Eitan Blumin’s Post

JSON data type finds its way into #AzureSQL #AzureSQLDatabase in the newest public preview version. Rather than storing a JSON document as a string and parsing it in its entirety when needed, we can now directly access individual elements of such an object within a SQL query! 🔥🔥🔥 #Azure

JSON Type & Aggregates Public Preview in Azure SQL Database | Data Exposed. The native JSON type allows JSON documents to be stored in a binary format. This binary format allows for efficient query processing and in-place modifications of JSON documents. The JSON aggregates - JSON_OBJECTAGG & JSON_ARRAYAGG enable easy aggregation of relational data into a JSON document. Learn more in this episode of Data Exposed with Anna Hoffman and Umachandar Jayachandran.   Watch on Data Exposed    Resources: JSON data type (preview) - SQL Server | Microsoft Learn JSON_OBJECTAGG (Transact-SQL) - SQL Server | Microsoft Learn  JSON_ARRAYAGG... #techcommunity #azure #microsoft https://lnkd.in/gFuDYg8h

Today I earned my "Use Azure Synapse serverless SQL pool to query files in a data lake" badge! I’m so proud to be celebrating this achievement and hope this inspires you to start your own @MicrosoftLearn journey! Through the course, I have learned that Synapse serverless SQL can not only load data from CSV, Parquet, and JSON files but can also store data into the file system(Data Lake).

Azure Synapse Serverless SQL Pool is a powerful feature within Azure Synapse Analytics that allows you to query data directly from your data lake using T-SQL without having to provision dedicated SQL resources. This can be particularly useful for ad-hoc querying and analysis of large volumes of data. Today I learned how to use Azure Synapse serverless SQL pool to query files of various common formats in a data lake and earned a badge from @MicrosoftLearn.

Today I earned my "Microsoft Azure Data Fundamentals: Explore relational data in Azure" trophy! I’m so proud to be celebrating this achievement and hope this inspires you to start your own @MicrosoftLearn journey! ----- Text above the dashed lines not mine. #azure #azurecloud #azuredataengineer #azuresql #sql #sqlserver #sqldeveloper #sqldba #sqldatabase #sqlskills #sqlserverdba #sqllearning #sqljobs #sqljourney #mysql #mysqldba #mariadb #postgresql

check out this cheat sheet on DBSQL which is very useful. #share with your network. for more such posts follow Priyam Jain #databricks #sql #azure #bigdata #dataengineering #azuredatabricks #bigdatadeveloper #sql #cheatsheet #adf #azuredataengineer

Watch my video to learn  How to create Azure SQL Database and connect to the database from local machine? #sql #azuresqlhttps://lnkd.in/gB668NmZ

End-to-End Data Pipeline Part 2: Data Ingestion First created a new Pipeline. Then I connected to my Access MDB file on my machine. This sent me on a 2-day wild goose chase to try to ingest the data into the Azure Data Lake Gen2. . . long story short:: file-servers don't play nicely with client-servers. 😫 Now I understand why everyone and their mother says that modern data stacks should migrate away from Access mdb files and to SQL Server. NI TestStand natively offers connection to local SQL Server databases so the only reason to stay in Access is nostalgia. 😒 OKAY. . . starting over. . . 😤 First Step (take 2), use SSMA to migrate the Access DB to Azure SQL Server. Step 2: Setup Azure SQL server + database to use System Managed Authentication since the server will be accessed by only the main admin user and not a distributed network of users. Had to use SQL to add ADF as user + permissions. Step 3: Get Tables to output a list of all files in the ASQL schema, then Copy Data [For Each] to extract every table in the schema to the Data Lake. Step 4: Debugging run succeeded so I took a deep breath and executed the full flow. . . 5 minutes later (the file is on 500MB), my on-site database was replicated in my Data Lake! Mission Accomplished!! 😎 I am working now on Part 3- Data Transformation -- the plan is to wrap my python-SQL script that I wrote last year (https://lnkd.in/dcz-ZB7B) into a Databricks workflow. Stay tuned for the next post in this series! #azuredataengineer #endtoendproject #azuredataengineeringproject #azuredatafactory #azuredatabricks #azuresynapseanalytics #azuredatalake #datalake #powerbi

Approximate Vector Search with KMeans and Azure SQL | Data Exposed https://buff.ly/4bfWfOZ #AzureSQL #database #Microsoft #madeiradata