Jason Obihara on LinkedIn: Programming for SAS Viya was issued by SAS to Jason Obihara. (2024)

Just finished the course “SAS® 9.4 Cert Prep: Part 04 Preparing Data” by SAS Institute! Check it out: https://lnkd.in/dk7SNnEG #dataprocessing #sas.

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SAS Enterprise Guide 8.4 is out with the ability to connect to SAS Viya !!Find below a simple step by step guide on how to connect to SAS Viya, and how to simply reopen your existing Enterprise Guide project and execute it on SAS Viya compute resources.https://lnkd.in/ey3vAJnF

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From Will's recent blog:In 2024, the three big themes we are interested in are performance, ecosystem, and SQL standard compliance.On the performance side, the EARLIEST/LATEST aggregators now support numerical columns during ingestion. Making it easier for you to model the data into better shapes for querying.On the ecosystem side, we continue to improve the multi-stage query (MSQ) engine, adding more cloud support and the ability to export data. We’ve also improved the ingestion system. You can now ingest folder names (such as parquet partitions) into Druid. This enabled Druid to source data from Deltalake, making it substantially easier to integrate Druid into the rest of your data platform.On the SQL standard compliance side, we have added support for PIVOT/UNPIVOT and unequal joins. Window functions have also seen substantial improvements and are ready for testing in more use cases.Lastly, there are 2 new extensions, Spectator Histogram and DD Sketch, both of which provide improved versions of current data sketches for specific use cases.Read the full blog here: https://bit.ly/4bMbTm9

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-- SELECT statement is used to specify the columns and rows to be retrieved.-- In this case, TOP 100 * selects the top 100 rows along with all columns.SELECT TOP 100 *-- FROM clause specifies the source of the data.-- OPENROWSET is used to access external data sources.-- In this example, it reads data from a CSV file stored in Azure Data Lake Storage.FROM OPENROWSET( BULK 'https://lnkd.in/dz2V7bM9', -- URL of the CSV file FORMAT = 'CSV', -- Specifies that the file is in CSV format PARSER_VERSION='2.0' -- Specifies the parser version to be used ) WITH ( -- Schema definition for the columns in the CSV file SalesOrderNumber VARCHAR(10) COLLATE Latin1_General_100_BIN2_UTF8, SalesOrderLineNumber INT, OrderDate DATE, CustomerName VARCHAR(25) COLLATE Latin1_General_100_BIN2_UTF8, EmailAddress VARCHAR(50) COLLATE Latin1_General_100_BIN2_UTF8, Item VARCHAR(30) COLLATE Latin1_General_100_BIN2_UTF8, Quantity INT, UnitPrice DECIMAL(18,2), TaxAmount DECIMAL (18,2) ) AS [result];

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Did you know that SAS Enterprise Guide can now integrate with SAS Viya 4.0? Learn from Wilbram Hazejager on how the very popular SAS desktop product can be continued to be used with a SAS Viya 4.0 server environment. Watch the demo (in the SAS Viya Release Highlights show) - using Project Maintenance to change the server association will make it easy and be well-received!#SASviya #SAS #SASusers #analytics #data #AIhttps://lnkd.in/gBcRmyCY

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I love and hate the declartive nature of SQL 🤪In theory it’s so beautiful, one separates the what from the how🤩 but as always there are caveats I feel that there are cases when it isn’t declarative enough, especially when we look from the business definiton angle 🧑💼 and especially when single query is not enough Let’s look example from attribute resolution in data warehouse setting. In this case priority order / attribute override✏️In this case Price by default comes from one system, but there are cases where the correct information comes from elsewhere and the price need to be overwritten💲The smartasses in the audience yell that it’s just simple coalesce, and in limited scope they are right. But…But when you say it’s coalesce you lock the business definion with the implementation. And need to make decision on which part of the pipeline this happens. All while thinking about all the other attributes and how those should be resolved in respect to this one… 🤯This can lead to example massive hard to maintain query or tons of small dbt models that burn sh*tton of cloud credits… 💸What we’re building at reconfigured is tool to build these business concepts in a GUI and then when all is done it compiles these definitions into full SQL pipeline, optimizing the location for different needed computations 🤓Here Erno is focusing on the business definition and reconfigured tool focusing on pipeline query writing 🥰#sql #dataengineering #dataanalytics #analyticsengineering

