Kumar Abhijeet’s Post

👍 Real-time analytics + support for ETL, OLAP workloads, ML, & geospatial analytics. .....or..... 👎 Real-time analytics with higher $$ and compute load-limitations. The data doesn't lie -- read the Ocient vs. Druid benchmark and learn more:

Maximize HDF performance & scalability for GPS data processing and querying: Data Partitioning 🗄️ Indexing Strategies 🔍 Distributed Computing 🌐 Caching Mechanisms 🚀 Query Optimization 📈 Efficiently handle GPS data with these optimization techniques! 🛰️💻 #HDF #DataProcessing #Scalability #Querying #BigData #Optimization

🔍 Apache Iceberg is all-powerful, but sometimes performance hiccups happen. Before you blame those complex queries, consider that slow metadata retrieval might be the real problem. Identify the impacts and root causes of slow Iceberg metadata retrieval, and take action with insights from our latest article to boost your query performance. https://hubs.la/Q02R8S-v0 #DataAnalytics #DataEngineering #DataLakeAnalytics #DataLake #DataLakeHouse

🔍 Apache Iceberg is all-powerful, but sometimes performance hiccups happen. Before you blame those complex queries, consider that slow metadata retrieval might be the real problem. Identify the impacts and root causes of slow Iceberg metadata retrieval, and take action with insights from our latest article to boost your query performance. https://hubs.la/Q02TKmQm0 #DataAnalytics #DataEngineering #DataLakeAnalytics #DataLake #DataLakeHouse

🔍 Apache Iceberg is all-powerful, but sometimes performance hiccups happen. Before you blame those complex queries, consider that slow metadata retrieval might be the real problem. Identify the impacts and root causes of slow Iceberg metadata retrieval, and take action with insights from our latest article to boost your query performance. https://hubs.la/Q02MjgBK0 #DataAnalytics #DataEngineering #DataLakeAnalytics #DataLake #DataLakeHouse

Just released: Part 2 of our Inpher XOR DataFrame API blog series! In this installment, we explore how cross-request caching streamlines computations, saving valuable time and resources. Read the blog article: https://hubs.li/Q02rGbQV0 #XORDataFrame #DataPrivacy #DataSecurity #DataFrame #MachineLearning #DataScience #TechBlog

> Patches are segmented dynamically based on the entropy of the next byte, allocating more compute and model capacity where increased data complexity demands it https://lnkd.in/drZ6APD4

Read Is sequential IO dead in the era of the NVMe drive? by Jack Vanlightly. Clears a lot of aspects with respect to sequential IO and flash and how sequential IO is much needed now/era of flash drives. https://lnkd.in/gdj54Qib

https://zenoh.io/ The Eclipse Zenoh: Zero Overhead Pub/sub, Store/Query and Compute. Zenoh (pronounce /zeno/) unifies data in motion, data at rest and computations. It carefully blends traditional pub/sub with geo-distributed storages, queries and computations, while retaining a level of time and space efficiency that is well beyond any of the mainstream stacks.

Just in! 🔔 We've launched new Kinsta API endpoints that give you access to in-depth application analytics. Now, you can programmatically retrieve data on bandwidth, build time, runtime, CPU usage, and so many more! 😎 Dive deeper here ↓ https://ow.ly/Xxl850RvcOW