@junaidwaseem474.bsky.socialManaging thousands of spatial data points per second requires specific scalable infrastructure. Here is a technical breakdown of an open-source GIS architecture designed to ingest and process real-time IoT sensor data. Click here to read the full guide.
@junaidwaseem474.bsky.socialStruggling with slow spatial queries? Learn advanced indexing strategies, clustering techniques, and configuration tweaks to maximize the performance of your open-source spatial database architectures. Read the database performance guide here.
@techinlayers.bsky.socialWarehouse: ACID + fast BI, proprietary storage.
Lake: cheap and open, no transactions.
Lakehouse: both, on one copy of the data.
Drawn step by step: youtu.be/-c09yVpUtAs
Full series: www.youtube.com/watch?v=B5iH...
@beyondroot.bsky.socialNeed to rotate encryption keys across terabytes of data? With envelope encryption you only rotate the master key. The data itself never gets re-encrypted. Saves enormous time. #DataEngineering
@nareshit.bsky.socialWant to understand how Microsoft Fabric is changing modern data engineering?
Register Now: t.ly/FWSMSFABRIC-...
Date: 20th September 2026
Time: 10:30 AM (IST)
Trainer: Mr Pawan
FREE Online Workshop
Zoom: zoom.us/j/89894404580
#FreeWorkshop #MicrosoftFabric #DataEngineering #AzureDataEngineering
@techinlayers.bsky.socialDatabricks in one picture: the control plane is hosted, clusters run in the compute plane, and your data stays in your own storage as Delta tables. Only results and metadata go back.
Drawn step by step: youtu.be/B5iHmoYgnqY
@techinlayers.bsky.socialDatabricks in one picture: the control plane is hosted, clusters run in the compute plane, and your data stays in your own storage as Delta tables. Only results and metadata go back.
Drawn step by step: youtu.be/B5iHmoYgnqY
@rezarad.bsky.socialOneLake Security Deep Dive | Fabric Insider Ep. 20 | Aaron Merrill 🔐
#MicrosoftFabric #OneLake #RowLevelSecurity #DataSecurity #DataGovernance #FabricInsider #AIAgents #Snowflake #Databricks #MicrosoftMVP #BusinessIntelligence #DataEngineering #FabricUpdates #PowerBI #RADACAD
@rajeshdataai.bsky.socialFull Post: www.instagram.com/p/DdZIdGvk40...
.
.
#SQL #interview #DataScience #DataAnalytics #ArtificialIntelligence #data #BusinessAnalyst #DataAnalyst #Database #DataEngineering #Tech #RajeshUmrao
@rajeshdataai.bsky.social
@businessam.bsky.socialAdidas ontslaat tot 350 IT-medewerkers in zijn Indiase vestiging #Adidas #bedrijfshervorming #ITwerkgelegenheid #dataengineering #softwareontwikkeling
@postgres-daily.bsky.socialManaging AI data complexity while maintaining tight security is a huge challenge.
Designing PostgreSQL architectures that handle both core transactional data and AI workloads bridges the gap. CYBERTEC expertise helps guide this process.
@datainfometrix.bsky.socialSnowflake tells you where the data lives. Now find the people and technology shaping it.
Reach Snowflake accounts across data leadership, engineering, architecture, and analytics with deeper installed-base intelligence.
datainfometrix.com/data-analyti...
#Snowflake #DataEngineering #DataAnalytics
@pgexperts.bsky.socialClickHouse shipped chdb, a Postgres extension for object-storage import and export.
@rajeshdataai.bsky.socialFull Post: www.instagram.com/p/DdWjsU9Ey9...
.
.
#SQL #interview #DataScience #DataAnalytics #ArtificialIntelligence #data #BusinessAnalyst #DataAnalyst #Database #DataEngineering #Tech #RajeshUmrao
@rajeshdataai.bsky.social
@rajeshdataai.bsky.socialFull Post: www.instagram.com/p/DdT--CwE4B...
.
.
#SQL #interview #DataScience #DataAnalytics #ArtificialIntelligence #data #BusinessAnalyst #DataAnalyst #Database #DataEngineering #Tech #RajeshUmrao
@rajeshdataai.bsky.social
@powerbitips.bsky.social562 - AI can write 10,000 lines of net new code in two minutes. Great. Now prove it's correct. The bottleneck moved somewhere new, and most teams haven't solved it yet.
@awscmblogposts.bsky.social✍️ New blog post by xbill
Four Iceberg Tools, Three Agent Frameworks: What Ports, and What Doesn't
@hugovalters.bsky.social180 lines of Python using great-expectations to validate datasets against YAML rule sets and output reports. For data engineers who want checks in CI without building a https://www.valtersit.com/python/data-quality-rules-engine/ #python #dataengineering #greatexpectations
@pgexperts.bsky.social30 agent-written schemas, 838 indexes audited. Only ten served no purpose. The indexes are fine.
The bill is cumulative: one tickets table got 16 indexes, six on a column updated on every touch. 1.8x the WAL, 1.9x the update time.
