Chaos Genius

Chaos Genius is proud to sponsor the #DataAISummit by Databricks!! Crazy excitement about Data & AI ROI as organizations embark on their AI journeys. If you're attending and looking to optimize your costs on Databricks, visit us at booth #81 Team Chaos Genius - Amatullah Sethjiwala Sahan Penakalapati Manas Solanki Kartikay Bagla Varun Purushotham Bhargav S. kumar Debanjan Dey Amogh Dhar Diwan Amrasha Srivastava Preeti Shrimal Poorva Mendiratta #chaosgenius #databricks #DataAISummit #finops #Moscone

#Databricks? Great! AWS #EMR? Also powerful! But which one works best for you? ?? Databricks: ?? A cloud-based Lakehouse platform built on #ApacheSpark — designed for unified #dataengineering, collaborative #datascience, and scalable #machinelearning across multi-cloud environments. ?? Architecture—Two-layer design with a managed control plane and a decoupled compute plane (serverless or classic clusters), leveraging cloud-native storage like #S3, #ADLS and #GCS via #DBFS. ?? Data Processing—Optimized for both batch and real-time streaming, featuring built-in #Photon acceleration, #DeltaEngine, and #MLflow for #AI and #ML workloads. ?? Deployment Options—Multi-cloud availability on #AWS, #Azure, and #GCP, offering a fully managed experience with deep cloud integrations. ?? Ecosystem Integrations—Seamless connectivity with #BI tools (#Tableau, #PowerBI, #Looker), #datalakes, and #datawarehouses. Supports Delta Sharing for cross-platform collaboration and growing #generativeAI integrations. ?? Security—Comprehensive #RBAC and fine-grained access control via #UnityCatalog, encryption at rest/in transit, and compliance-ready governance. ?? Pricing Models—Consumption-based #DBU (Databricks Unit) pricing, available as pay-as-you-go or reserved capacity for cost optimization. ?? AWS EMR: ?? A fully managed big data cluster platform on AWS that simplifies running #ApacheSpark, #Hadoop, #Hive, #Presto, and other frameworks on scalable #EC2 clusters. Deeply integrated with #AWS services. ?? Architecture—Cluster-based design with Primary, Core, and Task nodes. Uses #HDFS for ephemeral storage and #EMRFS as an S3 abstraction layer for persistent storage. ?? Data Processing—Supports multiple big data frameworks for batch processing, streaming analytics, and interactive queries. ?? Deployment Options—Optimized for AWS environments with options like EMR on EKS, Outposts, and Serverless. ?? Ecosystem Integrations—Tight AWS service integration (#S3, #Glue, #CloudWatch, #IAM) and extensive BI tool support. ?? Security—AWS IAM-based control, encryption mechanisms, VPC isolation, and auditing via #CloudTrail. ?? Pricing—Pay-as-you-go, with options for On-demand, #SpotInstances, #ReservedInstances, and #SavingsPlans. So, which one should you pick? Welp! It depends on your needs: ?? For unified data engineering & ML => Databricks ?? For AWS-native big data workloads => EMR Check out the article below for a more in-depth comparison of Databricks vs AWS EMR. Dive right in! https://lnkd.in/gGZYWbwy

#Databricks is a unified analytics platform that offers everything you need in one place. It is built on top of #ApacheSpark and provides a collaborative environment where #datateams can perform real-time #dataprocessing, handle #ETL (Extract, Transform, Load) operations, and train #machinelearning models—all within the same platform. Databricks' architecture is designed to take advantage of the capabilities of major #cloudproviders like #AmazonWebServices, #MicrosoftAzure, and #GoogleCloudPlatform. Each cloud provider offers distinct scalability options, performance optimizations, and easy integration with their proprietary services. The question is, which cloud platform is perfect for deploying Databricks—#AWS, #Azure or #GCP? In this article, we will deep dive into: ?? Architecture and feature differences between Databricks on AWS, Azure, and GCP. ?? Performance optimizations and platform-specific integrations. ?? Pricing comparisons to help you choose the most affordable option. ... and more! Check out the article below to explore in detail how Databricks performs across AWS, Azure, and GCP! Dive right in! https://lnkd.in/g-hBsJj4

We’re living in a time where #data is our most valuable resource and biggest opportunity. Everyone is on a quest to gather as much data as possible and monetize these #dataassets. But the real challenge lies in scaling, securing, and making it accessible. This is where #Snowflake steps in—a platform designed to tackle these challenges head-on with its powerful, cloud-based architecture. Snowflake can handle everything from storing vast amounts of data to analyzing it in detail. Taking it a step further, Snowflake has introduced the #SnowflakeMarketplace, which changes the way data is shared and monetized. You can easily publish ??, discover ??, and consume ??? live, ready-to-query #datasets, #nativeapps, and #AImodels. All of this happens securely, with controlled access to the data. No more tedious manual downloads or complex #ETL pipelines—just seamless, governed access to the #dataproducts you need, when you need them. In this article, we’ll cover three things: ?? How to become a provider on Snowflake Marketplace. ?? Step-by-step process of browsing and accessing data products. ?? How Snowflake Marketplace makes data sharing and monetization easier at scale. Check out the article below to learn more. Dive in! https://lnkd.in/gdCcxgrz

