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Microsoft Fabric Architecture Podcast (हिनदी)
Microsoft Fabric Architecture Podcast (हिनदी) episodes
- पंद्रह मिनट का Blind Spot
पंद्रह मिनट का Blind Spot
Episode 10 • 2026-03-06
Duration: 11:46SQL analytics endpoint हर Lakehouse को instant T-SQL access देता है। लेकिन 15 minutes की ख़ामोशी के बाद metadata sync रुक जाता है, T-SQL से freshness check करने का कोई तरीका नहीं है, और views चुपचाप Power BI को Direct Lake से DirectQuery पर switch कर देती हैं। एक ऐसी read window जो आपकी नज़र हटते ही अंधेरी हो जाती है।
What we discuss
- How it actually works underneath the abstraction
- Where the obvious answer breaks
- A real Reddit/Microsoft Q&A question unpacked
- The pattern we keep seeing in the field
- The concrete recommended architecture
- F-SKU realism — what this actually costs
- When the rejected approach is actually right
- The architectural principle to take home
Key takeaways
- Slide पर simplicity और runtime में simplicity — same बात नहीं है।
- इस वक़्त कहीं न कहीं एक report है जो पिछले महीने 2 seconds में load होती थी और आज 14 seconds लेती है। Data नहीं बदला। Model नहीं बदला। किसी ने एक view add की। और वो capacity को blame कर रहे हैं।
- ऐसा workbench जो 15 minutes की ख़ामोशी के बाद बंद हो जाता है।
Resources
- What is the SQL analytics endpoint for a lakehouse?
- SQL analytics endpoint — Capabilities
- Decision guide: Warehouse and Lakehouse
- Refresh SQL endpoint metadata
- Data types in the SQL analytics endpoint
- Direct Lake overview
- Share a Lakehouse
- Lakehouse overview
- Warehouse overview
- SQL Database in Fabric
- OneLake shortcuts
- Database mirroring
- OneLake security
- Cross-database queries
- Better together: the lakehouse and warehouse
About the show
AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
यह podcast AI से बनाया गया है। दोनों आवाज़ें synthetic हैं: Matthias की आवाज़ cloned है और Fabia एक designed AI co-host है।
Brand design based on fabricperiodictable.com.
12 min - वो SQL Endpoint जो Write Back नहीं कर सकता
Episode 9: Fabric Warehouse vs. Lakehouse: Same Storage, Different Engines (हिन्दी)
Fabric Architecture Podcast — हिन्दी
AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host.
Based on the Fabric Periodic Table.
New episodes every Friday. DM Matthias on LinkedIn.
AI-generated voices. Brand design based on fabricperiodictable.com.
12 min - 'Mirror All Data' बटन — जो actually सब mirror नहीं करता
वो Button जो कहता है 'Mirror All Data' — लेकिन करता नहीं
Episode 8 • 2026-02-20
Duration: 11:16Fabric mirrored databases का वादा है zero-code, near-real-time replication — SQL Server, Cosmos DB, और Snowflake से। Matthias और Fabia समझाते हैं कि CDC असल में कैसे काम करता है, replication free क्यों है लेकिन queries नहीं, और कौन से schema changes आपका mirror बिना बताए तोड़ देते हैं।
What we discuss
- How it actually works underneath the abstraction
- The pattern we keep seeing in the field
- A real Reddit/Microsoft Q&A question unpacked
- Where the obvious answer breaks
- The concrete recommended architecture
- F-SKU realism — what this actually costs
- When the rejected approach is actually right
- Risks of the recommended path
- The architectural principle to take home
Key takeaways
- इस वक़्त कहीं एक Delta table अपने source से तीन दिन पीछे है। और उस पर बना dashboard कह रहा है 'refreshed today।'
- Mirror चलता रहता है जब कोई नहीं देख रहा। यही architecture decision है — कि आपकी team में देखने की आदत है या नहीं।
- Pattern तय करता है platform। अगर आपका pattern एक supported database है जिसमें moderate change volume है, और आप वो data बिना pipeline code लिखे Fabric में queryable चाहते हैं — mirroring सही choice है। अगर आपके pattern में transformations...
