Manikandan Paramasivan

Repeatable and reliable PostgreSQL cluster deployments at scale are straightforward with Ansible. A consistent way to deploy PostgreSQL clusters becomes essential when environments are expected to behave predictably across regions, tenants, and multiple production estates. With tools like Ansible, once the cluster layout is defined, the same configuration can be applied across environments via code. Each run follows the same steps and produces the same result, whether it is development, staging, or any production environment. Repeatability and reliability become the default outcome. Every node is built the same way, every time, and PostgreSQL runs on a lean architecture that stays close to the operating system and to its original design. Postgres DeployPro is our signature deployment standard, with pre-tested patterns for HA, connection management, and backup integration that create a resilient foundation, ready to support demanding applications from day one. Learn more: https://lnkd.in/d9ihweDf

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#Airflow,#Apache Take command of your data pipelines with Apache Airflow — an open-source platform that lets you code, schedule, and monitor workflows like a pro. Say goodbye to vendor lock-in, manual intervention, and costly cloud-only tools. With Airflow, you get full Python control, multi-cloud flexibility, advanced scheduling, and rich monitoring

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Want to build applications using data in the lakehouse? Lakebase eliminates the need for custom pipelines, syncing gold-layer data directly into a fully-managed Postgres database.

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Cloud engineers automated deployments, scaling, and recovery. Yet for some reason, we are still manually taking screenshots for compliance. It’s the last manual mile in the cloud, and it’s killing team velocity. Cloud & infra teams get pulled into audits for one reason: they’re the only place the evidence lives. So engineers become the middleman. ·      Screenshots. ·      One-off queries. ·      Digging up logs from "last quarter." It breaks flow and adds zero operational value. What actually works: 1️⃣ Evidence becomes self-serve If data is timestamped and available for GRC and auditors on demand, engineers don’t need to be looped in every time a question comes up. 2️⃣ Engineers stop being translators Cloud teams shouldn’t have to explain configs or permissions to auditors. The data should speak for itself. 3️⃣ History > Snapshots Audits Historical record proves posture over time. If you can’t rewind, you’re stuck chasing screenshots. 4️⃣ Shared visibility reduces friction When everyone looks at the same source of truth, conversations shift from "did you do this?" to "what changed and why?" The outcome is simple: Infra teams stay focused on running the cloud. Evidence is always ready. Audits stop being disruptive. Is your cloud team still spending their time taking screenshots and working for auditors and compliance?

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Amazon S3 - One of the biggest storage system! 100s & 1000s of data lakes are built using Amazon S3 today. It is one of the biggest storage system ever built for cloud. In its simplest form, S3 is an object storage service offering high scalability, data availability, security, and performance. Building such a system needs several long lasting considerations. This incredibly detailed blog by Andy Warfield at S3 elaborates on what went into building such a system. Here's a quick glimpse but highly encourage reading the blog/watching the talk to capture granular details. 🌟 Scale Challenges: Operating S3 involves managing vast scale & complexity, with millions of hard drives and numerous microservices interacting. 🌟 Heat Management: S3 faces unique challenges in balancing I/O demand across disks to prevent hotspots and maintain performance. 🌟 Redundancy: S3 uses replication and erasure coding for both durability and load balancing, optimizing for cost and efficiency. 🌟 Replication and Load Balancing: S3 uses data replication not only to protect against disk failures but also to optimize I/O performance by allowing flexible data access, while erasure coding reduces capacity overhead without compromising durability. Blog link in comments. #dataengineering #softwareengineering

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From SRE to AI-SRE: Role Redesign Blueprint Core Shift: Reactive → Predictive → Autonomous AI-SRE Responsibilities: 1. Intelligent Monitoring & Observability - Deploy ML models to detect drift, anomalies, and performance degradation - Use tools like Prometheus + Grafana + AI plugins, Datadog Watchdog, or New Relic AI 2. Automated Incident Response - Integrate LLM agents into PagerDuty or Opsgenie for: - Auto-triage - Root cause suggestions - Remediation script generation 3. Predictive Scaling & Cost Optimization - Use AI to forecast traffic spikes, memory leaks, or cost overruns - Automate Terraform or Helm deployments based on predictive thresholds 4. Self-Healing Infrastructure - Combine Reinforcement Learning + GitOps to: - Roll back failed deployments - Patch vulnerabilities - Reconfigure services dynamically 5. AI-Augmented Runbooks - Replace static runbooks with LLM-powered agents that: - Interpret logs - Recommend fixes - Simulate outcomes before execution Tools & Stack Evolution Portfolio Ideas: - Build an AI-SRE onboarding deck with animated diagrams - Script a self-healing Terraform pipeline using anomaly detection - Create a YouTube explainer series: “From DevOps to AI-SRE” - Draft a grant pitch for AI-powered reliability tooling in cloud-native teams

