#cosmosdb — Public Fediverse posts

Hilarious #SitecoreLunch today. Discussed:

🌌 #CosmosDB
🏦 A little civics
🐟 Fishbowl cocktails
🎇 #IndependenceDay
🏴‍☠️ Flying country flags
🔢 Future of serialization
🟪 #Sitecore placeholders
🐭 #DisneyLand vs. #DisneyWorld
📝 Managing #Sitecore Page and Partial Designs

See you same time next week! 🥪🥗

Hilarious #SitecoreLunch today. Discussed:

🌌 #CosmosDB
🏦 A little civics
🐟 Fishbowl cocktails
🎇 #IndependenceDay
🏴‍☠️ Flying country flags
🔢 Future of serialization
🟪 #Sitecore placeholders
🐭 #DisneyLand vs. #DisneyWorld
📝 Managing #Sitecore Page and Partial Designs

See you same time next week! 🥪🥗

Hilarious #SitecoreLunch today. Discussed:

🌌 #CosmosDB
🏦 A little civics
🐟 Fishbowl cocktails
🎇 #IndependenceDay
🏴‍☠️ Flying country flags
🔢 Future of serialization
🟪 #Sitecore placeholders
🐭 #DisneyLand vs. #DisneyWorld
📝 Managing #Sitecore Page and Partial Designs

See you same time next week! 🥪🥗

Hilarious #SitecoreLunch today. Discussed:

🌌 #CosmosDB
🏦 A little civics
🐟 Fishbowl cocktails
🎇 #IndependenceDay
🏴‍☠️ Flying country flags
🔢 Future of serialization
🟪 #Sitecore placeholders
🐭 #DisneyLand vs. #DisneyWorld
📝 Managing #Sitecore Page and Partial Designs

See you same time next week! 🥪🥗

Hilarious #SitecoreLunch today. Discussed:

🌌 #CosmosDB
🏦 A little civics
🐟 Fishbowl cocktails
🎇 #IndependenceDay
🏴‍☠️ Flying country flags
🔢 Future of serialization
🟪 #Sitecore placeholders
🐭 #DisneyLand vs. #DisneyWorld
📝 Managing #Sitecore Page and Partial Designs

See you same time next week! 🥪🥗

Cloud thinking: Data in 3rd denormalised form

One schema to rule them all and in the darkness bind them.

That’s the SQL way. One schema, many tables, be efficient with space. Join, don’t duplicate. Third normal form and structured data. More structure.

But that’s not always the best way to scale. How important is it that everything is identical everywhere? How important is history? When a customer moves house, do you want to know the difference between the address they want their next order delivered to and the address the last order was delivered to?

When you let go of SQL and joins, and when storage is cheap, does it matter if you have multiple copies, so long as each document has the right copy for itself?

When you aggregate data, when your change log turns multiple orders into 1 delivery schedule for your driver, does it matter that the driver has their own copy for when they lose signal? Does it matter that there’s a copy of the addresses in the route that will be deleted tomorrow because it serves no more purpose?

If storage is cheap and joins and transmissions are expensive, wouldn’t you want to cache data and trade storage for speed?

Shouldn’t you embrace heterogeneous data? The address on your payment looks like the address on the order, but it serves a different purpose, it’s embedded in a different context, and it has a different lifecycle.

It’s sometimes hard to change your mental model, but it can save you from doing the wrong thing.

Cloud thinking: Data in 3rd denormalised form

One schema to rule them all and in the darkness bind them.

That’s the SQL way. One schema, many tables, be efficient with space. Join, don’t duplicate. Third normal form and structured data. More structure.

But that’s not always the best way to scale. How important is it that everything is identical everywhere? How important is history? When a customer moves house, do you want to know the difference between the address they want their next order delivered to and the address the last order was delivered to?

When you let go of SQL and joins, and when storage is cheap, does it matter if you have multiple copies, so long as each document has the right copy for itself?

When you aggregate data, when your change log turns multiple orders into 1 delivery schedule for your driver, does it matter that the driver has their own copy for when they lose signal? Does it matter that there’s a copy of the addresses in the route that will be deleted tomorrow because it serves no more purpose?