Resources
- Mirrored databases overview
- Azure SQL DB mirroring
- Azure SQL DB limitations
- SQL Managed Instance limitations
- SQL Server limitations
- Cosmos DB mirroring
- Cosmos DB limitations
- Snowflake mirroring
- Snowflake limitations
- Troubleshooting
About the show
AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
यह podcast AI से बनाया गया है। दोनों आवाज़ें synthetic हैं: Matthias की आवाज़ cloned है और Fabia एक designed AI co-host है।
Brand design based on fabricperiodictable.com.
12 min - Pipeline Succeeded — लेकिन Data कहाँ गया?
Pipeline Succeeded — Data कहाँ गया?
Episode 7 • 2026-02-13
Duration: 11:49Matthias और Fabia Fabric का सबसे common pipeline failure dissect करते हैं — green checkmark मिला लेकिन Lakehouse table खाली। Control flow success semantics कैसे empty runs छुपाती हैं, validation architecture जो उन्हें पकड़ती है, और pipelines को पूरी तरह skip करने का case।
What we discuss
- How it actually works underneath the abstraction
- Where the obvious answer breaks
- A real Reddit/Microsoft Q&A question unpacked
- The concrete recommended architecture
- F-SKU realism — what this actually costs
- When the rejected approach is actually right
- Risks of the recommended path
- The architectural principle to take home
Key takeaways
- कहीं अभी एक pipeline है जिस पर green checkmark लगा है और दूसरी तरफ़ table खाली है। Friday से किसी ने check नहीं किया।
- Query pattern सब बता देता है। Pipelines के साथ भी यही principle — dependency pattern दिखाइए। अगर steps के बीच कोई dependency नहीं, तो pipeline की ज़रूरत नहीं। और जिस moment आप एक add करते हैं, आप orchestration maintain कर रहे हैं हर उस...
- एक activity, कोई dependency नहीं — direct scheduling। दो activities जहाँ एक को दूसरे का output चाहिए — pipeline। Conditional logic या dynamic data पर iteration — definitely pipeline। Fabric documentation इसे decision tree के रूप में lay...
Resources
- Data Pipelines in Fabric
- Activity Overview
- Control flow activities
- Pipeline Overview
- Copy Activity
- Parameters and Expressions
- Monitor Pipeline Runs
- Decision Guide: Pipeline vs Dataflow vs Spark
- Data Integration Strategy Guide
- Reddit r/MicrosoftFabric
- MS Q&A
- Stack Overflow
About the show
AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
यह podcast AI से बनाया गया है। दोनों आवाज़ें synthetic हैं: Matthias की आवाज़ cloned है और Fabia एक designed AI co-host है।
Brand design based on fabricperiodictable.com.
12 min - रात 3 बजे का Variable
रात 3 बजे का Variable
Episode 6 • 2026-02-06
Duration: 10:40एक scheduled notebook जो बीच-बीच में 'variable not defined' error देती है — यही तो lucky outcome है। unlucky outcome में वो stale state के साथ चलती है, numbers सही लगते हैं, और एक महीने तक कोई check नहीं करता।
What we discuss
- How it actually works underneath the abstraction
- The pattern we keep seeing in the field
- A real Reddit/Microsoft Q&A question unpacked
- Where the obvious answer breaks
- F-SKU realism — what this actually costs
- When the rejected approach is actually right
- The concrete recommended architecture
- Risks of the recommended path
- The architectural principle to take home
Key takeaways
- आज रात कहीं एक scheduled notebook चलेगी जिसमें पिछले Tuesday की session का variable होगा। Job crash नहीं होगी। Data land होगा। और numbers बस इतने करीब होंगे कि शक न हो।
- 8 लोगों की team जो cost pressure में हो, उनके लिए मेरा rule ये होगा: जो भी scheduled है उसे promote कीजिए। Development के लिए notebook, production के लिए SJD, SJD को Pipeline के अंदर। बीस minutes का setup है ये — बदले में एक महीने का वो...
- दरअसल — notebook को directly schedule करना कुछ specific conditions में genuinely सही call है। अगर notebook visualizations produce करती है जो कोई हर सुबह review करता है — charts, rendered markdown, summary tables — तो SJD ये सब हटा देता है।...
Resources
- What is a Spark Job Definition?
