Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
In our previous posts, we showed how open table formats, open APIs and unified governance are coming together to complete the Open Lakehouse vision. We also introduced cross-engine attribute-based access control, which allows policies defined in Unity Catalog to be enforced consistently when external engines access governed data. Now, that vision is beginning to materialize in the open.
In the dynamic world of cloud computing, data engineers are constantly challenged with managing and analyzing vast amounts of data. A critical aspect of this challenge is effectively handling AWS Load Balancer Logs. This article examines the integration of AWS Load Balancer Logs with ClickHouse for efficient log analysis. We start by exploring AWS’s method of storing these logs in S3 and its queuing system for data management.
Data is widely considered the lifeblood of an organization; however, it is worthless—and useless—in its raw form. Help is needed to turn this data into life-giving information. Part of this help is to move this data from its source to destination via data pipelines. But managing the end-to-end data pipeline lifecycle is not easy: ones are challenging to scale on demand, others can result in delays in detecting and addressing issues or be difficult to control.
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
