openhousedb’s Post

Watch CEO Ori Rafael on the Partially Redacted podcast with Sean Falconer! 💫 LOTs of astute insights on the data lakehouse architecture! 🎉 #dataengineering #dataarchitecture #lakehouse

Amazing article published by Dave Eyler on how SingleStore’s bidirectional integration to Apache Iceberg can help you get the most of the data stored into your data lakehouse to power your mission critical applications and still guaranteeing sub-second SLA. https://lnkd.in/e9XWJwya

Day 24 #100DaysOfCode learned about stack and queue data structure A stack is a linear data structure that follows the last-in-first-out principle. This means the last item(element) to be added will be the first to be removed from the stack. It has two primary operations:- - Push(add to the top of the stack) -Pop(remove from the top of the stack) While a QUEUE is a data structure(linear) that follows the first-in-first-out principle. This means the first item that is added to the structure is also the first one to be removed. The two primary operations in Queue includes - Enqueue( Adds an element to the end of the queue) - Dequeue( Removes the element at the front of the queue) #buildinginpublic

🚀 Unlock Superior Performance and Cost Savings with Dremio's Reflections! 🚀 Ever wonder how you can avoid pushdowns to multiple systems every time you query a view that combines multiple data sources? Dremio's Reflections feature is here to revolutionize your data querying experience! Reflecting a view that joins multiple data sources can drastically improve query performance and achieve significant cost savings. Instead of repeatedly pushing down queries to various systems, Dremio creates a reflection that stores the result of the join. This means faster query responses and reduced load on your systems. But that’s not all! Any views that are derived from this join can also leverage the reflection to boost query performance, maximizing the value of your data architecture. Even more impressive is how Dremio’s reflections use Apache Iceberg to bring the best of both worlds—indexing and materialization—into a single, powerful acceleration feature. By utilizing Apache Iceberg tables for materialization, you gain the additional benefit of Iceberg’s robust metadata, which acts as an index, further speeding up query processing on the materialized data. While Dremio already has industry-leading out-of-the-box price/performance, Reflections is a tool in the Dremio toolbox that pushes that value even further. Are you interested in learning more about how Dremio’s reflections can transform your data strategy? Check out the comments for more resources on this game-changing feature! #DataAnalytics #DataEngineering #Dremio #ApacheIceberg #DataLakehouse #BigData #CostSavings #PerformanceOptimization #Materialization #Indexing #DataReflections

💾 Embracing the BYOS Revolution with Apache Iceberg 🚀 Apache Iceberg is redefining the landscape of data engineering, introducing a "Bring Your Own Storage" (BYOS) philosophy that is set to revolutionize how organizations manage and interact with their data at scale. Authored by Hugo Lu in the Orchestra’s Data Release Pipeline Blog, this article explores why Apache Iceberg is pivotal in the new era of data handling. 🔍 Innovative Data Management: Apache Iceberg allows enterprises to leverage their existing storage systems while adopting sophisticated data architecture capabilities. This approach not only enhances flexibility but also reduces costs and complexities associated with data management. 🛠️ Key Features of Apache Iceberg: 🔹 Schema Evolution: Manage and evolve your data schema without affecting your existing data, ensuring seamless transitions and updates. 🔹 Hidden Partitioning: Simplifies data handling by abstracting complex partitioning into the background, enhancing performance without manual intervention. 🔹 Snapshot Isolation: Provides robust data integrity, ensuring that data remains consistent across reads, even in highly concurrent environments. 🔹 Full Compatibility: Works effortlessly with a variety of data processing engines like Apache Spark, Trino, and Flink, ensuring flexibility in data processing workflows. 🔹 Incremental Updates: Supports atomic operations for adding, removing, or merging data, which minimizes data corruption risks and simplifies version control. 🔹 Rollbacks: Offers the ability to revert to previous states of data, providing safety nets for data recovery strategies. 🔹 Efficient Storage Utilization: Enhances storage efficiency by optimizing data layout, reducing overhead, and improving query performance. 🌐 https://lnkd.in/dGEGbp98 Let's discuss: 🔹 How could the BYOS approach of Apache Iceberg transform your data architecture strategies? 🔹 Are there particular challenges in your organization that this technology could address? #ApacheIceberg #DataEngineering #BigData #TechInnovation #CloudStorage #DataManagement #BYOS #TechnologyTrends #FutureOfData #DigitalTransformation

Imagine if your #data & #Application teams could take #operational data in JSON documents and flatten it into a #columnar format for #analytics - without ETL, from multiple sources and in near-real time. Would that help deliver valuable insight to the business faster? Then imagine if that insight was fed back into an adaptive applications that used that data to change the offer to your customer - automatically?

Ready to up your data game? We dropped a new video on YouTube that's a game-changer for decision-makers and data enthusiasts alike! 🚀💼 Whats Inside: Airbyte concepts: A comprehensive understanding of this data integration solution. ETL vs. ELT: Get the lowdown on these data buzzwords. Live Demo: Watch us connect PostgreSQL, MongoDB, and Redshift, live! 🔗 Watch Now: https://lnkd.in/gsRjJVgT Ready to geek out with us? 🤓 Hit play, drop us a like, share the knowledge, and subscribe for more tech goodness! 🤝 Let's make data exciting together! #databasemanagement #Airbyte #TechInnovation #redshift #mongodb #postgresql #aptuz #dataanalysis #ETL #ELT #datatransformation

Ever wonder why you're moving data from lakes to warehouses for quicker queries? 🤔 It's a common fix but comes with its own set of challenges. Our article explores how data lakes have transformed to provide data warehouse-level performance, enabling in-place analysis and eliminating the need for expensive data transfers and complex governance. Check out our insights: https://hubs.la/Q02q-FQ_0 Keen to learn more or have questions? Join our live webinar tomorrow on how to achieve better lakehouse performance with Apache Iceberg and StarRocks: https://hubs.la/Q02q-zb80 #DataAnalytics #DataEngineering #DataLakeAnalytics #DataLake #DataLakeHouse

Searching through one million records is very fast thanks to Elasticsearch and asynchronous data mutation operations https://lnkd.in/dUhCjV3T #Dotnet #Elasticsearch #Microservices

We've heard so much about different stores, different interfaces, different ways to use data. And usually one "just" has to connect them, right? Turns out shuffling data around like that can be a complicated effort all on its own! We recently rebranded EDB around our belief that Postgres is the right lens to present a contiguous experience when it comes to the questions you have for your data. #PostgresData #DataArchitecture