Shashank Verma

Unlock Real-Time Insights! Dive into live analytics with Azure Databricks and Power BI in this hands-on demo! 🔍📊 💡 Build a real-time streaming pipeline using Delta Live Tables (DLT) 🛠️ Ingest raw JSON data & configure continuous mode pipelines 📈 Watch real-time dashboards come to life with Direct Query ⚡ Learn best practices for low-latency streaming analytics 🎥 Watch now: https://lnkd.in/d7uxUSWu #AzureDatabricks #PowerBI #StreamingAnalytics #DeltaLiveTables #RealTimeData #ITBusiness

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Manual documentation can’t keep up with modern data pipelines. Traxccel automates the process, using LLMs and Databricks metadata to generate clean, version-controlled docs embedded into your CI/CD workflows. The result is scalable, audit-ready documentation that’s always in sync with your data logic. Read the blog and see why it’s redefining documentation across the enterprise: https://lnkd.in/dkdXEsBq #ETLautomation  #DataEngineering #LLM #Databricks #TechnicalDocumentation  #axlPULSE #Traxccel 

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As Databricks environments scale, the challenge shifts. It is no longer about adopting features. It is about execution efficiency, governance, and cost control. Entrada 𝗙𝗼𝗿𝘄𝗮𝗿𝗱 𝗗𝗲𝗽𝗹𝗼𝘆𝗲𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 (𝗙𝗗𝗘) works inside complex Databricks implementations to modernize configuration, tune performance, establish guardrails, and optimize cost structure. If your Databricks environment is growing and efficiency is becoming strategic, let’s connect. 📨 #Databricks #DataEngineering #CloudOptimization

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🦈📡 Connecting Megaladata with Apache Kafka: Real-time data streaming with purpose. In a world where data moves fast, legacy batch pipelines fall behind. That’s where Apache Kafka comes in. As a distributed event streaming platform, Kafka acts as the "central nervous system" for your data: ingesting, buffering, and distributing continuous streams of events from producers to consumers without delay. The Shift to Real-Time In practical terms, the Kafka integration allows organizations to: ✨Instantly Capture: Ingest live transactions, sensor logs, and user events the moment they occur. ✨Decouple Systems: Allow producers and consumers to scale independently, removing architectural bottlenecks. ✨Slash Latency: Move from "yesterday’s reports" to "this second’s insights." The Megaladata + Kafka Advantage: By integrating Megaladata with Kafka, you bridge the gap between high-speed data transport and sophisticated analytics. Megaladata acts as both a consumer (reading streams for analysis) and a producer (sending transformed data back to Kafka topics). Why this integration changes the game: 🚀 Eliminate "Batch Lag": Megaladata ingests data from Kafka topics as it is generated, replacing stale nightly uploads with a live, consistent data flow. 📊 Low-code stream processing: Skip the complex Java/Scala boilerplate. Use Megaladata’s low-code interface to apply complex transformations and deliver up-to-the-minute dashboards. 🤝 Flexible, unified pipelines: Connect multiple data sources without custom coding. Kafka serves as the backbone, and Megaladata serves as your intelligent analytics engine. 🔁 Resilience at scale: Use Kafka’s industry-leading fault tolerance alongside Megaladata’s orchestration to ensure your pipelines scale with your growth. Turning Events into Action This integration is the gold standard for use cases where every second counts: fraud detection, IoT telemetry, fleet tracking, and financial transaction processing. Megaladata + Kafka turns raw event streams into actionable insight, closing the gap between observation and decision. If your business depends on responsiveness, streaming is no longer optional—it’s the new baseline. 🔥 🧠 Ready to bring streaming data into your analytics workflows? Let’s talk about how Megaladata + Kafka can transform your real-time data strategy. #Megaladata #ApacheKafka #StreamingAnalytics #DataEngineering #RealTime #DataPipelines #LowCode #Automation

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Scaling Databricks effectively while maintaining clean, reliable data is a challenge many enterprises face as they expand their AI capabilities. Data quality directly impacts AI model performance, and managing large-scale data pipelines requires...