If storage is cheap and joins and transmissions are expensive, wouldn’t you want to cache data and trade storage for speed?

Shouldn’t you embrace heterogeneous data? The address on your payment looks like the address on the order, but it serves a different purpose, it’s embedded in a different context, and it has a different lifecycle.

It’s sometimes hard to change your mental model, but it can save you from doing the wrong thing.

Cloud thinking: Data in 3rd denormalised form

One schema to rule them all and in the darkness bind them.

That’s the SQL way. One schema, many tables, be efficient with space. Join, don’t duplicate. Third normal form and structured data. More structure.

But that’s not always the best way to scale. How important is it that everything is identical everywhere? How important is history? When a customer moves house, do you want to know the difference between the address they want their next order delivered to and the address the last order was delivered to?

When you let go of SQL and joins, and when storage is cheap, does it matter if you have multiple copies, so long as each document has the right copy for itself?

When you aggregate data, when your change log turns multiple orders into 1 delivery schedule for your driver, does it matter that the driver has their own copy for when they lose signal? Does it matter that there’s a copy of the addresses in the route that will be deleted tomorrow because it serves no more purpose?

If storage is cheap and joins and transmissions are expensive, wouldn’t you want to cache data and trade storage for speed?

Shouldn’t you embrace heterogeneous data? The address on your payment looks like the address on the order, but it serves a different purpose, it’s embedded in a different context, and it has a different lifecycle.

It’s sometimes hard to change your mental model, but it can save you from doing the wrong thing.

Cloud thinking: Data in 3rd denormalised form

One schema to rule them all and in the darkness bind them.

That’s the SQL way. One schema, many tables, be efficient with space. Join, don’t duplicate. Third normal form and structured data. More structure.

But that’s not always the best way to scale. How important is it that everything is identical everywhere? How important is history? When a customer moves house, do you want to know the difference between the address they want their next order delivered to and the address the last order was delivered to?

When you let go of SQL and joins, and when storage is cheap, does it matter if you have multiple copies, so long as each document has the right copy for itself?

When you aggregate data, when your change log turns multiple orders into 1 delivery schedule for your driver, does it matter that the driver has their own copy for when they lose signal? Does it matter that there’s a copy of the addresses in the route that will be deleted tomorrow because it serves no more purpose?

If storage is cheap and joins and transmissions are expensive, wouldn’t you want to cache data and trade storage for speed?

Shouldn’t you embrace heterogeneous data? The address on your payment looks like the address on the order, but it serves a different purpose, it’s embedded in a different context, and it has a different lifecycle.

It’s sometimes hard to change your mental model, but it can save you from doing the wrong thing.

Cloud thinking: Data in 3rd denormalised form

One schema to rule them all and in the darkness bind them.

That’s the SQL way. One schema, many tables, be efficient with space. Join, don’t duplicate. Third normal form and structured data. More structure.

But that’s not always the best way to scale. How important is it that everything is identical everywhere? How important is history? When a customer moves house, do you want to know the difference between the address they want their next order delivered to and the address the last order was delivered to?

When you let go of SQL and joins, and when storage is cheap, does it matter if you have multiple copies, so long as each document has the right copy for itself?

When you aggregate data, when your change log turns multiple orders into 1 delivery schedule for your driver, does it matter that the driver has their own copy for when they lose signal? Does it matter that there’s a copy of the addresses in the route that will be deleted tomorrow because it serves no more purpose?

If storage is cheap and joins and transmissions are expensive, wouldn’t you want to cache data and trade storage for speed?

Shouldn’t you embrace heterogeneous data? The address on your payment looks like the address on the order, but it serves a different purpose, it’s embedded in a different context, and it has a different lifecycle.

It’s sometimes hard to change your mental model, but it can save you from doing the wrong thing.

