Ray Givler’s Post

🔨 So in some database systems, if you divide an INT by an INT you get an INT and therefore drop decimal precision.   There is a hack to circumvent this - multiple by 1.0 to convert the numerator to a float/real.   However, in TERADATA, the precision of that multiplier apparently affects the precision of the answer, as I discovered when testing my dashboard.   😲TERADATA multiplied 4.49367... by 1.0 and got 4.5 and then rounded that up to 5.   ✔️ Tableau rounded 4.49367 to 4.   When I changed my TERADATA multiplier to 1.00000, it rounded to 4.   As an aside, you can also CAST data types in TERADATA.   🏗→🧠 Build to Learn!   💭🚶♀️🚶♂️ Follow for more.   #tableau #data #analytics #VizoftheRay

🔍 Mastering SQL Subqueries for Advanced Data Insights 🔍 Subqueries in SQL—queries nested within other queries—are a powerful tool for anyone working with complex data. They allow you to build dynamic, layered data queries that help answer more detailed questions, like identifying specific groups, comparing metrics, or calculating summaries. 💻✨ 💡 Here’s a quick breakdown of SQL subquery types: Single-Row Subqueries: Useful for comparisons, like finding the max value. Multi-Row Subqueries: Great for when you need multiple results, often with IN or ALL operators. Correlated Subqueries: Allows inner queries to reference each row in the outer query, enabling row-by-row comparison. With SQL subqueries, data analysis becomes not only more efficient but also opens the door to deeper insights. Whether you’re filtering, aggregating, or performing nested comparisons, subqueries are essential for taking SQL to the next level! 📈 💬 Let’s connect and share tips for using SQL to get the most out of data! #SQL #DataAnalysis #Subqueries #TechSkills #DatabaseManagement #DataScience

❓ Have you ever dealt with "empty" VARCHAR fields in an RDBMS?   ❌ Do you realize that they are (sometimes) NOT the same thing as null?   ⚠ This once caused me a problem in Tableau.   👀 In TERADATA, a VARCHAR field can have a value of '' (single quotes), meaning empty.   ❌ And that is not the same thing as null.   You *can* JOIN one table to another on empty in TERADATA - an equivalency check will work.   However, Tableau will not blend one table to another on an empty string; it seems to treat it more like a null.   🔎 Check your own RDBMS to see how it handles empty string data - it's not consistent across systems.   😱 This caused a blank graph in Tableau, but one solution is to create a new variable (in ETL or within Tableau) based on that possibly-empty field, check for the empty (with = '') and convert it to some text literal.   ❓ Have you struggled with empty dimensions?   🏗→🧠 Build to Learn!   💭🚶♀️🚶♂️ Follow for more.   #tableau #data #analytics #VizoftheRay

🔀 Mastering SQL Joins: The Key to Effective Data Analysis! 📈 ✒️ In SQL, Joins are essential tools for working with data across multiple tables. Whether you're handling small datasets or large-scale databases, knowing when and how to use different types of joins can make your queries more efficient and insightful. ⏺️ Here’s a quick guide to the most common SQL joins: 1️⃣ INNER JOIN – Returns only matching rows from both tables. 2️⃣ LEFT JOIN – Returns all rows from the left table, with matching rows from the right table. Non-matches are filled with NULL. 3️⃣ RIGHT JOIN – Returns all rows from the right table, with matching rows from the left table. 4️⃣ FULL OUTER JOIN – Combines rows from both tables, filling in NULL where there’s no match. 5️⃣ CROSS JOIN – Returns the Cartesian product of both tables. 6️⃣ SELF JOIN – Joins a table with itself, useful for hierarchical data. 🛠️ Understanding and mastering these SQL joins will give you the power to effectively query your data and generate meaningful insights. 💬 Question: What’s your go-to SQL Join? Share below! 👇 #SQL #DataAnalysis #SQLJoins #DataScience #DataEngineering #BusinessIntelligence#DataStorytelling #CareerInData #PredictiveAnalytics #SQL #DataAnalysis#DataAnalyst #DataCommunity #DataForGood #BigData #RealTimeAnalytics

🎯 Day 25/75 of #DataScience Challenge: "Views and Their Applications in Data Analysis." ✨ "Unlocking data insights with views! 🔓 Today, I explored views and their applications in data analysis: What are Views? A view is a virtual table based on the result-set of a SQL statement 📝. Types of Views: 🗂 ▪️ Simple View: Based on a single table 📄 ▪️ Complex View: Based on multiple tables 📈 ▪️ Materialized View: Physical copy of the view 💻 Applications of Views: 🎯 1. Data Abstraction 🔒 2. Data Filtering 🔄 3. Data Transformation 🔮 4. Data Security 🔒 5. Simplifying Complex Queries 📈 Benefits of Views: ▪️ Improved Data Organization ▪️ Enhanced Data Security ▪️ Reduced Query Complexity ▪️ Better Data Reusability ▪️ Faster Data Retrieval Common Use Cases: 📊 ▪️ Data Summarization ▪️ Data Aggregation ▪️ Data Visualization ▪️ Reporting and Analytics ▪️ Business Intelligence Stay tuned for more Updates! 🚀 #entri_elevate #Dr_JithaPnair #DataScience #DataAnalysis #DataAnalytics #DataDriven #DataInsights #Analytics #75DaysOfDataAnalysis #DataChallenge #LearningDataScience #DataScienceJourney #MySQL #SQLQueries #DataScienceCareer #CareerDevelopment #DataScienceSkills

