Cloud-native Database Management

I was discussing databases with my mentees, I think people hear these terms time and again and many engineers are unaware of what is what. Usually I just ask folks to pick any decent relational db or a document store they are most comfortable working with and run with it. Usually most things work on most databases unless there are specific use-cases. [I prefer sticking to Azure CosmosDB if I can] But here is my thought-process if I have to make a choice - 1. Relational Databases (RDBMS) The Tools: PostgreSQL, MySQL. Cloud Native: Amazon Aurora, Azure SQL. When to use: - You need strict ACID compliance (Banking, Inventory). - Your data is highly structured with defined schemas. - You need complex joins (e.g., "Find all customers who bought X in May"). 2. Document Stores (Also called in layman terms - NoSQL) The Tools: MongoDB. Cloud Native: Azure CosmosDB, AWS DynamoDB. When to use: - Flexible Schema: Data structure changes frequently (User Profiles, Product Catalogs). - Read/Write Heavy: You generally read the whole "document" at once. 3. Key-Value Stores (Cache) The Tools: Redis, Memcached. Cloud Native: Azure Cache for Redis, AWS ElastiCache. When to use: - Sub-millisecond latency requirements. - Simple lookups (Session management, Shopping Carts, Leaderboards). - Distributed Locking or basic Pub/Sub. Warning: Ensure you are not putting in the cache to bandage a deeper problem. Always ensure you know your eviction and rehydration policies. 4. Wide-Column Stores The Tools: Apache Cassandra, HBase. Cloud Native: Azure Managed Instance for Cassandra, AWS Keyspaces. When to use: - Extreme write throughput (IoT sensor data, Chat history). - Linearly scalable: You need to handle PetaBytes of data. Warning: Reads are fast only if you query by key. Arbitrary searches are slow. Data can be stale. 5. Vector Databases The Tools: Chroma, Pinecone When to use: - AI/ML applications (RAG - Retrieval Augmented Generation). - Storing high-dimensional embeddings. - Semantic Search (Searching by meaning, not just keywords) or Image Similarity. 6. Search Engines (Inverted Index) The Tools: Elasticsearch, Solr. Cloud Native: Azure AI Search, AWS OpenSearch. When to use: - Full-text search (Fuzzy matching, Type-ahead). - Complex filtering and ranking logic (E-commerce product search). 7. Time-Series Databases The Tools: InfluxDB, TimescaleDB. Cloud Native: Azure Data Explorer (Not necessarily time-series but similar capabilities) When to use: - Monitoring metrics (CPU usage, Stock prices). - Data is append-only and queried by time ranges. 8. Graph Databases The Tools: Neo4j. Cloud Native: AWS Neptune, Azure CosmosDB (Gremlin API). When to use: - Deeply connected data (Social Networks, Fraud Detection rings). - "Friends of friends" queries that would kill a SQL DB with joins.

I just built a distributed database from scratch! 🚀 I recently completed a months-long project building ConureDB 🦜, a distributed key-value database entirely from the ground up, using minimal external dependencies to truly understand database internals. This was a 0-to-1 build - implementing everything from the storage layer to distributed consensus. The goal was to understand how these systems actually work rather than just integrating existing solutions. ✨ Key features of ConureDB: • Persistent Storage: B-tree engine with copy-on-write concurrency for lock-free reads and node pooling for reduced allocation overhead and better performance. • Distributed Consensus: Raft algorithm ensuring data consistency across multiple nodes, surviving up to 50% failures. • Client Interfaces: RESTful HTTP API and interactive CLI for database operations with automatic write routing. • Kubernetes Deployment: Cloud-native orchestration with StatefulSets for stable identities and Helm charts for easy deployment. • Fault Tolerance: Nodes automatically discover and join clusters on startup, failed nodes seamlessly rejoin when recovered, preventing data loss and service interruption. The hardest part? Understanding the intricate trade-offs between consistency, performance, and operational complexity. Every design decision - from storage engines to bootstrap strategies - required deep research into how systems like etcd and PostgreSQL actually work under the hood. I have captured this whole account of building a distributed database from scratch in a blog, do check it out! https://lnkd.in/gbhXUxnz And, here's the GitHub project link, feel free to play around with it and give it a ⭐️ if you like it: https://lnkd.in/gRhaawXr