The Azure Cosmos DB Data Migration Desktop Tool makes it easy to move data into Azure Cosmos DB without building a one-off importer.
#Azure #CosmosDB #Mongodb #docker
https://github.com/AzureCosmosDB/data-migration-desktop-tool

via @dotnet : Vector Data in .NET – Building Blocks for AI Part 2

https://ift.tt/VtJUvye
#VectorData #NET #AI #BuildingBlocks #SemanticSearch #RAG #Embedding #Embeddings #VectorDatabase #Qdrant #Redis #CosmosDB #SQLServer #PostgreSQL #SQLite #InMemory #VectorSto…

via @dotnet : Vector Data in .NET – Building Blocks for AI Part 2

https://ift.tt/VtJUvye
#VectorData #NET #AI #BuildingBlocks #SemanticSearch #RAG #Embedding #Embeddings #VectorDatabase #Qdrant #Redis #CosmosDB #SQLServer #PostgreSQL #SQLite #InMemory #VectorSto…

via @dotnet : Vector Data in .NET – Building Blocks for AI Part 2

https://ift.tt/VtJUvye
#VectorData #NET #AI #BuildingBlocks #SemanticSearch #RAG #Embedding #Embeddings #VectorDatabase #Qdrant #Redis #CosmosDB #SQLServer #PostgreSQL #SQLite #InMemory #VectorSto…

via @dotnet : Vector Data in .NET – Building Blocks for AI Part 2

https://ift.tt/VtJUvye
#VectorData #NET #AI #BuildingBlocks #SemanticSearch #RAG #Embedding #Embeddings #VectorDatabase #Qdrant #Redis #CosmosDB #SQLServer #PostgreSQL #SQLite #InMemory #VectorSto…

via @dotnet : Vector Data in .NET – Building Blocks for AI Part 2

https://ift.tt/VtJUvye
#VectorData #NET #AI #BuildingBlocks #SemanticSearch #RAG #Embedding #Embeddings #VectorDatabase #Qdrant #Redis #CosmosDB #SQLServer #PostgreSQL #SQLite #InMemory #VectorSto…

https://www.youtube.com/watch?v=FAc873nQ5KU - OmniRAG: how to do #AI #RAG with #Azure #CosmosDB. Thanks for the walk-through Aleksey Savateyev.

https://www.youtube.com/watch?v=FAc873nQ5KU - OmniRAG: how to do #AI #RAG with #Azure #CosmosDB. Thanks for the walk-through Aleksey Savateyev.

https://www.youtube.com/watch?v=FAc873nQ5KU - OmniRAG: how to do #AI #RAG with #Azure #CosmosDB. Thanks for the walk-through Aleksey Savateyev.

https://www.youtube.com/watch?v=FAc873nQ5KU - OmniRAG: how to do #AI #RAG with #Azure #CosmosDB. Thanks for the walk-through Aleksey Savateyev.

https://www.youtube.com/watch?v=FAc873nQ5KU - OmniRAG: how to do #AI #RAG with #Azure #CosmosDB. Thanks for the walk-through Aleksey Savateyev.

Announcing: Dynamic Data Masking for Azure Cosmos DB
#DataSecurity #DataPrivacy #Azure #CosmosDB #Cybersecurity #nosql
https://learn.microsoft.com/en-us/azure/cosmos-db/dynamic-data-masking?wt.mc_id=MVP

Fleet analytics in Azure Cosmos DB
#Azure #CosmosDB #CostManagement #CostAnalysis
https://learn.microsoft.com/en-us/azure/cosmos-db/fleet-analytics?wt.mc_id=DP-MVP-4015656

Microsoft Azure Virtual Training Day: Data Fundamentals
#training #databricks #MicrosoftFabric #OneLake #Cloud #analytics #BigData #DataVisualization #CosmosDB #Azure
https://msevents.microsoft.com/event?id=3491783715

#cosmosdb is not happy tech

#cosmosdb is not happy tech

#cosmosdb is not happy tech

#cosmosdb is not happy tech

#cosmosdb is not happy tech

Learn to Build Your Own AI Apps with Azure Cosmos DB!
#AI #ArtificialIntelligence #Azure #CosmosDB
https://www.youtube.com/watch?v=SzpueCxajKI&feature=youtu.be

Query a Cosmos DB Database in Microsoft Fabric
#Azure #CosmosDB #MicrosoftFabric #NoSQL #DataEngineering
https://www.youtube.com/watch?v=JXPYjHVn3sI