- Create SJD
- Run SJD
- SJD Git Integration
- Pipeline SJD Activity
- Streaming Data into Lakehouse
- Spark Best Practices
- Job Queueing
- Decision Guide: Pipeline vs Dataflow vs Spark
- Fabric vs Synapse Spark Comparison
- MS Q&A
- Reddit r/MicrosoftFabric
- Stack Overflow
About the show
AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
यह podcast AI से बनाया गया है। दोनों आवाज़ें synthetic हैं: Matthias की आवाज़ cloned है और Fabia एक designed AI co-host है।
Brand design based on fabricperiodictable.com.
11 min - वो Install Tax जो किसी ने गिना ही नहीं
वो Install Tax जो किसी ने गिना ही नहीं
Episode 5 • 2026-01-30
Duration: 10:14हर team जो Fabric Environments skip करती है, हर scheduled notebook पर, हर session में, हर दिन %pip install चलाती रहती है. जिन twelve minutes की सब शिकायत करते हैं, वो Spark budget का सबसे सस्ता line item निकलता है.
What we discuss
- How it actually works underneath the abstraction
- A real Reddit/Microsoft Q&A question unpacked
- F-SKU realism — what this actually costs
- Where the obvious answer breaks
- When the rejected approach is actually right
- The concrete recommended architecture
- Risks of the recommended path
- The architectural principle to take home
Key takeaways
- इस वक़्त कहीं न कहीं, एक scheduled notebook अपने पहले 90 seconds वही 3 packages install करने में लगा रही है जो उसने कल install किए थे.
- query pattern दिखाइए. अगर exploration है, तो Starter Pools पर रहिए — वो इसके लिए excellent हैं. अगर scheduled है, तो एक बार publish का cost दीजिए और हर run पर install का cost देना बंद कीजिए.
- जिन twelve minutes की सब शिकायत करते हैं, वो आपके Spark budget का सबसे सस्ता line item है.
Resources
- Create and manage environments
- Configure Starter Pools
- Manage libraries
- Environments GA
- Reddit r/MicrosoftFabric
- MS Q&A
- Stack Overflow
About the show
AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
यह podcast AI से बनाया गया है। दोनों आवाज़ें synthetic हैं: Matthias की आवाज़ cloned है और Fabia एक designed AI co-host है।
Brand design based on fabricperiodictable.com.
11 min - वो Lakehouse जो आपने बनाया ही नहीं
वो Lakehouse जो आपने कभी बनाया ही नहीं
Episode 4 • 2026-01-23
Duration: 9:38हर Dataflow Gen2 चुपचाप एक staging Lakehouse बना लेता है — Power Query के familiar interface के पीछे। हम execution architecture समझते हैं, query folding क्यों performance तय करती है, और कब notebook ही सही जवाब है।
What we discuss
- How it actually works underneath the abstraction
- The pattern we keep seeing in the field
- A real Reddit/Microsoft Q&A question unpacked
- Where the obvious answer breaks
- The concrete recommended architecture
- F-SKU realism — what this actually costs
- When the rejected approach is actually right
- The architectural principle to take home
Key takeaways
- और staging Lakehouse ईमानदारी से उन सारे bytes को Parquet में लिख रहा है।
- अभी इस वक़्त कहीं न कहीं एक dataflow पूरी table wire पर खींच रहा है क्योंकि किसी ने step three में Year column add कर दिया। और progress bar 60 percent दिखा रहा है। पिछले एक घंटे से 60 percent दिखा रहा है।
- Dataflow Gen2 से मैं बार-बार यही सीख निकालता हूँ कि visual simplicity असली है — लेकिन ये surface है। नीचे एक staging Lakehouse बैठा है, एक Parquet layer है, और एक query folding mechanism है जो चुपचाप तय कर रहा है कि आपका flow minutes में...
Resources
- What is Dataflow Gen2?
- Dataflow Gen2 architecture
- Connector overview
- Power Query M reference
- Staging settings
- Incremental refresh
- Schedule Dataflow Gen2
- Best practices
- Create your first Dataflow Gen2
- Monitor Dataflows
About the show
AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
यह podcast AI से बनाया गया है। दोनों आवाज़ें synthetic हैं: Matthias की आवाज़ cloned है और Fabia एक designed AI co-host है।
Brand design based on fabricperiodictable.com.
