A comprehensive collection of SQL scripts for data exploration, analytics, and reporting. These scripts cover various analyses such as database exploration, measures and metrics, time-based trends, cumulative analytics, segmentation, and more. This repository contains SQL queries designed to help data analysts and BI professionals quickly explore, segment, and analyze data within a relational database. Each script focuses on a specific analytical theme and demonstrates best practices for SQL queries.
Exploratory Data Analysis (EDA): This phase focuses on understanding the data through basic queries and aggregations. It involves:Data Profiling: Examining the characteristics of the data. Dimension and Measure Identification: Differentiating between dimensions (categorical data like Category, Products, Birthdate, ID) and measures (numeric data that can be aggregated like Sales, Quantity, Age).
- Data Exploration: Analyzing the range and distribution of dates and the magnitude of measures. Date Exploration: Finding the minimum and maximum dates, and the date difference using functions like DATEDIFF. Measures Exploration: Calculating aggregations like SUM, AVG, and COUNT to understand the data's scale.
*Advanced Data Analytics: This final phase uses complex queries to answer specific business questions and create reports. This involves several types of analysis: Aggregate Analysis: Calculating measures by dimensions, such as Total Sales by Country or Average Price by Product. Trend Analysis: Tracking changes over time, for example, Total Sales by Year. * Cumulative Analysis: Calculating running totals or moving averages.
Window functions are key to this analysis.
- Performance Analysis: Comparing a current measure against a target or a previous period, such as Current Sales vs. Average Sales or Current Year Sales vs. Previous Year Sales. This also uses window functions. Proportional Analysis: Calculating the percentage of a part to the whole, such as (Sales / Total Sales) * 100 by category. Data Segmentation: Categorizing data into groups, like segmenting customers by age or products by sales range using a CASE WHEN statement
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Hi there! I'm Bondalakunta Himanth.I have a strong background in SQL databases and Business Intelligence (BI) tools, with hands-on experience in data analysis. I specialize in extracting, transforming, and interpreting data to support business decision-making and drive insights. My skill set includes creating efficient SQL queries, working with BI platforms, and turning complex datasets into actionable reports and dashboards.