Developing a Python app using Azure Cosmos DB
#Azure #CosmosDB #Python #NoSQL
https://github.com/Azure-Samples/azure-cosmos-db-python-getting-started

Developing a Python app using Azure Cosmos DB
#Azure #CosmosDB #Python #NoSQL
https://github.com/Azure-Samples/azure-cosmos-db-python-getting-started

Developing a Python app using Azure Cosmos DB
#Azure #CosmosDB #Python #NoSQL
https://github.com/Azure-Samples/azure-cosmos-db-python-getting-started

Developing a Python app using Azure Cosmos DB
#Azure #CosmosDB #Python #NoSQL
https://github.com/Azure-Samples/azure-cosmos-db-python-getting-started

Developing a Python app using Azure Cosmos DB
#Azure #CosmosDB #Python #NoSQL
https://github.com/Azure-Samples/azure-cosmos-db-python-getting-started

A small security issue that I have discovered and reported is now mitigated. This is great work by the team behind CosmosDB vCore of acknowledging it, fixing it and being transparent with the customers 🙇 #Azure #CosmosDB

A small security issue that I have discovered and reported is now mitigated. This is great work by the team behind CosmosDB vCore of acknowledging it, fixing it and being transparent with the customers 🙇 #Azure #CosmosDB

AI-Enhanced advertisement generation using Azure Cosmos DB for MongoDB vCore
#AI #ArtificialIntelligence #CosmosDB #Mongodb #Azure #OpenAI #pyth
on
https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/ai-advertisement-generation?wt.mc_id=DP-MVP-4015656

AI-Enhanced advertisement generation using Azure Cosmos DB for MongoDB vCore
#AI #ArtificialIntelligence #CosmosDB #Mongodb #Azure #OpenAI #pyth
on
https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/ai-advertisement-generation?wt.mc_id=DP-MVP-4015656

AI-Enhanced advertisement generation using Azure Cosmos DB for MongoDB vCore
#AI #ArtificialIntelligence #CosmosDB #Mongodb #Azure #OpenAI #pyth
on
https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/ai-advertisement-generation?wt.mc_id=DP-MVP-4015656

AI-Enhanced advertisement generation using Azure Cosmos DB for MongoDB vCore
#AI #ArtificialIntelligence #CosmosDB #Mongodb #Azure #OpenAI #pyth
on
https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/ai-advertisement-generation?wt.mc_id=DP-MVP-4015656

AI-Enhanced advertisement generation using Azure Cosmos DB for MongoDB vCore
#AI #ArtificialIntelligence #CosmosDB #Mongodb #Azure #OpenAI #pyth
on
https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/ai-advertisement-generation?wt.mc_id=DP-MVP-4015656

Azure Cosmos DB Python Basics: Data Modeling with Pydantic
#Python #Azure #CosmosDB
https://www.youtube.com/watch?v=9sJGxigUido

Azure Cosmos DB Python Basics: Data Modeling with Pydantic
#Python #Azure #CosmosDB
https://www.youtube.com/watch?v=9sJGxigUido

Azure Cosmos DB Python Basics: Data Modeling with Pydantic
#Python #Azure #CosmosDB
https://www.youtube.com/watch?v=9sJGxigUido

Azure Cosmos DB Python Basics: Data Modeling with Pydantic
#Python #Azure #CosmosDB
https://www.youtube.com/watch?v=9sJGxigUido

Azure Cosmos DB Python Basics: Data Modeling with Pydantic
#Python #Azure #CosmosDB
https://www.youtube.com/watch?v=9sJGxigUido

Data API builder 1.6: Advanced Behaviors with Special HTTP Headers | by Jerry Nixon

https://devblogs.microsoft.com/azure-sql/data-api-builder-16-http-headers/

#database #sqlserver #azuresql #data #apis #cosmosdb

Data API builder 1.6: Advanced Behaviors with Special HTTP Headers | by Jerry Nixon

https://devblogs.microsoft.com/azure-sql/data-api-builder-16-http-headers/

#database #sqlserver #azuresql #data #apis #cosmosdb