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𝗪𝗵𝘆 𝗣𝗮𝗿𝗾𝘂𝗲𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗗𝗲𝗳𝗮𝘂𝗹𝘁 𝗖𝗵𝗼𝗶𝗰𝗲 𝗶𝗻 𝗠𝗼𝗱𝗲𝗿𝗻 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲𝘀 When building data lakes and lakehouses, one question always comes up: 👉 “𝘞𝘩𝘺 𝘥𝘰𝘦𝘴 𝘦𝘷𝘦𝘳𝘺𝘰𝘯𝘦 𝘤𝘩𝘰𝘰𝘴𝘦 𝘗𝘢𝘳𝘲𝘶𝘦𝘵? 𝘞𝘩𝘺 𝘯𝘰𝘵 𝘖𝘙𝘊, 𝘈𝘷𝘳𝘰, 𝘰𝘳 𝘦𝘷𝘦𝘯 𝘑𝘚𝘖𝘕?” Here’s a breakdown every data leader should know 👇 🔹 𝗪𝗵𝘆 𝗣𝗮𝗿𝗾𝘂𝗲𝘁? ✅ Columnar format → stores data by column, making analytical queries lightning-fast. ✅ Efficient compression → repetitive column values compress better with Snappy/GZIP/ZSTD. ✅ Predicate pushdown → scan only required rows/columns (e.g., filter on event_date). ✅ Schema evolution support → can add/drop columns gracefully. ✅ Ecosystem friendly → works natively with Spark, Glue, Athena, Snowflake, BigQuery, Presto. 💡 Result: Smaller storage footprint + faster queries + lower cost. 🔹 𝗪𝗵𝘆 𝗡𝗼𝘁 𝗔𝗹𝘄𝗮𝘆𝘀 𝗢𝗥𝗖? ORC is also a columnar format and sometimes even compresses better than Parquet. So why do most lakehouse projects lean towards Parquet? ORC is tightly coupled to Hive/Presto ecosystems. If you’re migrating to cloud-native systems (Athena, Snowflake, Databricks), Parquet has broader compatibility. ORC has limited adoption outside Hadoop-native workloads, so cross-platform integration is harder. ORC is great for batch analytics in Hive/Presto, but Parquet offers more flexibility in multi-cloud + streaming + ML pipelines. 👉 If you’re fully Hive-based = ORC can win. 👉 If you’re building for hybrid + multi-cloud + open ecosystem = Parquet wins. 🔹 𝗔𝘃𝗿𝗼 – 𝗧𝗵𝗲 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 𝗛𝗲𝗿𝗼 Row-based format → great for Kafka events & serialization. Keeps schema embedded → perfect for schema evolution in streaming. Not great for analytics (slow scans, high storage). 👉 Best for ingestion zones before standardizing into Parquet. 🔹 𝗝𝗦𝗢𝗡/𝗖𝗦𝗩 – 𝗛𝘂𝗺𝗮𝗻-𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆, 𝗠𝗮𝗰𝗵𝗶𝗻𝗲-𝗨𝗻𝗳𝗿𝗶𝗲𝗻𝗱𝗹𝘆 ✅ Easy to generate, human-readable. ❌ Inefficient for analytics (no compression, no schema). ❌ High storage & query cost. 👉 Use only in raw/ingest layer before converting. 🏆 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗶𝗻 𝗠𝗼𝗱𝗲𝗿𝗻 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲 Raw Zone → Avro/JSON (retain fidelity). Clean / Curated Zone → Parquet (optimized analytics). Specialized Zone → ORC (Hive-heavy workloads) or Delta/Apache Iceberg/Hudi (for ACID + versioning). 🔑 Takeaway: Parquet is not “always better” — but it’s the safest choice for performance, ecosystem compatibility, and future-proofing. ORC shines in Hive-based workloads, while Avro rules in streaming pipelines. 💬 What’s your experience? Have you ever chosen ORC over Parquet in production? What tipped the balance? #DataEngineering #BigData #Databricks #Snowflake #AWS #Spark #DataLake #Parquet #ORC #Avro

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