MJ
María José F.Verified purchase
Your cart is empty
Add prompt packs to continue
Copy, paste and use them in your favorite AI:
Just $0.08 per prompt · one-time payment
100 resources included
This definitive collection for Data Analysts has been designed as the master resource for professionals seeking to master the entire data lifecycle, from raw ingestion to strategic value delivery. Each section addresses real technical challenges in today's market, providing precise frameworks to optimize highly complex SQL queries, design robust data architectures under the Kimball methodology, and run advanced statistical analyzes with Python. By integrating this repository of prompts into their workflow, the analyst not only automates repetitive cleaning and ETL tasks, but also increases their storytelling capacity, translating complex metrics into actionable business decisions. It is the essential tool to ensure technical accuracy in production environments and excel in senior selection processes through expert resolution of case studies and architectural challenges.
Acts as a Senior Data Architect and SQL Performance Tuning Specialist with experience in high-concurrency environments. Your goal is to perform a thorough audit and technical refactoring of a complex query that uses multiple Common Table Expressions (CTEs). The focus should be on transforming the [ORIGINAL_SQL_CODE] code to maximize execution speed, reduce I/O cost, and improve RAM usage efficiency within the [DATABASE_ENGINE] engine. Carefully analyze the chain of dependencies between the provided CTEs. Identifies specific bottlenecks, such as redundancy in base table scanning, failure to materialize intermediate results that are queried multiple times, and lack of 'Predicate Pushdown'. Evaluates whether the current logic allows the query optimizer to generate an efficient execution plan or whether, on the contrary, excessive use of CTEs is forcing unnecessary spooling in the [WORK_ENVIRONMENT] database. Proceed to generate an optimized version of the code by applying, as most efficient for this case, the following strategies: 1) Conversion of critical CTEs into Temporary Tables with specific indexes to improve subsequent JOINs. 2) Restructuring the filtering logic to ensure that WHEREs are applied at the earliest possible stage. 3) Use Window Functions instead of complex self-joins if logic allows. 4) Applying materialization hints (such as MATERIALIZED in PostgreSQL or equivalent techniques in other engines) to avoid re-evaluating costly subqueries. The final deliverable must include three mandatory sections: First, the SQL code refactored and formatted under professional standards. Secondly, a detailed technical explanation of each change made, justifying why the new structure is superior in terms of latency and throughput for a data volume of [DATA_VOLUME]. Finally, a list of indexing recommendations (cluster and non-cluster) that complement the new query structure to ensure optimal long-term performance. If any key information needed to fill the bracketed fields is missing, ask me the necessary questions before answering.
Instant access after purchase from your dashboard. Just copy and paste into your AI.
ChatGPT, Claude, Gemini, DeepSeek, Grok, Qwen and any AI chat.
Yes. Every prompt includes bracketed fields where you insert your own information, context and specifics, so they fit your situation, country or industry.
Yes. Above you can read full sample prompts, exactly as you'll receive them, to check the quality before paying.
Yes. Pay once and they're yours forever, updates included.
Acts as a Senior Data Architect and SQL optimization expert for the [Motor_DB] database engine. Your mission is to design, develop and optimize a complex query based on Recursive Common Table Expressions (CTEs) to address a hierarchical data structure in table [Nombre_Tabla]. The problem focuses on a [Tipo_de_Estructura: ej. organigrama, lista de materiales BOM o red de transporte] model, where the relationship is established between the primary column [Columna_ID] and the hierarchical reference column [Columna_Parent_ID]. The solution must be able to process thousands of records while maintaining optimal execution time and avoiding excessive consumption of memory resources. The design should start by setting the 'Anchor Member' filtered by [Condicion_Inicial], followed by a 'Recursive Member' that performs the logical join efficiently. It is mandatory that you implement depth control mechanisms using a virtual 'Level' column and that you include Cycle Detection logic to prevent infinite loops in case circular references exist in [Nombre_Tabla]. Additionally, it integrates the cumulative or aggregate calculation of the [Metrica_a_Calcular] metric as the query navigates the different branches of the hierarchy, ensuring that data inheritance is consistent and accurate. In the optimization phase, you analyze how the [Motor_DB] execution plan would treat this CTE. Suggests creating specific indexes or using temporary tables if the recursion is too deep for the current memory stack. Evaluate whether using operators like UNION vs UNION ALL affects deduplication and overall performance. Also provide a strategy to limit maximum recursion (such as OPTION MAXRECURSION in SQL Server or session variables in other engines) based on the [Limite_Profundidad] parameters. Generates the final SQL code, perfectly indented and commented, that resolves the proposed business case. It includes an explanatory section that details the operation of each part of the CTE and ends with three expert recommendations for 'Fine-Tuning' performance in high-concurrency environments or [Entorno_Produccion] type production environments. The ultimate goal is that any Data Analyst can run this query to get a clear view of the entire hierarchy and its aggregated values. If any key information needed to fill the bracketed fields is missing, ask me the necessary questions before answering.
He acts as a Data Architect and SQL Performance Tuning Specialist with over 15 years of experience optimizing petabyte-scale data warehouses. Your mission is to analyze, diagnose and refactor an SQL query that has severe latency and resource consumption issues due to complex joins between large tables in the [Motor_DB] environment. First, it performs a thorough analysis of the given query: [Query_Original]. Identifies possible causes of inefficiency, such as Data Skew, accidental Cartesian products, Spilling to disk, or inefficient Nested Loop Joins on tables that lack proper indexes. Evaluate whether the order of the joins is optimal based on the cardinality of the tables involved and the use of filtering predicates. Propose an advanced optimization strategy that includes, if applicable, the use of Broadcast Joins for small versus large tables, Hash Join techniques, or implementing dynamic partitioning and clustering. Consider replacing CTEs (Common Table Expressions) with temporary tables with indexes if the [Motor_DB] engine does not materialize CTEs efficiently, and apply 'Filter Pushdown' to reduce the data volume before performing join operations. Generates the optimized version of the SQL code, commenting in detail on each change made and justifying the technical improvement. Additionally, it provides guidance on creating recommended indexes or distribution keys for tables: [Estructura_Tablas]. Finally, include a validation checklist to compare the 'Execution Plan' before and after optimization, focusing on I/O metrics, CPU usage and memory. The result must be structured in four sections: 1. Bottleneck Diagnosis, 2. Refactoring Strategy, 3. Optimized SQL Code, and 4. Infrastructure/Indexing Recommendations. Make sure the solutions are scalable for a data volume of approximately [Volumen_Datos] and specifically solve the [Cuello_Botella] problem. If any key information needed to fill the bracketed fields is missing, ask me the necessary questions before answering.
It's a master instruction, optimized for AI.
Prompt
your instruction
AI
Result
Based on 6 reviews
I was impressed by the quality. The quality of the answers I get improved a lot. One hundred percent recommended.
Worth every penny. The prompts are really well thought out and the effort shows. Already recommended them to my team.
Delivers what it promises. The organization helps you get oriented fast. I'd buy again.
Exactly what I was looking for. The quality of the answers I get improved a lot. Totally recommend them.
I was impressed by the quality. They work just as well in ChatGPT and Claude. An investment that pays for itself.
I was impressed by the quality. The quality of the answers I get improved a lot. One hundred percent recommended.