Juicedata的动态

You can (and should) start your DataOps / Data Quality Improvements without buying any software 4 Easy Ways to Start DataOps Today https://bit.ly/3KxCI16 #dataops

You can (and should) start your DataOps / Data Quality Improvements without buying any software 4 Easy Ways to Start DataOps Today https://bit.ly/3Kx9GPd #dataops

You can (and should) start your DataOps / Data Quality Improvements without buying any software 4 Easy Ways to Start DataOps Today https://bit.ly/4bQZfSO #dataops

?? Day 2??4?? of our annual Content Advent Calendar ????? Today, we’re exploring ways to resolve the problem of destructive data operations ?? Click "... more" to unveil ? ?? In the fast-paced world of software development, the way we think about data management is evolving. Traditional CRUD operations have been the cornerstone of database interactions for decades, but they often fall short in capturing the full complexity of modern systems. In this blog post, Jason Bloomberg – Managing Director at Intellyx – dives into the limitations of CRUD, and highlights how bitemporality can transform the way we manage data. By embracing bitemporal data models, systems can achieve better resilience, auditability, and a more comprehensive understanding of "what happened when" – a critical capability for today’s dynamic, data-driven environments. ?? Read the full post ?? https://lnkd.in/gQ6QXa-u ? #Data #DataManagement #SoftwareEngineering #BitemporalData #Databases #DatabaseDesign #opensource #bitemporality #CountdownToChristmas ? #JUXTContentAdventCalendar ?? #UnwrapKnowledge ??

While no-code might sound tempting for quick setup, there are big trade-offs you can't ignore when it comes to maintainability, flexibility, and performance. Here's 10 reasons why, as well as why having control over your code is needed for reliability and scalability in your data systems. https://bit.ly/3Bx3G7T

In situations where deep customization and complex data transformations are not required, the no-code approach is much better and faster.

Really excited to share this article which Zhou (Jane) Xing, Andy Woods, and I wrote which highlights the data movement tool we've been working on -- MOLT Fetch. It's exciting to build tooling that simplifies our customers' migrations over to CockroachDB. In this post we discuss how our data movement story has evolved and how Fetch aims to solve issues we discovered along that process. If you're interested, you can read more here: https://lnkd.in/gCzDKU8G I'm also curious if anyone in my network has experienced challenges with performing data movement and migrations, and what some of the biggest gripes have been?

Enterprises needing specialized software that provides an environment for developing and delivering data as a product should check out #DataOps.live,?says analyst Matt Aslett. Learn more: https://bit.ly/3QEDGMH @DataOpslive #DataIntelligence #SoftwarePlatform?

What are Cache Expiration Policies? & 3 Best Practices ?????? Cache Expiration refers to the automatic removal of cache entries after a specified period, ensuring that outdated or stale data does not remain in the cache. Once the period is over, the item is considered stale and is removed from the Cache. Cache Expiration is set for each Cache Item to be put into the Cache, and one needs to provide one of these Expiration for the item. Generally, there are two types of expirations - Absolute Expiration Sliding Expiration Absolute Expiration ? We set a fixed time after which a cache item is automatically removed, irrespective of whether it has been accessed. Ideal for scenarios where data becomes stale or invalid after a set period, such as temporary session data or data that changes at regular intervals. Sliding Expiration ?? The expiration timer is reset each time the cache item is accessed, removing it only if it hasn't been accessed for a specified period. Ideal when data should remain cached as long as it's actively used, like user session data or frequently accessed but time-sensitive information. 3 Best Practices for Cache Expiration 1. Define expiration times based on the nature of the data - use absolute expiration for data that becomes stale after a specific period (e.g., configuration data), and sliding expiration for data that should remain fresh as long as it's actively used (e.g., session data). 2. Regularly monitor cache performance metrics such as hit/miss rates, memory usage, and eviction rates and adjust expiration times based on observed patterns to balance memory usage and data freshnes. 3. For critical data, ensure that expiration times don’t overlap with high-demand usage patterns. Use sliding expiration for frequently accessed data to extend its life as long as it's needed, and set reasonable absolute expiration for less critical data to avoid unnecessary recomputation. -------------------- Hello there ??, I'm Sriram Kumar Mannava I'm a full-stack developer, and I can help you jumpstart into software engineering by sharing various useful concepts in a simple way, based on my experience ???? If you're interested, please do follow me and stay notified! #CacheManagement #DataCaching #CacheExpiration #PerformanceOptimization #DataFreshness #SoftwareDevelopment #BackendDevelopment #CachingStrategies #ApplicationPerformance #DataManagement