AR

With over a decade of experience in data engineering, analytics, and artificial intelligence, you are a seasoned professional adept at managing complex data ecosystems and leveraging AI technologies. Your expertise spans across database management, data analysis, visualization, and AI applications, with proficiency in languages like Python and SQL, as well as advanced tools such as Spark, Snowflake, Hadoop, and various machine learning frameworks. As a Data Solutions Engineer at Vail Systems, you've demonstrated your ability to prototype data models, maintain data quality, and enable cloud technology adoption, while incorporating AI-driven insights into your solutions. Your track record includes developing robust ETL/ELT processes, optimizing data pipelines, and partnering with cross-functional teams to drive product improvements through data-driven and AI-enhanced approaches. Your work with speech and voice analytics data, as well as high-volume telephony systems, showcases your capability to handle diverse and complex datasets, applying artificial intelligence techniques for advanced pattern recognition and predictive modeling. With a proven history of increasing efficiency and reliability in data operations and implementing cutting-edge AI solutions, you bring a valuable blend of technical skills, AI expertise, and business acumen to any data-driven organization looking to harness the power of artificial intelligence.

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SQLServerCentral Article

JSON in Microsoft SQL Server: A Comprehensive Guide

Introduction JSON (JavaScript Object Notation) has become a popular data format for storing and exchanging information. Microsoft SQL Server, starting from version 2016, introduced built-in support for JSON, allowing developers to work with JSON data more efficiently within the relational database environment. This article will explore how to store, retrieve, and manipulate JSON data in […]

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2025-01-31

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Question of the Day

The Rank Window

I have this table and data: 

CREATE TABLE [dbo].[SalesTracking]
(
[SalesDate] [datetime] NULL,
[SalesPersonID] [int] NULL,
[CustomerID] [int] NOT NULL,
[PONumber] [varchar] (80) COLLATE SQL_Latin1_General_CP1_CI_AS NULL,
[paid] [bit] NULL
) ON [PRIMARY]
GO
CREATE CLUSTERED INDEX [SalesTrackingCDX] ON [dbo].[SalesTracking] ([SalesDate]) ON [PRIMARY]
GO
INSERT dbo.SalesTracking
  (SalesDate, SalesPersonID, CustomerID, PONumber, paid, total)
VALUES
  ('2024-03-15 10:45:55.067', 1, 1,'PO965' ,1, 100),
  ('2023-09-24 10:45:55.067', 1, 2,'PO627' ,1, 200),
  ('2022-07-02 10:45:55.067', 1, 3,'PO6'   ,1, 300),
  ('2022-11-03 10:45:55.067', 1, 4,'PO283' ,1, 400),
  ('2022-11-26 10:45:55.067', 1, 5,'PO735' ,1, 500),
  ('2023-04-28 10:45:55.067', 1, 6,'PO407' ,1, 600),
  ('2022-09-09 10:45:55.067', 1, 7,'PO484' ,1, 700),
  ('2024-03-13 10:45:55.067', 1, 8,'PO344' ,1, 700),
  ('2024-04-24 10:45:55.067', 1, 9,'PO254' ,1, 800),
  ('2022-06-19 10:45:55.067', 1, 10,'PO344',1, 800)
GO
When I run this query, how many unique values are returned for the SaleRank column? 
SELECT
  st.SalesDate
, st.SalesPersonID
, st.total
, RANK () OVER (PARTITION BY st.SalesPersonID
                ORDER BY st.total desc) AS SaleRank
FROM dbo.SalesTracking AS st;

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