❄ Did you know Snowflake has duplicated functions? There are for example: - RLIKE & REGEXP - NVL & IFNULL - CAST & TO_<EVERY_TYPE> … and some other different use cases like "::" to also cast data types Even tho their output is exactly the same, there is a reason behind why they are still here. And the answer is…. Compatibility with different SQL dialects! Imagine migration from Oracle (or some other DB) to Snowflake without knowing all of these new & fancy functions. You would need to go through your SQL very carefully (I still recommend to do that) and change most of the Oracle's not-ANSI functions for a Snowflake ones. Which sometimes can be challenging. Snowflake simplify this process with the support of "legacy" functions so there is no need to do that. It can also help with code readability, so you don't need to instantly switch to a brand-new different functions and patterns. Do you know some other examples? #Snowflake #dataengineering
Sebastian Flak’s Post
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Sometimes the little things go a long way. If you are a #sql developer you've probably had experience below. But, no longer - Snowflake now allows "trailing commas"! Read more from our own Qinyi Ding by following the link below, https://lnkd.in/gnUb33Eu #sqldelight #sqldeveloper #sqlskills #datawarehousing #dataengineering #datacloud
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It is great to see extensions to the DDL syntax that help address the pain of having to safely make incremental changes to the table structures.
Making the lives of Snowflake SQL developers easier, one delightful feature at a time. Check out this blog post from Qinyi Ding to learn about our new syntax that supports adding/deleting columns using IF [NOT] EXISTS. https://lnkd.in/d4-r_yc5
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Making the lives of Snowflake SQL developers easier, one delightful feature at a time. Check out this blog post from Qinyi Ding to learn about our new syntax that supports adding/deleting columns using IF [NOT] EXISTS. https://lnkd.in/d4-r_yc5
Snowflake Supports IF [NOT] EXISTS at Column Level
medium.com
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Yesterday, BigQuery introduced the (must-have ?) GROUP BY ALL feature. This comes 8 months after Snowflake released the identical feature. Also comes a year after Databricks added it to their arsenal. And follows a couple of years after DuckDB introduced the same feature. Great to see databases embracing #sql upgrades! EDIT : added Databricks
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Unlock the full potential of your Amazon Redshift database Connect effortlessly with a JDBC driver, create tables and load data from S3 or local disks, and build powerful queries using the visual SQL Query Builder. Generate insightful charts, dashboards, and ad-hoc reports, and collaborate seamlessly across teams with our SQL sharing and job scheduling features. Explore more about how DBHawk can streamline your Redshift database management: https://lnkd.in/e4xzjXMA #AmazonRedshift #DatabaseManagement #SQLWorkbench #DataAnalytics #TechInnovation #CloudComputing #DataVisualization #TeamCollaboration #DBHawk #SQL
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Hooking Snowflake up to your application? At first I was more than skeptical. Having connected similar data warehousing tools in the past (with “mixed results” being a kind recount of the experience) I had my doubts about how effective a strategy this could truly be. 1 year and two successful deployments of Snowflake to 2 applications later, I can safely say it has been the easiest, most performant, and seamless integration of this kind I have ever managed. To be clear, I am not saying one should replace their application database with Snowflake. MongoDB or Postgres are still top of the stack (pun) when it comes to running the nuts and bolts of your application; but if you have data that you’re bringing in from outside the friendly confines of your application – especially if you are a Snowflake shop already – I would highly recommend an integration if you have the right circumstance(s). Just be careful that you write the appropriate safeguards, throttle the warehouse you have dedicated to the service, and tighten up your queries. It’s still “pay as you go”, so you want to make sure you don’t have runaway costs because of a bug or oversight in your application.
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Have you heard of the Snowflake query hash? When looking for tuning opportunities DBAs and engineers often focus on long running queries. Queries that run faster but where the same query is run over and over again often fall under the optimisation rug. Don't miss out on those tuning and cost saving opportunities. In Snowflake you can use a feature called the query hash to identify identical queries. The feature removes noise such as comments, parameters and white space from an SQL query and then calculates a unique hash from that query, which you can use to identify trends and patterns and indeed queries that are executed frequently by looking at the query history https://lnkd.in/dk2VydXE
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❄ Snowflake is case-insensitive by default. This means that "TABLE_NAME" and "table_name" refer to the same object. But you can make Snowflake case-sensitive with quoted identifiers. To ignore the case of quoted identifiers in Snowflake, you need to change the `QUOTED_IDENTIFIERS_IGNORE_CASE` parameter to `TRUE`. You can do this at the session or account level. To see the current value of this parameter (the default is `FALSE`), use the `SHOW PARAMETERS` command. ALTER SESSION SET QUOTED_IDENTIFIERS_IGNORE_CASE = TRUE; SHOW PARAMETERS LIKE 'QUOTED_IDENTIFIERS_IGNORE_CASE'; One common use case is when moving databases from systems that enforce case sensitivity. By enabling the `QUOTED_IDENTIFIERS_IGNORE_CASE` parameter, Snowflake can act like the original system, making migration easier. But, be careful when using this parameter. If you have two tables named `TABLE_NAME` and `table_name` and reference them without quotes, Snowflake will treat them as the same table. This can cause unexpected results. So, use this feature with caution.
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Common Table Expression (#CTE) is a temporary named result set that you can reference within a single SQL statement. It can be used multiple times in that statement to simplify complex queries. And the best part? #Snowflake not only supports CTEs but also supercharges 'em with additional optimizations for peak performance ⚡!! In our latest piece, we: 👉 Uncover What are #SnowflakeCTE? 👉 Delve into the Strength of Snowflake CTEs 👉 Highlight the benefits of using Snowflake CTEs 👉 Explore the Types of Snowflake CTEs 👉 Dive deep into Snowflake CTE & Query Plan 👉 Use cases and Performance improvements with Snowflake CTE 👉 Limitations of using Snowflake CTE ... and so much more!! Think of Snowflake CTEs as Lego blocks🧱. Each block is simple on its own, but when combined, they craft a magnificent structure! To learn more, dive right in: https://lnkd.in/gxfRCaqU
Snowflake CTE 101: How to utilize CTE (2024)
chaosgenius.io
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How to upload CSV to Snowflake: 3 approaches Snowflake users are often faced with the need to import data in CSV format into Snowflake tables. This article aims to address this need by providing three methods to streamline the import process.... Read more on following #n8n Blog post!
How to upload CSV to Snowflake: 3 approaches
blog.n8n.io
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