Snowflake Cloud Datawarehouse | ETL | SQL Learning/ Upskilling
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DM @Snowflake_Guide This channel is primarily for people who want to upskill to Snowflake Cloud Dataware House/ETL.
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доля реакций к просмотрам- 13 июн. 2024 г.Learning series #snowflake What types of data can Snowflake store and process? Snowflake is designed to handle a wide range of data types, from structured data to semi-structured data such as JSON, XML, and Avro. Snowflake eliminates the need for a separate NoSQL database to handle semi-structured data, because it can natively ingest, store, and directly query semi-structured data using standard SQL. This simplifies the data architecture and enables analysts to work with different data types within the same platform.1,32%
- 11 июн. 2024 г.Learning series #snowflake What is Snowflake? Developed in 2012, Snowflake is a fully managed SaaS (software as a service) that provides a single platform for data warehousing, data lakes, data engineering, data science, data application development, and secure sharing and consumption of real-time / shared data. Snowflake features out-of-the-box features like separation of storage and compute, on-the-fly scalable compute, data sharing, data cloning, and third-party tools support in order to handle the demanding needs of growing enterprises1,00%
- 5 окт. 2024 г.без подписи0,32%
- 12 окт. 2024 г.𝐒𝐐𝐋 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐏𝐫𝐨 𝐓𝐢𝐩𝐬! Are your SQL queries experiencing a slowdown? It's time to ramp up your database performance with these 20 expert tips! 💡 1: Preferably, use UNION ALL for faster query results unless removing duplicates is essential. 2: Avoid using OR in join conditions to prevent substantial impacts on query speed. 3: Use UNION instead of UNION ALL when eliminating duplicates from combined queries. 4: Minimize network latency by performing computations or filtering closer to the database using stored procedures. 5: Regularly update statistics and indexes on tables to ensure optimal query execution plans. 6: Simplify and improve code readability by restructuring subqueries for better efficiency. 7: Minimize using functions on the right-hand side of operators for smoother query execution. 8: Employ EXISTS rather than DISTINCT in one-to-many table joins to boost query performance. 9: Specify individual column names instead of using '*' in SELECT statements for improved accuracy and speed. 10: Optimize JOIN order by arranging joins based on the smallest result set first for faster processing. 11: Streamline queries by eliminating redundant mathematical operations, promoting a simpler and faster approach. 12: Leverage the IN predicate when working with indexed columns for enhanced query efficiency. 13: Break down complex queries into smaller, more manageable parts for improved execution time. 14: Implement query caching to store frequently executed queries for faster retrieval. 15: Profile query performance using tools like EXPLAIN or query execution plans to identify bottlenecks. 16: Avoid unnecessary nesting or chaining of views to reduce query complexity and improve performance. 17: Use the HAVING clause judiciously within SELECT statements to prevent potential query slowdowns. 18: Consider selective database denormalization to reduce JOINs and boost query speed for specific operations. 19: Reduce the use of DISTINCT conditions to only necessary instances for quicker query performance. 20: Regularly review and fine-tune your queries for enhanced efficiency and optimal performance. Share with ur friends/colleagues whom you think this might be useful #Learning0,30%
- 4 нояб. 2024 г.без подписи0,21%
- 27 авг. 2024 г.Learning series #snowflake What is Snowflake? Developed in 2012, Snowflake is a fully managed SaaS (software as a service) that provides a single platform for data warehousing, data lakes, data engineering, data science, data application development, and secure sharing and consumption of real-time / shared data. Snowflake features out-of-the-box features like separation of storage and compute, on-the-fly scalable compute, data sharing, data cloning, and third-party tools support in order to handle the demanding needs of growing enterprises0,14%
- 28 авг. 2024 г.Learning series #snowflake What types of data can Snowflake store and process? Snowflake is designed to handle a wide range of data types, from structured data to semi-structured data such as JSON, XML, and Avro. Snowflake eliminates the need for a separate NoSQL database to handle semi-structured data, because it can natively ingest, store, and directly query semi-structured data using standard SQL. This simplifies the data architecture and enables analysts to work with different data types within the same platform.0,14%
- 31 окт. 2024 г.Wish everyone a very Happy and prosperous Diwali0,14%
- 15 окт. 2024 г.Answer is one... Star schema has one fact with N number of dimensions around it.. Snowflake schema you have fact table with dimensions around with dimensions connected to another dimensions0,13%
- 31 авг. 2024 г.без подписи0,11%
- 27 окт. 2024 г.Snowflake playing nicely with other data lakes: At this time Snowflake supports three external tables or external stages: AWS S3, Azure Blob, and GCP Cloud Storage. You can use the "External Table" type to point to a data lake that is not housed in the Snowflake environment but allows you to perform compute read-only in the Snowflake environment. When using Snowpipe to load data into Snowflake you can use serverless computing, which reduces cost as data warehouses are not required.0,08%
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