Aleksandra Alekseienko’s Post

Data migration is a crucial process in the realm of e-discovery platforms. Smooth transitions with minimal disruptions and preservation of data fidelity depend on getting all of them right.

Data migration is a crucial process in the realm of e-discovery platforms. Smooth transitions with minimal disruptions and preservation of data fidelity depend on getting all of them right.

Data migration is a crucial process in the realm of #ediscovery platforms. Smooth transitions with minimal disruptions and preservation of data fidelity depend on getting all of them right.

Data migration is a crucial process in the realm of #ediscovery platforms. Learn from Sara Anwar of Elevate on the best approaches to data migration, leveraging industry best practices and advanced tools to streamline the migration process.

The centralization of data has been a prevalent trend for many years. From large corporations to small businesses, data is collected, processed, and stored in central databases. However, with the rise of data privacy regulations across the world, there is a growing interest in decentralized data processing. This blog is the second part of the blog series on Regulation-Compliant Federated Data Processing. Learn more below..

JUXT's XTDB is an interesting example of a bi-temporal database, where you can literally wind back the clock and check its state at a particular point in time and compare it to what it is currently. Among it's applications is compliance checking --

🚨 On-call for data teams and 3 design patterns to avoid it Data teams are powering more and more business-critical, customer-facing products. This is fantastic because it's a signal of creating real value, but it also means data teams often end up having to introduce on-call responsibilities. While sometimes on-call is unavoidable, I’ve come across several smart ways to build data applications that can significantly reduce the need for it. Here are 3 design patterns for data products that help avoid on-call: 💡 Batch Processing + Real-time Serving Even if predictions need to be served in real-time, they don’t always have to be generated in real-time. Data can be processed in batches and the results exposed via a low-latency database like Postgres. Your front-end apps can serve predictions in real-time from there. If your pipeline breaks, you’ve got time to fix it without disrupting users. ⚙️ Fallback Defaults for Real-time Use Cases For real-time applications, you can design front-end systems to handle failures gracefully. If personalized predictions fail, you could default to globally popular recommendations. Or you can cache previously processed results and serve slightly older data if the pipeline goes down. This ensures a seamless experience for users and reduces the pressure on your team to rush a fix. 👩🏻💻 Manual Workarounds for Critical Processes For time-sensitive workflows, having manual fallback processes can prevent critical failures. At iZettle, the risk team had backup queries for identifying fraudulent cases if the ML system was down, which was a simple but effective way to maintain operations during downtime. These are patterns I’ve come across, but I’d love to hear from others. What design strategies have you seen that help reduce the need for on-call for data teams?

Decentralized data processing refers to the distribution of data processing tasks across a network of nodes, rather than relying on a single centralized system.

Immuta launches Domains policy enforcement to improve security and governance for data owners: Immuta launched Domains policy enforcement, a new capability in the Immuta Data Security Platform that provides additional controls for data owners to implement a data mesh architecture with domain-specific data access policies. Centralizing data access decisions across organizations often leads to bottlenecks, preventing timely policy authoring, editing, and access to data. With Domains, data owners define data controls with both broad reach and specific domain controls. This is done by mirroring structures such as business … More → The post Immuta launches Domains policy enforcement to improve security and governance for data owners appeared first on Help Net Security.

Dear LinkedIn contacts ! Woody Walton just posted an interesting blog on how Elastic can be used as global Data Mesh. Elastic does not only bring scalability and performance for your analytics, but also advanced search and a vector store that previous centralised data solutions did not offer. Please have a look at this blog, and post here your comments and feedback : I am very curious if this architecture and approach resonates !