Historical Data Analysis for IT Systems

Using Artificial Intelligence (AI) and Machine Learning (ML) in a Data Center environment. Why? An AI/ML platform that integrates IT and OT data from DCIM (Data Center Infrastructure Management), BAS (Building Automation Systems), EMIS (Energy Management Information Systems), and Power Monitoring systems can offer numerous valuable analytics for data center facilities and IT teams. Key analytics include: Predictive Maintenance: Analyze historical data from DCIM, BAS, and Power Monitoring systems to predict when equipment like cooling systems, UPS units, and power distribution units might fail. This can prevent downtime and extend the lifespan of the equipment. Energy Optimization: Use EMIS and Power Monitoring data to identify energy usage patterns and detect inefficiencies in cooling and power systems. Recommend adjustments to setpoints, load balancing, or equipment usage for optimal energy consumption. Capacity Planning: Leverage DCIM data to analyze resource utilization (power, cooling, space) and predict future capacity needs based on historical growth trends. Anomaly Detection: Monitor IT and OT systems to detect unusual patterns that could indicate potential security breaches, equipment malfunctions, or network issues. Cross-System Correlations: Identify correlations between IT workload data (from servers and network devices) and OT data (from power and cooling systems) to optimize the environment, ensuring that power and cooling resources align with IT workload demands. Environmental Monitoring: Use BAS data for climate control monitoring (temperature, humidity, airflow) to identify hotspots or areas that are overcooled, potentially adjusting airflow to balance the environmental conditions. To provide these analytics, the platform would need access to the following data points: From DCIM: Asset details, location information, power and cooling consumption, space utilization, historical incidents, and maintenance logs. From BAS: Temperature, humidity, airflow data, setpoint configurations, and control system logs. From EMIS: Historical and real-time energy consumption data across devices, areas, and trends in peak usage times. From Power Monitoring Systems: Real-time and historical data on voltage, current, and power factor; alarms and alerts; and load distribution information across the facility. Integrating these data points allows the AI/ML platform to offer comprehensive analytics, predictive insights, and actionable recommendations for both IT and facility management teams. https://lnkd.in/eN97jYDe #DataCenter #COLO

I’ve led SAP migrations since the early 2000s. Some were for global manufacturers, others for Fortune 100s. And what I’ve learned is that the most dangerous thing in a migration isn’t a broken tool - it’s a blind spot. That’s why we always start with data. Not dashboards. Not scripts. Data. You can have the best tools, clean code, sharp engineers…but if you don’t know what you’re migrating in detail? None of that matters. I’ve seen teams spend weeks tuning their automation platform… only to realize halfway through that they were missing a key dependency or running outdated sizing assumptions. That’s how migrations fall apart: not from action, but from assumptions. Here’s why we always start with raw data — not tools: 1. Tools inherit whatever bias you feed them. Most platforms only see what you tell them to scan. If your inventory is wrong or incomplete, the outputs are just dressed-up guesses. 2. Data reveals your real architecture. We look at actual CPU use, memory allocation, storage trends, and job schedules — not just system names in a spreadsheet. 3. Baseline metrics drive realistic timelines. If your export step took 20 hours in staging, it’s not magically going to take 6 in prod. We use historical data to model time, cost, and effort. 4. Trends matter more than snapshots. A single performance snapshot doesn’t tell you when bottlenecks will hit. But trending data - over days or weeks - shows your real risk zones. 5. Data gives you leverage in the business conversation. Executives want facts. When you walk in with precise downtime windows, impact models, and “what-if” simulations - they listen. At IT-Conductor Inc., we start every engagement by pulling the data first. Then we build the plan. Then we use the tools. Because when the stakes are high - and they always are - visibility beats velocity.

