacements, that is database persistent storage, that is advanced analytics and about a dozen more things. Go into detail with Rakshith Venkatesh on all things exascale: https://hubs.li/Q02rSz0n0

Data migration poses significant challenges with the exponential data growth. Another reason to use Nous DMaaS Solutions (powered by AI) to address these to help unlock the value of enterprise data! To get the size and scale of data explosion read below... #DataMigration #DigitalTransformation #BigData #DataManagement #TechInnovation #CloudComputing #DataStorage #ITInfrastructure #DataSecurity #DataStrategy #TechTrends #EnterpriseTechnology #DataEvolution #DataChallenges  https://lnkd.in/gZNkT8Mu

𝗦𝗧𝗔𝗥𝗚𝗔𝗧𝗘 = 𝗠𝗜𝗖𝗥𝗢𝗦𝗢𝗙𝗧 + 𝗚𝗢𝗢𝗚𝗟𝗘 How 𝗠𝗨𝗖𝗛 𝗔𝗜 𝗗𝗔𝗧𝗔 𝗖𝗘𝗡𝗧𝗘𝗥 𝗖𝗔𝗣𝗔𝗖𝗜𝗧𝗬 does $𝟭𝟬𝟬 𝗕𝗜𝗟𝗟𝗜𝗢𝗡 buy you? The 𝗦𝗧𝗔𝗥𝗚𝗔𝗧𝗘 numbers are 𝗕𝗜𝗚. More than $𝟭𝟬𝟬 𝗕𝗜𝗟𝗟𝗜𝗢𝗡 𝗽𝗲𝗿 𝘆𝗲𝗮𝗿 𝗶𝗻 𝗔𝗜 𝗱𝗮𝘁𝗮 𝗰𝗲𝗻𝘁𝗲𝗿 𝗶𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁 to be deployed over the next 4 years; aiming for a 𝗧𝗢𝗧𝗔𝗟 𝗩𝗢𝗟𝗨𝗠𝗘 of up to $𝟱𝟬𝟬 𝗕𝗜𝗟𝗟𝗜𝗢𝗡. But how does this 𝗦𝗧𝗔𝗖𝗞 𝗨𝗣 versus the ongoing AI data center build out by the 𝗛𝗬𝗣𝗘𝗥𝗦𝗖𝗔𝗟𝗘𝗥𝗦? Well, 𝗠𝗜𝗖𝗥𝗢𝗦𝗢𝗙𝗧 𝗮𝗻𝗱 𝗚𝗢𝗢𝗚𝗟𝗘 spent about $𝟲𝟵 𝗕𝗜𝗟𝗟𝗜𝗢𝗡 𝗰𝗮𝗽𝗲𝘅 𝗼𝗻 𝗔𝗜 𝗱𝗮𝘁𝗮 𝗰𝗲𝗻𝘁𝗲𝗿𝘀 𝗳𝗿𝗼𝗺 𝗝𝗔𝗡-𝗔𝗨𝗚 𝟮𝟬𝟮𝟰. Extrapolating this to the full 𝟭𝟮 𝗠𝗢𝗡𝗧𝗛𝗦 equals ~$𝟭𝟬𝟯 𝗕𝗜𝗟𝗟𝗜𝗢𝗡... hence 𝗦𝗧𝗔𝗥𝗚𝗔𝗧𝗘 will add AI data center capacity at a rate of a 𝗖𝗢𝗠𝗕𝗜𝗡𝗘𝗗 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 + 𝗚𝗼𝗼𝗴𝗹𝗲 https://lnkd.in/gRMyZMu6 https://lnkd.in/g7Cf3hm9 AI Data Center Chart from Visual Capitalist (edits in red are my own) https://lnkd.in/g6bMqWnF #stargate #openai #oracle #softbank #microsoft #google #datacenter #ai #artificialintelligence #aidatacenter #nvidia