562 - 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.

562 - 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.
✍️ New blog post by xbill
Four Iceberg Tools, Three Agent Frameworks: What Ports, and What Doesn't
180 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
30 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.
SerpApi’s Markdown output shows how changing API response format can cut LLM token costs without changing prompts. #dataengineering
🔹 What You’ll Learn
💡 Snowflake architecture and core concepts
☁️ Modern cloud data warehousing
🗄️ Databases, schemas, tables, and data management
📥 Data loading and ingestion techniques
🔄 Data transformation and processing
📞 For Details: +91 7032290546
Wondering what actually gets you hired as a junior data engineer? 👀
We broke down the 3 core skills employers are looking for.
Find out what they are: https://loom.ly/cfAiETk
#TechCareers #LearnToCode #DataEngineering
Thinking about a career in Azure Data Engineering? This could be the right place to start.
Register Now: t.ly/AzureDataEng...
Trainer: Mr Pawan
(Experience: 15+ Years in IT)
Starts: 21st September 2026
Time: 6:00 PM IST
#AzureDataEngineer #DataEngineering #Azure #Databricks
The real #AIagent problem? Messy enterprise semantics.
If Marketing and Finance can’t agree on “active customer,” how can an AI agent?
Models aren’t the problem. Conflicting definitions are!
🔗 Watch Fabiane Nardon’s full talk: buff.ly/TAbVpH4
Reliable systems depend on reliable data.
More than 56% of data professionals cite poor data quality as a significant challenge.
Start with trusted data.
#DataQuality #DataEngineering #DataReliability #Observability #DinaBridge
Full Post: www.instagram.com/p/DdRaJMXk3R...
.
.
#SQL #interview #DataScience #DataAnalytics #ArtificialIntelligence #data #BusinessAnalyst #DataAnalyst #Database #DataEngineering #Tech #RajeshUmrao
@rajeshdataai.bsky.social
Want to pass the Databricks Data Engineer Certification in 2026?
Read the guide:
www.myexamcloud.com/blog/how-to-...
#Databricks #DataEngineering #DataEngineer #PySpark #DeltaLake #MyExamCloud
CS-NRRM™ is a non-medical longitudinal data framework. The goal is to check whether an independent reviewer can reproduce the recorded result—not to perform clinical validation or medical interpretation.
Full Post: www.instagram.com/p/DdO1VCfk3T...
.
.
#SQL #interview #DataScience #DataAnalytics #ArtificialIntelligence #data #BusinessAnalyst #DataAnalyst #Database #DataEngineering #Tech #RajeshUmrao
@rajeshdataai.bsky.social
Hi friends
I’ve added a new video in — “LeetCode SQL50” series
Checkout: youtu.be/TeugNdGQEGs
#SQL #interview #DataScience #DataAnalytics #ArtificialIntelligence #data #BusinessAnalyst #DataAnalyst #Database #DataEngineering #Tech #RajeshUmrao
@rajeshdataai.bsky.social
Stop wrestling with CSV bloat. New script converts CSVs to compressed Parquet with PyArrow, squeezing storage and speeding up your pipelines. Intermediate level, 180 lines. Try it: https://www.valtersit.com/python/parquet-file-writer-with-pyarrow/ #Python #DataEngineering
What is Azure Data Factory?
Answering this with "It’s Microsoft’s ETL tool" is the fastest way to get ghosted by an interviewer.
ADF is an orchestration powerhouse. 🧵👇
#DataEngineering #Azure #TechInterviews
Full Post: www.instagram.com/p/DdMQk7WE92...
.
.
#SQL #interview #DataScience #DataAnalytics #ArtificialIntelligence #data #BusinessAnalyst #DataAnalyst #Database #DataEngineering #Tech #RajeshUmrao
@rajeshdataai.bsky.social
Classify and drop non-events early and the expensive part of an AI pipeline never sees them. Some errors still slip through, which is why a human reviews the flagged ones.
The newsletter's focus on data quality and lineage suggests that the next bottleneck won't be storage or compute, but trust. When pipelines break silently, every downstream decision inherits that risk. Invest in observability before you need it. #DataEngineering