Snowflake — The Cloud Data Platform Redefining Modern Analytics If you’ve worked in data engineering or analytics recently, you’ve probably heard the buzz around Snowflake — but what really makes it special? Let’s break down how Snowflake transformed the way organizations manage, process, and share data in the cloud 👇 🌨️ What Is Snowflake? Snowflake is a fully managed, cloud-native Data Warehouse-as-a-Service (DWaaS) that separates compute, storage, and services, allowing massive scalability, zero maintenance, and real-time data sharing. It’s built natively for the cloud — not a migrated on-prem product — and it runs seamlessly on AWS, Azure, and GCP. ⚙️ The Core Architecture (The 3-Layer Magic) 1️⃣ Storage Layer All data is stored in columnar format inside cloud object storage (S3, Blob, or GCS). Auto-compression & partitioning Handles both JSON and relational data Secure, durable, and auto-scaled 2️⃣ Compute Layer (Virtual Warehouses) This is where your queries actually run. Each virtual warehouse is an isolated compute cluster that can scale up/down or pause when idle. 💡 Multiple teams can query the same data without blocking each other. 3️⃣ Cloud Services Layer Acts as the “brain” — managing metadata, optimization, authentication, and query parsing. This layer ensures performance consistency and zero-maintenance operations. 🧩 Game-Changing Features ✨ Separation of Compute & Storage – Independent scaling, infinite concurrency. ✨ Zero-Copy Cloning – Create full table copies instantly for dev/testing — no extra storage. ✨ Time Travel – Query historical data snapshots (up to 90 days). ✨ Data Sharing – Securely share live data between organizations without data movement. ✨ Semi-Structured Data Support – Store and query JSON, Parquet, Avro using the VARIANT type. ✨ Snowpipe – Near-real-time streaming data ingestion. 🔗 Integrates with the Entire Data Ecosystem Category Examples ETL/ELT Fivetran, Airbyte, dbt, Informatica Orchestration Apache Airflow, Prefect BI Tools Tableau, Power BI, Looker Machine Learning Snowpark (Python/Scala), SageMaker, Vertex AI Streaming Kafka, Pub/Sub, Kinesis via Snowpipe Snowflake fits perfectly into modern data stacks, enabling everything from traditional BI to real-time ML pipelines. 🧠 Real-World Use Case (Data Engineering Flow) Imagine this: Your marketing events are coming through Kafka → stored in S3 → ingested into Snowflake via Snowpipe. Then, dbt models transform it into analytics-ready tables. Finally, Tableau connects directly to Snowflake for business dashboards. ✅ End-to-end pipeline with zero infrastructure management. ✅ Scales to petabytes without breaking. 💬 Your Turn: Are you using Snowflake in your current project? What do you love (or hate) about it — scaling, pricing, or simplicity? #DataEngineering #Snowflake #CloudDataPlatform #DataWarehouse #BigData #dbt #Airflow #Fivetran #DataAnalytics #MachineLearning #Python #GCP #AWS #Azure #AI

Snowflake and Databricks are leading cloud data platforms, but how do you choose the right one for your needs? - 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞 - 𝐍𝐚𝐭𝐮𝐫𝐞: Snowflake operates as a cloud-native data warehouse-as-a-service, streamlining data storage and management without the need for complex infrastructure setup. - 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐬: It provides robust ELT (Extract, Load, Transform) capabilities primarily through its COPY command, enabling efficient data loading. Snowflake offers dedicated schema and file object definitions, enhancing data organization and accessibility. - 𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲: One of its standout features is the ability to create multiple independent compute clusters that can operate on a single data copy. This flexibility allows for enhanced resource allocation based on varying workloads. - 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: While Snowflake primarily adopts an ELT approach, it seamlessly integrates with popular third-party ETL tools such as Fivetran, Talend, and supports DBT installation. This integration makes it a versatile choice for organizations looking to leverage existing tools. - 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 - 𝐂𝐨𝐫𝐞: Databricks is fundamentally built around processing power, with native support for Apache Spark, making it an exceptional platform for ETL tasks. This integration allows users to perform complex data transformations efficiently. - 𝐒𝐭𝐨𝐫𝐚𝐠𝐞: It utilizes a 'data lakehouse' architecture, which combines the features of a data lake with the ability to run SQL queries. This model is gaining traction as organizations seek to leverage both structured and unstructured data in a unified framework. - 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬 - 𝐃𝐢𝐬𝐭𝐢𝐧𝐜𝐭 𝐍𝐞𝐞𝐝𝐬: Both Snowflake and Databricks excel in their respective areas, addressing different data management requirements. - 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞’𝐬 𝐈𝐝𝐞𝐚𝐥 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: If you are equipped with established ETL tools like Fivetran, Talend, or Tibco, Snowflake could be the perfect choice. It efficiently manages the complexities of database infrastructure, including partitioning, scalability, and indexing. - 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐟𝐨𝐫 𝐂𝐨𝐦𝐩𝐥𝐞𝐱 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞𝐬: Conversely, if your organization deals with a complex data landscape characterized by unpredictable sources and schemas, Databricks - with its schema-on-read technique - may be more advantageous. 𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧: Ultimately, the decision between Snowflake and Databricks should align with your specific data needs and organizational goals. Both platforms have established their niches, and understanding their strengths will guide you in selecting the right tool for your data strategy. 𝗜 𝗵𝗮𝘃𝗲 𝗽𝗿𝗲𝗽𝗮𝗿𝗲𝗱 𝗮 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 𝗚𝘂𝗶𝗱𝗲 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀. 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗚𝘂𝗶𝗱𝗲 𝗵𝗲𝗿𝗲 👉 https://lnkd.in/eQRtDTRq If you’ve read this far, LIKE ❤️ and RESHARE 🔄 the post!