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Last month I had the privilege of being a facilitator for the 1000 Corpers Analytics Workshop organised by my boss Adewale Yusuf (MVP, MCSA, MCDA, MCT). I saw the post and messaged him I would love to be involved in this project. He said no problem. Adewale Yusuf (MVP, MCSA, MCDA, MCT) has helped me a lot in my data analytics, He trained me and I am glad am training others too to be an expert. This vision He has will actually outlive Him because He has successors already. I appreciate it so much, Sir.Here are the areas I took the Corpers using MS Server SQL:1. How to create databases and tables2. How to create relationships between entities3. Terms used in database designs4. Naming your entities and attributes and your keys for easy comprehension5. Various data types and how to use the correct ones6. Keys (Primary and Foreign Keys)7. Cascading Inferential Integrity and uses8. Various constraints and their application (default, null, not null, check, unique, etc) 9. Entity Relationship Diagram using the keys stated above.10. Database OptimizationAll stated above were done using SQL queries and the GUI of SSMS. It was such a great time to share my little knowledge with them as this is what I am passionate about.I am available to share knowledge anytime. My DM is free. Here is the link to the recorded session:https://lnkd.in/guHMPr4f#dataanalytics #mssqlserver #communitydevelopment

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Have you ever seen WHERE 1=1 in a SQL query?Have you scratched your head at what it means?Maybe because you don't see any actual filtering happening in the query being returned... Well, let's think about the WHERE clause and what it does. It filters records based on whatever condition you specify as being true. SELECT account_id FROM accounts WHERE state='AZ' This query returns all accounts that were created in the state of Arizona. SELECT account_id FROM accounts WHERE is_active When specifying a boolean in the WHERE clause, SQL looks for the values where this field is true.This query will return all active accounts. When you see WHERE 1=1, this is a true statement, so all records will be returned in the query. I've found this helpful in using the QUALIFY clause in Redshift, as you need to specify a WHERE clause to use this function. What are some other examples of when you might want to use WHERE 1=1 in a SQL query?⬇ Comment below #SQL #dataanalytics

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You can load on-prem Excel to any cloud database withData Controller for SAS®1 - Load2 - Approve3 - ProfitThe approach is:✅ Zero Code - just configure PK, load type and security✅ Fully auditable - and we don't just mean log files!✅ Secure - no direct user access permissionsNot only that, Data Controller works for:🧑🏭 Any version of #Excel - 5.0/95 through xlsm & xlsb🧑🏭 Any version of #SAS - #Viya, EBI or Base🧑🏭 Any Budget. Starting price - zero!When loading from Excel, data can be on any sheet, starting on any row and any column, with columns in any order. Password protection is supported, as well as loading of formulae rather than values. Copies of the original Excel are always kept, alongside the staged data.It's thanks to SAS/ACCESS engines that we can load pretty much ANY database you can think of! We have customers on Postgres, Oracle, Netezza, SQL Server, Teradata and Redshift.Common data loading techniques such as SCD2, Bitemporal, retained keys, composite keys, full replace and more are all supported.#sasviya #sassoftware#masterdatamanagement

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📌 QUALIFY vs HAVING: The QUALIFY clause filters results of window functions while HAVING targets aggregate functions or GROUP BY clauses. Think of QUALIFY as a modern way to streamline your SQL!🚀 Simplify with QUALIFY: Previously, we used CTEs or subqueries for filtering window function results. Now, QUALIFY lets us skip that, making SQL more efficient. Compare example #1 (CTE method) with example #2 (QUALIFY method).💡 When to Use QUALIFY: -Perfect for simple tasks. -For complex queries with multiple joins, stick with CTEs for their flexibility.🌐 Platform Support: it's available in BigQuery, Databricks, Teradata, Snowflake, and now Redshift!#SQLTips #DataOptimization

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