Day 17/75: Data Analysis Challenge Today, I dove into joining tables in MySQL. I learned how to combine data from multiple tables using INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN. #DataScience #DataAnalysis #DataAnalytics #BigData #DataVisualization #DataDriven #DataInsights #Analytics #75DaysOfDataAnalysis #DataChallenge #LearningDataScience #DataScienceJourney #SQL #SQLServer #SQLQueries #SQLProgramming #SQLDevelopment #SQLLearning #DataScienceCareer #DataJobs #CareerDevelopment #JobSearch #DataScienceSkills #75DayDataAnalysisChallenge #entri_elevate #dr_jitha_p_nair

🔍 The Hidden Power of SQL JOINS: Are You Using Them Effectively? 🔗 SQL JOINS are the backbone of relational databases, yet many data analysts overlook their full potential. Here are 4 types of JOINS you need to master to unlock deeper insights from your data: 1️⃣ INNER JOIN: Combines records from two tables where there’s a match in both. Perfect for finding intersecting data. 🔄 2️⃣ LEFT JOIN: Returns all records from the left table and matched records from the right. Use this when you need all the details from one table. 👀 3️⃣ RIGHT JOIN: Opposite of LEFT JOIN, giving you all the records from the right table. Often used when tracking historical data. 📚 4️⃣ FULL OUTER JOIN: Combines records when there’s a match in either table. Great for merging datasets with missing information on both sides. 🔄➖🔄 Mastering JOINS will take your data querying skills to the next level! 💡 Which JOIN do you find most useful? Let’s discuss! 👇 #SQL #DataAnalysis #Databases #TechSkills #DataScience #SQLQueries #BusinessIntelligence

🌟🌟🌟Window Function🌟🌟🌟 In SQL, window functions are powerful for performing operations on sets of rows relative to each row. These functions allow calculations across partitions of data, giving insights into things like rankings, cumulative sums, moving averages, and more. Let’s break down the syntax and see some examples of commonly used SQL window functions. Basic Syntax of a Window Function : window_function(column_name) OVER (PARTITION BY partition_column ORDER BY order_column ROWS/RANGE BETWEEN <frame_start> AND <frame_end>) window_function: The actual function you’re applying, such as SUM, AVG, ROW_NUMBER, etc. column_name: The column to which the function applies. PARTITION BY: (Optional) Divides the data into partitions where the window function is applied separately. ORDER BY: Specifies the order of rows within each partition. ROWS/RANGE BETWEEN: (Optional) Defines the specific range of rows for the window frame, allowing you to limit the calculation to, for example, the current row and the previous . 1. ROW_NUMBER() assigns a unique row number to each row within a partition. 2. RANK() and DENSE_RANK(): Ranking Rows with Ties 3. SUM(): Calculating a Cumulative Sum 4. LAG() and LEAD() 5. AVG() with ROWS/RANGE for Moving Averages Follow Shubham Kumar 🎯 for more content. #Data # Data Engineering #SQL #Big Data #Data Analysis

💥75 DAYS CHALLENGE -- DATA SCIENCE DAY 25/75 MySQL--BASICS 🔰 VIEWS AND THEIR APPLICATIONS IN MySQL Views in SQL are virtual tables that represent the result of a query. They are powerful tools in data analysis for several reasons: 🔎 Simplification of Complex Queries: Views can encapsulate complex queries, making it easier to reuse and manage them. Instead of writing a complex query multiple times, you can create a view and query it as if it were a table. 🔎 Data Security: Views can restrict access to specific rows and columns of data, enhancing security. By granting access to a view rather than the underlying table, you can control what data users can see and manipulate. 🔎 Data Abstraction: Views provide a level of abstraction, allowing users to interact with data without needing to understand the underlying table structures. This is particularly useful in large databases with complex schemas. 🔎 Data Aggregation: Views can be used to aggregate data, such as summing sales figures or averaging scores, which is useful for reporting and analysis. 🔎 Real-time Data Analysis: Since views are dynamically generated, they always reflect the current state of the data. This is beneficial for real-time data analysis and reporting. #75_Days_Challenge #drjithapnair #excel #data_science #DataScience #DataAnalysis #DataAnalytics #BigData #DataVisualization #DataDriven #DataInsights #Analytics #entri_elevate #75DaysOfDataAnalysisChallenge Dr.Jitha P Nair

Blog alert! Where the reverse function in #SynapseAnalytics works differently from #SqlServer #Data #Analytics #Query #SQL #MVPBuzz #MCTBuzz