Choosing the Right Database Made Simple As data engineers, picking the right database can be tricky. It’s not just about storing data—it’s about understanding its journey. Here's a quick guide: 🔴 Data Flow Patterns Heavy Writes: Use Apache Cassandra or TimescaleDB for time-series data. Read-Heavy Apps: Go for Redis or MongoDB with read replicas. ACID Compliance: PostgreSQL or MySQL are your go-to options. 🔴 Scaling Needs Horizontal Scaling: Choose DynamoDB or Cassandra for distributed systems. Vertical Scaling: PostgreSQL works well for single powerful instances. Global Reach: CockroachDB or Azure Cosmos DB for multi-region setups. 🔴 Data Complexity Complex Relationships: Neo4j for graph-based data. Document Storage: MongoDB or CouchDB for flexible schemas. Time-Series Data: InfluxDB or TimescaleDB. Search-Heavy Apps: Elasticsearch for full-text search. 🔴 Operational Overhead Managed Services: Cloud options like RDS or Atlas for less maintenance. Self-Hosted: Choose based on team expertise. Backup & Recovery: Check for replication and recovery features. 🔴 Performance Query Patterns: Optimize for frequent queries. Indexing: Ensure efficient indexing. Memory vs. Disk: Use Redis for ultra-low latency. 🔴 Costs Storage Growth: Plan for scaling expenses. Query Costs: Monitor costs in cloud-based solutions. Operational Costs: Include monitoring and maintenance. Real-World Examples: User Tracking: Cassandra (high write throughput). Financial Transactions: PostgreSQL (ACID compliance). Content Management: MongoDB (flexible schema). Real-Time Analytics: ClickHouse (fast aggregations). Cache: Redis (in-memory, fast). Pro Tip: Start with a proven solution like PostgreSQL unless you need something specific. Scaling a reliable system is easier than fixing an exotic one in production. Cloud Database Options: AWS: DynamoDB, ElastiCache, Redshift. Google Cloud: BigQuery, Firestore, Cloud SQL. Azure: Cosmos DB, Redis Cache, Data Lake Storage. CC:Rocky Bhatia #Data #Engineering #SQL #Databases

If you are responsible for your company’s data platform, you should take a very close look at this image. At first glance it may look simple, but if you really think about it, it solves a surprising number of problems that most organizations face when running databases in the cloud. For years, the dominant model has been the one promoted by Oracle. The idea is straightforward: you can run your applications on any cloud, but your databases are operated by Oracle on Oracle infrastructure. With services like Oracle Autonomous Database and engineered systems such as Oracle Exadata, Oracle has extended its database platform into other hyperscalers. In practice, Oracle brings its database infrastructure close to other clouds like **Microsoft Azure, **Google Cloud, or **Amazon Web Services. This model provides: • dedicated database infrastructure • predictable performance • integrated database services • reduced operational overhead For many organizations, this has been a very attractive approach. But it also comes with a fundamental architectural choice: Your data platform is tightly coupled to a single vendor ecosystem. There is now a different model emerging. Instead of moving your databases into a specific cloud provider’s database platform, you introduce a database automation layer that runs consistently across clouds. This is the idea behind Nutanix Database Service (NDB). With NDB running on Nutanix Cloud Clusters (NC2), organizations can operate enterprise databases across multiple clouds while keeping: • full infrastructure control • predictable performance on dedicated infrastructure • automated database lifecycle management • consistent operations across environments And importantly, this model is database engine agnostic. The same platform can operate databases such as: • Oracle Database • Microsoft SQL Server • PostgreSQL • MongoDB • MySQL • MariaDB And you can immediately automate: provisioning, scaling, BC/DR, patching, backup and restore. You start with the dedicated infrastructure you require and scale based on your need. The is a new sheriff in town for enterprise data management.