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AI pilots don’t die because the models are bad. They die because everything around the model is chaos. Databricks Agent Bricks is Databricks’ answer to that chaos: a structured way to design, evaluate, and deploy AI agents directly inside the Lakehouse, where data, governance, and cost controls already live. We took a deeper look at what Agent Bricks actually does, what problems it solves, and why it’s becoming one of the most important agentic AI frameworks Databricks has introduced so far. If you want a clear, practical explanation of how Agent Bricks actually works, start here: 👉 https://lnkd.in/d73TmWBs #Databricks #Datapao #AgentBricks #AgenticAI #GenAI

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Struggling to get full visibility into your Databricks workloads? You're not alone. 😣 Data teams across the globe face similar challenges in optimizing cloud data costs, streamlining compute operations, and ensuring overall data health. Discover how Revefi automates FinOps, DataOps, and Data Observability to give you crystal-clear insights into warehouse costs, efficiency, and overall performance. Download our 2 page brochure here: https://lnkd.in/gQvTaK5S #Databricks #FinOps #DataOps #DataObservability #CloudCostManagement

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Data leaders are moving from legacy ETL to the Databricks Lakehouse, unlocking scalability, performance, and cost efficiency. KPI Partners demonstrated to Gartner how our Informatica-to-Databricks Migration Utility enables enterprises to achieve: • Up to 70% faster migrations • 80% automated code conversion • Cloud-native, AI-ready data ecosystems Modernize with confidence → https://lnkd.in/gECydTkr   Kusal Swarnakar | Sid Goel | Rajesh Ramachandran | Prashanth Ashwathram | Mayank Mishra | Akshay Singh Chauhan | Flory Morris | Nitin Sharma | Fari Breguet | Michael Tennant | Daniel Shaw | Darren Chilton | Kraig Sauter | Joey Beitdashtoo #KPIPartners #Databricks #DataModernization #CloudTransformation #EnterpriseData #AnalyticsInnovation

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ETL vs ELT: One choice. Two very different outcomes. ETL offers control and compliance for structured data. ELT delivers speed and scale for modern cloud pipelines. Both solve different problems—and both come with trade-offs in cost, compliance, performance, and flexibility. We break down: ✔️ Core differences (beyond the acronyms) ✔️ Real-world use cases ✔️ Cost, compliance, and infrastructure factors 🔗 Read the full blog to find the right fit for your data architecture: https://buff.ly/L8B9yHr #ETL #ELT #dataengineering #databricks #microsoftfabric #datastrategy #digitaltransformation

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💡 The Missing Link Between Your Data Lake and Real Business Impact Enterprises spend millions building data platforms, yet 70% fail to see real business value. The missing link? Enterprise-Grade Orchestration. With iVedha Inc.’s Apache Airflow as a Service, you can: 👉 Automate workflows end-to-end 👉 Turn data lakes into AI-ready ecosystems 👉 Deliver insights 40% faster 👉 Unlock true ROI and business growth Because at iVedha, orchestration isn’t infrastructure — it’s intelligence in motion. Discover how Airflow as a Service transforms data into decisions: https://lnkd.in/ep_SX-Ye #iVedhaInc #Airflow #ApacheAirflow #DataStrategy #DigitalTransformation #CIO #CFO #CEO #BusinessImpact #DataEngineering #AI #Automation #DataModernization #CloudComputing #BigData #DataAnalytics #TechInnovation #DataScience #DataPipeline #WorkflowAutomation

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Vantage now supports billing data ingestion from Databricks System Tables, for the most accurate and native way to visualize your Databricks costs. Previously, before Databricks released System Tables, we ingested cost data from Billable Usage Logs, which provided SKU-level usage at list prices, but didn’t include discounts. That meant users had to do some manual work to reconcile their actual costs. With the new System Tables integration, users can get even more precise usage quantities and see the list price before and after discounts, with no manual work. This makes it easier than ever to visualize and optimize Databricks costs alongside 20+ other native integrations.

