How to publish and manage maps for utility networks

Have you started comparing Sentinel in data lake (now GA) to what it has cost in the past? Check out Marko Lauren's write up to see what the impact would be and see if that could be a good fit (or the piece that was missing in a move over to Sentinel).

𝐈𝐬 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐆𝐞𝐭𝐭𝐢𝐧𝐠 𝐒𝐥𝐨𝐰? 𝐈𝐭 𝐌𝐢𝐠𝐡𝐭 𝐍𝐞𝐞𝐝 𝐚 𝐓𝐮𝐧𝐞-𝐔𝐩! ✌🏻 If your application's query performance is dipping, the culprit could be 𝐟𝐫𝐚𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐢𝐧𝐝𝐞𝐱𝐞𝐬. What is fragmentation? Think of it like taking notes on a document, but scattering the pages all over the room. The computer has to run around to find them! The simple solution is "𝐈𝐧𝐝𝐞𝐱 𝐑𝐞𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠." This is a maintenance task that gathers those scattered pages (data pointers) and puts them back in order, making data retrieval snappy again. This is a key best practice for maintaining robust, high-performance applications. #DatabaseAdministration #TechTips #Performance #Tuning #optimization

We get a lot of requests for really valuable features that are missing in the Axis cameras, or features that a specific company wish worked in a different way. Luckily, that's exactly why Axis created the AXIS Camera Application Platform (ACAP) that let's you install third-party applications directly in the cameras. We at FixedIT have created a lot of such applications for customers. Nowadays, we also offer our FixedIT Data Agent which allows programmers or IT professionals to implement their own edge applications in a very simple way. We have a GitHub repository with open-source projects that are implemented with the FixedIT Data Agent (see comment for link). We're constantly adding new projects to this repo. This means that you can buy a license for the FixedIT Data Agent, install it in your camera and load the config files from our open-source repo. If you want to modify it or further extend it, doing so is easy! One of the requests that we got recently was adding a button to the live view which will save a snapshot to the SD card. Currently, snapshots are downloaded to the operators computer, something that is not optimal for data organization, privacy perspectives or bandwidth constraints. This use case is one example of a scenario that could be implemented with the FixedIT Data Agent. You just need to upload a new config with an input connected to the on-screen button, add a tiny script to capture the image and save it to the SD-card. In an upcoming release of the FixedIT Data Agent, we will add functionality that makes it easier to inspect and download content from the SD-card. What if you want to snapshot to be saved to AWS S3 instead? Just swap the output for the remote file output. You can find an example of this in our timelapse project in the same open-source repo. Interested in this project? If the interest is large enough, this project might end up in our open-source repository in a near future!

Your data infrastructure is costing you millions. Is it even worth it? Here's 3 common data infrastructure mistakes I see too often: 1. View on view on view You're stacking temp views to power a dashboard that never changes. The solution? Build a permanent table to save on data processing costs. 2. Real or near real time data when it isn't actually worth the ROI You wanted real-time data. But it costs more than the insights are worth. The solution? Batch your data. 3. Your data model doesn't make sense Your data model isn't set up properly. It's unnecessarily complicated, and draining your money every day. The solution? Get a data infra audit. What's the most expensive data infra mistake you've seen? Comment down below.

The New Economics of Open Source Data Infrastructure. Tiered storage, vector search and real-time analytics optimize for performance, cost and flexibility simultaneously rather than as competing concerns.

dependencies { implementation 'com.google.firebase:firebase-auth-ktx:22.2.0' implementation 'com.google.firebase:firebase-firestore-ktx:24.9.0' implementation 'com.google.firebase:firebase-storage-ktx:21.2.2' implementation 'com.github.bumptech.glide:glide:4.15.1' implementation 'androidx.recyclerview:recyclerview:1.3.1' implementation 'androidx.cardview:cardview:1.0.0' } apply plugin: 'com.google.gms.google-services' object FirebaseUtils { val auth = FirebaseAuth.getInstance() val firestore = FirebaseFirestore.getInstance() val storage = FirebaseStorage.getInstance() } data class Book( val id: String = "", val title: String = "", val author: String = "", val description: String = "", val imageUrl: String = "", val ownerId: String = "" ) data class Message( val senderId: String = "", val text: String = "", val timestamp: Long = System.currentTimeMillis() )

The environments in which we manage data are not truly modern. Large chunks of the past remain embedded in the present, and many primitive forms survive. It is not just hardware and software, but also ways of thinking about data. And all new activities are really only building the legacy systems of the future. This is why it is so important to learn how we got here, which I try to do at a high level in my latest Substack post on How Data Became Everything. https://lnkd.in/grwsiR5N #data #metadata #datagovernance #dataproduct

Our dashboard was crawling. Every page load triggered 200+ database queries. I discovered something that changed everything in 3 hours. The problem wasn't complex queries or server capacity. It was the classic N+1 query trap hiding in our user relationship loading. Here's what I implemented: **Context:** Each user card displayed profile data plus their latest 5 posts. Simple enough, right? **Method:** Instead of lazy loading posts individually, I switched to eager loading with a single optimized query that fetches all user data and their posts in one database hit. **Example:** Before: 1 query for users + 200 queries for individual posts. After: 2 queries total using JOIN statements. **Result:** Page load dropped from 4.2 seconds to 0.8 seconds. Database load decreased by 89%. The real lesson? Performance optimization often comes from questioning your data loading patterns, not just throwing more hardware at the problem. Six months later, our users still comment on how fast the platform feels. What's the biggest performance win you've discovered by changing your approach rather than your infrastructure? #APIDesign #TechOptimization #SoftwareDevelopment #WebPerformance #DatabaseOptimization #TechInnovation #Rankue #Vonyex

💲Costs of Sentinel data lake - now generally available Summary of changes: ✅ Data lake only ingestion costs total $0.15/GB (ingestion + processing) ✅ Compression enabled, 6:1 across all data sources (6GB ingest -> 1GB in storage - you pay only for stored GB!!) ✅ Data lake replaces old "archive" for long-term retention, 6:1 compression is used there too! 💡 Pricing: https://lnkd.in/dUFn5Ctq ✅ New in-portal cost management features (usage reports, cost control/notifications, NOTE: you must be billing admin): https://lnkd.in/dTqFfD2W ✅ Asset data (billable) in default workspace is populated automatically by connectors. Solutions in Content Hub (Entra Asset, M365 Asset, ARG): https://lnkd.in/dkDzT8Bv ✅ Not a change but relevant: all queries to data lake are billable (also when querying the "mirrored" data in Analytics tier). Also MCP Server queries as they use data lake. #datalake

Migrating data from an older system can feel daunting, but it doesn’t have to be. In our latest article, we outline what to expect during a data migration, common hurdles to watch for, and how working with an experienced partner like webCemeteries makes the process smooth and successful. https://hubs.li/Q03M_Xx70 #CemeterySoftware #CemeteryManagement #Data