🚀 Demystifying the Data Lifecycle in the Cloud – Your Ultimate Matrix for Cloud-Native Data Management! 😎 Every organization generates data, but are you managing that data effectively through its full lifecycle—from creation to deletion—while ensuring security, governance, and actionable insights? To help bridge that gap, I've created a cloud-agnostic matrix that maps out how AWS, Azure, and GCP support each stage of the data lifecycle. This visual cheat sheet is designed for architects, engineers, data professionals, and tech leaders to quickly identify the right tools and services for their needs. 📊 What’s Inside: ✅ Lifecycle Stages & Key Tasks: Data Creation, Storage, Usage, Archiving, and Destruction ✅ Cloud-Native Services: A side-by-side look at AWS, Azure, and GCP offerings ✅ Comprehensive Coverage: Tools for ingestion, real-time processing, machine learning, business intelligence, data loss prevention, audit logging, data lineage, and more 💬 Let's Discuss: What tools or patterns are you using in your cloud projects? Are there any services you love (or avoid)? #DataArchitecture #CloudComputing #AWS #Azure #GCP #EnterpriseArchitecture #DataGovernance #DataStrategy #DigitalTransformation #DataLifecycle #AI #ML

Lift and shift is the most expensive way to avoid real cloud transformation. Moving your mess to the cloud just gives you an expensive mess. At Mayfair IT, we have built cloud platforms using fundamentally different approaches. The difference in outcomes is dramatic. Lift and shift is seductive. Take existing servers, virtualise them, run them in Azure or AWS. Call it cloud migration. Declare victory. The infrastructure is now in the cloud. The problems are unchanged. Applications still assume they run on dedicated hardware. Scaling requires manual intervention. Failures cascade because nothing was designed for distributed failure. You pay cloud prices for on premises architecture. What cloud native actually means, We have built greenfield platforms on Azure designed from the beginning for cloud. Platform as a Service and Software as a Service components doing what they do best. Azure Data Factory orchestrating data pipelines instead of custom ETL running on virtual machines. Cosmos DB providing distributed databases instead of clustered SQL servers. Serverless functions handling event driven workloads instead of always on application servers. The difference is economic and operational. What changes with cloud native architecture: → Scaling happens automatically based on demand, not manual capacity planning → Failures in individual components do not bring down entire services → You pay only for resources actually used, not capacity provisioned for peak load → Updates deploy without downtime because architecture assumes continuous change We have also migrated legacy systems to cloud where complete refactoring was not feasible. The challenge is knowing which approach fits which situation. Greenfield builds should always be cloud native.  Legacy migrations require honest assessment of whether lift and shift provides enough value to justify the effort. Sometimes the answer is yes.  Moving a stable system with known workloads to cloud can reduce operational overhead even without refactoring. But presenting lift and shift as cloud transformation is dishonest.  You moved the location. You did not change the architecture. The organisations getting real cloud value are the ones willing to rebuild applications to use cloud capabilities properly. How much of your cloud spending is on virtualised servers that could be replaced by managed services? #CloudNative #Azure #DigitalTransformation