11 min - आपका Notebook रात 2 बजे क्यों टूटता है
आपका Notebook रात 2 बजे क्यों टूटता है
Episode 3 • 2026-01-16
Duration: 10:22Fabric notebooks एक simple coding environment लगते हैं, लेकिन नीचे एक distributed system है जिसमें driver node की boundary के बारे में किसी ने warn नहीं किया। Pipeline से actually क्या बदलता है — और Spark Job Definition में जाना कब सही है।
What we discuss
- How it actually works underneath the abstraction
- The pattern we keep seeing in the field
- Where the obvious answer breaks
- A real Reddit/Microsoft Q&A question unpacked
- The concrete recommended architecture
- F-SKU realism — what this actually costs
- When the rejected approach is actually right
- Risks of the recommended path
- The architectural principle to take home
Key takeaways
- Driver हमेशा वहाँ है। बस आप तय कीजिए — आप उसे manage कर रहे हैं या वो आपको।
- इस वक़्त कहीं एक starter pool में cluster spin कर रहा है, किसी का इंतज़ार कर रहा है कि notebook खोले और पहला cell चलाए। उनमें से आधों को पता नहीं होगा कि नीचे एक driver node है जिसकी memory ceiling उन्हें दिखती ही नहीं। जिन्हें पता है,...
- और जब टूटता है, तो रात दो बजे pipeline में टूटता है। दोपहर दो बजे नहीं जब आप देख रहे हों।
Resources
- What is a notebook?
- Python experience in notebooks
- Configure Starter Pools
- Lakehouse and notebooks
- Data Wrangler
- Manage libraries
- Schedule notebook
- High Concurrency mode
- VS Code extension
- Spark Job Definition
- Notebook best practices
- Author and execute notebooks
- Notebook visualization
About the show
AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
यह podcast AI से बनाया गया है। दोनों आवाज़ें synthetic हैं: Matthias की आवाज़ cloned है और Fabia एक designed AI co-host है।
Brand design based on fabricperiodictable.com.
11 min - वो Folder जो है ही नहीं
Episode 2: OneLake Shortcuts: Virtual Pointers, Real Tradeoffs (हिन्दी)
Fabric Architecture Podcast — हिन्दी
AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host.
Based on the Fabric Periodic Table.
New episodes every Friday. DM Matthias on LinkedIn.
AI-generated voices. Brand design based on fabricperiodictable.com.
12 min - वो File Count जो कोई Track नहीं कर रहा — Fabric Lakehouse
Lakehouse आर्किटेक्चर: एक Storage Layer, कोई बहाना नहीं
Episode 1 • 2026-01-02
Duration: 8:37Fabric Lakehouse सिर्फ एक बार सेट करके भूल जाने वाला data platform नहीं है — यह बात Matthias और Fabia बखूबी समझाते हैं। असली architectural tradeoffs — medallion layers, Delta table maintenance, Direct Lake, और वो situations जहाँ Warehouse बेहतर option होता है।
What we discuss
- A real-world mistake from a pre-Fabric era
- The one question that reframes the architectural debate
- How we got here — predecessor products and evolution
- Why the "obvious" answer is often wrong
- A real Reddit/Microsoft Q&A question unpacked
- The concrete recommended architecture
- F-SKU realism — what this actually costs
- When the rejected approach is actually right
- Risks of the recommended path
- What Microsoft is shipping that changes the calculus
- The architectural principle to take home
Key takeaways
- यही पूरा lesson है। Demo के लिए architect मत करो। Month six के लिए architect करो।
- बिल्कुल valid choice है। अगर आपकी team SQL-first है, Spark की ज़रूरत नहीं, ML नहीं — तो Warehouse genuinely बेहतर हो सकता है। वो tool चुनें जो team और workload से match करे, न कि वो जो architecture slide पर सबसे impressive दिखे।
- बात सही है। Trap नहीं — tradeoff है। आपको Spark मिलता है, flexibility मिलती है, streaming और batch एक ही जगह। लेकिन maintenance आपकी ज़िम्मेदारी है। यही deal है।
About the show
Built on ElevenLabs voice synthesis. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
Built on ElevenLabs voice synthesis. Brand design based on fabricperiodictable.com.
9 min
About Microsoft Fabric Architecture Podcast (हिनदी)
From the publisher's feed
Microsoft Fabric के लिए architecture decisions। असली customer scenarios, cost realism, counter-arguments शामिल। Fabric Friday recordings के साथ weekly episodes। AI द्वारा नि��्मित।