Fed up with writing super complicated #code just to build simple #dataapplications? #Streamlit is the perfect solution. You can easily create interactive #data apps with just a few lines of #Python code. No fuss, no muss—it's super fast and painless. Streamlit is an open-source #Pythonframework designed for #datascientists and #AI / #ML engineers to build interactive #dataapps. What's wonderful is that it provides users with a simple #nocode / #lowcode interface, so they don't have to be #frontend development gurus to use it. Back in March 2022, #Snowflake made a big move—they acquired Streamlit. Now, Streamlit is fully integrated into the Snowflake platform. This means that users can simply leverage Snowflake's powerful #dataprocessing and #storage to create dynamic interactive data apps—all within Snowflake. In this article, we'll walk you through all you need to know about Streamlit and show you how to build and deploy interactive data apps in Snowflake using Streamlit. Check out the article below and start building right away. https://lnkd.in/gAkjeAJD

It's been an all-out #AI explosion over the past couple years—every week or so, a new AI-powered assistant or #LLM hits the market, each promising to outperform the previous one. #Databricks has also made significant moves in this arena, rolling out a bunch of AI-powered solutions. Last year, they dropped a gem called #DatabricksAssistant, which is quite strong and useful. So, what is Databricks Assistant? Think of it as a #ChatGPT-like #chat interface built right into your Databricks workspace. You can chat with it using plain English to perform complex tasks, such as: ?? Generate #SQL and #Python code ?? Debug and fix #code issues ???Autocomplete code ... and a whole lot more features! Check out the article below to learn all about Databricks Assistant and get a detailed, step-by-step guide on how to use it. https://lnkd.in/ghjMmcfN

Having trouble figuring out your #DatabricksCosts ? #Databricks follows a pay-as-you-go pricing model, where you're only charged for what you use—#compute, #storage, and additional features. To make sense of these costs, using a #pricingcalculator is a must. We've compiled the top 5 #DatabricksPricingCalculator tools to help you find the one that works best for you. These calculators provide precise cost estimates tailored to various configurations, workload types, and resource requirements, so you can plan your spending and allocate resources accordingly. Each tool comes with its own set of features that help you manage and optimize your costs in a more effective way. Check out the article below to learn more about these tools and how they can help you slash your Databricks spending. Dive in! ?? https://lnkd.in/gCbq5mWc

#ApacheSpark continues to dominate the world of #bigdata processing and #analytics —and the demand for skilled #Sparkprofessionals is only growing! Thinking about getting certified, or looking for top-quality learning resources? ?? We've compiled some excellent resources just for you. Check out our comprehensive guide to find the right #ApacheSparkCertification and learning resources/programs—featuring both free and paid options—to take your #Spark skills to a whole new level. Dive right in! https://lnkd.in/g32ztHQz

#ApacheSpark ? is well-known for its speed in handling and processing large-scale #data workloads. Meanwhile, #Kubernetes ??—a powerful #containerorchestration platform—makes it easier to deploy, scale, and manage #containerizedapplications. Combine the two, and you get the best of both worlds—optimized #resourceutilization, #scalability, and streamlined operations. In this article, we'll explore how to run Spark on Kubernetes. Here's what we'll cover: ?? Why using Kubernetes with Apache Spark makes sense ?? An in-depth look at the underlying architecture that powers it all ?? A detailed, step-by-step guide to getting Apache Spark up and running on Kubernetes So, if you’re looking to improve performance and scalability in your #dataprocessing workflows, this is worth a read. Below, you'll find everything you need to know about running Apache Spark on Kubernetes. Dive right in! https://lnkd.in/gacCXAmM

#ApacheSpark has really simplified the way we work with #bigdata —making massive #datasets much easier to process. But the more complex your #Spark applications get, the more problematic they can become. You may run into slow execution times, #resourcecontention, job failures,?and a whole lot of other issues. The good news is that sometimes it just takes a few small tweaks to significantly improve your #SparkPerformance. In this article, we will cover 7 essential techniques to help you address these tough issues and get the most out of Apache Spark. Check out the article below to explore these #PerformanceTuning techniques and take your #Sparkworkloads to a whole new level! Dive in! https://lnkd.in/gb-4MevY