𝗠𝗶𝗴𝗿𝗮𝘁𝗶𝗻𝗴 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝘁𝗼 𝗔𝗪𝗦: 𝗥𝗗𝗦, 𝗔𝘂𝗿𝗼𝗿𝗮, 𝗼𝗿 𝗗𝘆𝗻𝗮𝗺𝗼𝗗𝗕?  How to Choose the Right One Choosing the right database service on AWS isn’t just a technical decision; it’s a strategic one. Whether migrating from on-premises systems or optimizing existing cloud workloads, picking between Amazon RDS, Aurora, and DynamoDB can significantly impact your cost, performance, and scalability.  Let’s dive into the details to help you make an informed choice: 1️⃣ 𝗔𝗺𝗮𝘇𝗼𝗻 𝗥𝗗𝗦 (𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗦𝗲𝗿𝘃𝗶𝗰𝗲) – 𝗧𝗵𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗱 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 RDS offers managed relational databases like MySQL, PostgreSQL, SQL Server, and Oracle, handling backups, scaling, and patching for you. 𝗣𝗿𝗼𝘀: • Familiar environments for existing applications. • Automated backups, scaling, and failover. • Ideal for applications requiring strong ACID compliance. 𝗖𝗼𝗻𝘀: • Scaling can be slower compared to cloud-native options. • Licensing costs for proprietary engines (e.g., Oracle, SQL Server). 💡 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: Traditional applications that need relational database capabilities with minimal refactoring. 2️⃣ 𝗔𝗺𝗮𝘇𝗼𝗻 𝗔𝘂𝗿𝗼𝗿𝗮 – 𝗧𝗵𝗲 𝗖𝗹𝗼𝘂𝗱-𝗡𝗮𝘁𝗶𝘃𝗲 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗣𝗼𝘄𝗲𝗿𝗵𝗼𝘂𝘀𝗲 Aurora is MySQL and PostgreSQL-compatible but turbocharged for the cloud, offering auto-scaling, multi-region replication, and high availability. 𝗣𝗿𝗼𝘀: • Up to 5x faster than standard RDS for MySQL/PostgreSQL. • Built-in fault tolerance and high availability. • Cost-effective with Aurora Serverless (pay-as-you-go). 𝗖𝗼𝗻𝘀: • Higher costs for smaller workloads compared to RDS. • Limited to MySQL and PostgreSQL compatibility. 💡 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: High-performance applications that demand scalability, availability, and cloud-native efficiency. 3️⃣ 𝗔𝗺𝗮𝘇𝗼𝗻 𝗗𝘆𝗻𝗮𝗺𝗼𝗗𝗕 – 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗡𝗼𝗦𝗤𝗟 𝗮𝘁 𝗦𝗰𝗮𝗹𝗲 DynamoDB is a fully managed, serverless NoSQL database designed for key-value and document workloads with millisecond latency. 𝗣𝗿𝗼𝘀: • Instant auto-scaling to handle unpredictable workloads. • Zero maintenance with built-in fault tolerance. • Pay-per-use pricing model. 𝗖𝗼𝗻𝘀: • It is not ideal for complex queries or joins. • Requires NoSQL expertise for efficient data modeling. 💡 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: Applications needing high throughput and low latency, such as IoT, gaming, or e-commerce. 𝗪𝗵𝗶𝗰𝗵 𝗢𝗻𝗲 𝗦𝗵𝗼𝘂𝗹𝗱 𝗬𝗼𝘂 𝗖𝗵𝗼𝗼𝘀𝗲? 𝗥𝗗𝗦: Stick with RDS if you need a managed version of a traditional relational database. 𝗔𝘂𝗿𝗼𝗿𝗮: Choose Aurora for cloud-native performance, scalability, and advanced features. 𝗗𝘆𝗻𝗮𝗺𝗼𝗗𝗕: Opt for DynamoDB if you need a serverless NoSQL solution with extreme scalability and low latency. 💡 𝗣𝗿𝗼 𝗧𝗶𝗽: For hybrid use cases, consider a multi-database strategy—use RDS or Aurora for transactional data and DynamoDB for high-speed lookups. 𝗪𝗵𝗮𝘁’𝘀 𝗬𝗼𝘂𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲? #AWS #awscommunity 

The database administrator role is undergoing its most significant transformation in decades, and Oracle is leading the charge by integrating artificial intelligence directly into the database platform itself. Gone are the days when DBAs spent their time on routine maintenance, manual tuning, and reactive troubleshooting. Today’s DBA is evolving into a strategic data architect and AI enabler, empowered by intelligent automation that handles the mundane while unlocking new possibilities for business innovation. Oracle has reimagined database administration as a comprehensive AI-native platform. The Autonomous Database eliminates manual tuning and patch management, allowing DBAs to focus on higher-value activities. Vector search capabilities and ONNX model integration bring machine learning directly to where the data lives, eliminating the complexity of data movement and external processing. The RAG pipeline enables sophisticated retrieval-augmented generation workflows, while SELECT AI introduces natural language querying that democratizes data access across the organization. What makes this transformation remarkable is that everything remains within the Oracle ecosystem. DBAs no longer need to cobble together disparate tools, manage multiple vendor relationships, or worry about data governance across fragmented platforms. From AI enrichment to conversational interfaces, from multi-cloud portability through MCP bridges to complete platform integration, Oracle has created a self-contained intelligent database environment. The modern DBA is transitioning from database operator to data strategist, from problem-solver to innovation catalyst. With AI handling the operational overhead, database professionals can now architect solutions that directly drive business outcomes, implement advanced analytics at scale, and deliver insights at the speed of conversation. The question is no longer whether AI will transform database administration, but rather how quickly organizations will embrace this integrated approach to unlock the full potential of their data and their people. #OracleDatabase #AutonomousDatabase #DatabaseAdministration #DBA #ArtificialIntelligence #MachineLearning #DataManagement #VectorSearch #RAG #SelectAI #EnterpriseAI #DataStrategy #CloudDatabase #DatabaseAutomation #AIIntegration #DataArchitecture #DigitalTransformation #EnterpriseData #OracleTechnology #DatabaseInnovation #AIinDatabase #DataOps #MLOps #ModernDBA #IntelligentDatabase