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Snowflake’s Snowpipe Streaming just got a major upgrade, and we’ve broken it all down for you. From blazing-fast ingestion speeds to seamless SDK integration and in-flight transformations, this is a game-changer for modern data pipelines. 🔍 Learn how your organization can move beyond batch processing and embrace true real-time analytics in this blog post by Chris Schneider here: https://lnkd.in/gpFFqY7f #DataEngineering #Snowflake #RealTimeAnalytics #evolvConsulting #SnowpipeStreaming

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Data teams are under pressure. • Snowflake costs are rising • ETL pipelines are fragile • Business users want real-time dashboards • Internal teams are overloaded If your organization relies on Snowflake and complex ETL workflows, stability and performance are no longer optional — they are mission-critical. At NTech1, we provide dedicated offshore ETL and Snowflake engineering support for companies that need scalable, production-grade data infrastructure. What we help with: ✔ Snowflake architecture & performance optimization ✔ ETL pipeline development (dbt, Airflow, Informatica, Talend, SSIS, Python) ✔ Data warehouse migration & modernization ✔ Cost optimization & query tuning ✔ Real-time & batch data integration ✔ Ongoing managed data platform support We operate as an extension of your internal data team — not just as a vendor. Why companies choose NTech1: • Senior engineers with strong architecture background • US time-zone alignment • Structured delivery & governance model • Enterprise-level experience without enterprise overhead If your Snowflake environment needs optimization, stabilization, or scale — let’s connect. 📩 Reach out directly at Sai@ntech1.com 🌐 www.NTech1.com #Snowflake #ETL #DataEngineering #OffshoreSupport #DataWarehouse #CloudData #Analytics #NTech1

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Struggling to manage enterprise-scale data across clouds and formats? Discover how Informatica's Lakehouse architecture solves today’s toughest data integration challenges—while powering AI, analytics, and scale. Learn how OpenTable formats like Iceberg and Delta are reshaping modern data architecture. 👉 Read the post: https://lnkd.in/gyG2XWAv #DataLakehouse #DataIntegration #AI #CloudData #Informatica #SponsoredPost

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Most enterprises already have the data they need for AI. The issue? It’s stuck in systems that weren’t built for real-time or intelligence at scale. Redis Data Integration changes that. RDI continuously syncs data from existing databases into Redis Cloud on AWS, no migrations required. Your source of truth stays put. Your apps get sub-millisecond, real-time access. Read more: https://lnkd.in/eUx6pVk2

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🚀 𝐁𝐚𝐭𝐜𝐡 𝐨𝐫 𝐒𝐭𝐫𝐞𝐚𝐦𝐢𝐧𝐠? 𝐖𝐡𝐲 𝐧𝐨𝐭 𝐛𝐨𝐭𝐡 𝐰𝐢𝐭𝐡 𝐀𝐩𝐚𝐜𝐡𝐞 𝐁𝐞𝐚𝐦! Discover how a single Beam pipeline can power historical ETL and real-time analytics using windowing, watermarks, and runner portability across Google Cloud Dataflow, Apache Flink, and Apache Spark. Plus, learn how the book 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 𝐔𝐬𝐢𝐧𝐠 𝐀𝐩𝐚𝐜𝐡𝐞 𝐁𝐞𝐚𝐦 helps you implement these concepts step-by-step in production. 𝑹𝒆𝒂𝒅 𝒕𝒉𝒆 𝒃𝒍𝒐𝒈 & 𝒍𝒆𝒗𝒆𝒍 𝒖𝒑 𝒚𝒐𝒖𝒓 𝒅𝒂𝒕𝒂 𝒆𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈 𝒂𝒑𝒑𝒓𝒐𝒂𝒄𝒉! For International - https://lnkd.in/gXMkagUh For India - https://lnkd.in/g777wKji #ApacheBeam #DataEngineering #StreamingData #BatchProcessing #ETL #BigData #DataPipelines #RealTimeAnalytics

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