Query Optimizer in Data Systems. While trying to read a bit more on query optimizers, I stumbled upon the work that's been going on for historical-based optimization in PrestoDB (Presto Foundation). Query optimizers in database systems are critical because they select the most efficient execution plan for a query. They improve performance by minimizing resource usage (CPU, memory, network) and reducing execution time. To produce an optimal query plan, the system needs to estimate cardinalities (super critical) and computational costs for intermediate nodes. And this will depend on structure of the query and the way data is distributed. These are also tough problems to solve! Presto's "History-based Optimization (HBO)" targets this aspect & brings in a historical learning-based approach. ✅ The HBO framework improves query performance by using historical statistics, such as row counts, data sizes, and CPU usage, from previously executed queries. ✅ HBO optimizes join order & aggregations, reducing data transfer/memory consumption by choosing the most resource-efficient plans based on historical data. ✅ It integrates Redis to store and retrieve historical statistics, which allows Presto to cache these statistics locally, improving query optimization speed. ✅ Uber deployed HBO in production environments & saw performance gains of up to 50%, especially in complex queries involving large joins. Check out the blog and VLDB paper link in comments if you want to dive deep into the techniques. #dataengineering #softwareengineering

𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗔𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: 𝗧𝗵𝗲 𝗢𝗟𝗧𝗣 𝗮𝗻𝗱 𝗢𝗟𝗔𝗣 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 . . 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄: Every business relies on data, but using it effectively is key. OLTP (Online Transaction Processing) ensures smooth real-time operations, while OLAP (Online Analytical Processing) enables in-depth analysis of historical data for better decision-making and long-term strategy. Together, they drive both operational efficiency and strategic growth. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗢𝗟𝗧𝗣? OLTP systems are designed for real-time transaction processing, ensuring seamless day-to-day operations. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀: ➤ E-commerce platforms for order placement. ➤ Banking systems for real-time transactions like deposits and withdrawals. ➤ Customer Relationship Management (CRM) systems to track and update customer interactions. 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝗢𝗟𝗧𝗣: ➤ Handles high transaction volumes with speed and accuracy. ➤ Optimized for concurrent user operations, ideal for large-scale, real-time environments. ➤ Ensures data integrity in dynamic, transaction-heavy scenarios. ➤ Supports ACID properties (Atomicity, Consistency, Isolation, Durability) for transaction reliability. ➤ Provides real-time data access, ensuring business continuity. ➤ Minimizes latency for quick and responsive transactions. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗢𝗟𝗔𝗣? OLAP systems empower businesses with data analytics and decision-making capabilities by processing historical and aggregated data. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀: ➤ Data warehouses for generating sales performance reports. ➤ Executive dashboards for monitoring KPIs. ➤ Marketing analytics to evaluate campaign effectiveness. 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝗼𝗳 𝗢𝗟𝗔𝗣: ➤ Enables deep analysis through multi-dimensional querying. ➤ Provides valuable insights into trends and patterns to inform strategic decisions. ➤ Designed for read-intensive workloads and large datasets, ensuring scalability and efficiency. ➤ Supports complex aggregations, summarizing data from different angles. ➤ Ideal for business intelligence tools, delivering actionable insights. ➤ Improves decision-making by uncovering hidden relationships in data. 𝗞𝗲𝘆 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 𝗮𝗻𝗱 𝗪𝗵𝗲𝗻 𝘁𝗼 𝗨𝘀𝗲 𝗧𝗵𝗲𝗺 𝗢𝗟𝗧𝗣 is ideal for handling real-time operations and ensuring business continuity, such as in transactional systems where data integrity and speed are critical. 𝗢𝗟𝗔𝗣 is perfect for strategic analysis, empowering decision-makers with actionable insights derived from historical data, such as performance analytics or business intelligence. 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲: OLTP and OLAP systems work together to drive both operational efficiency and strategic insights. A balanced approach to both can transform your business into a data-driven enterprise. #DataStrategy #TechTrends #DataManagement #DataArchitecture #DecisionMaking #DigitalTransformation #DataProcessing #BigData #EnterpriseData #Analytics