#SQL Engineer

[technicalfika]

Unlocking Your Future: How to Become an SQL Engineer at Top Tech Companies

As a college student with aspirations of landing your dream job at a top tech giant company, becoming an SQL Engineer can open the doors to a data-driven and fulfilling career. SQL (Structured Query Language) is an essential skill in the tech industry, enabling professionals to manage and analyze vast amounts of data efficiently. In this comprehensive guide, we will explore the key steps to…

[mechanical-moron]

Programmers, Web designers, game developers, anyone else who does stuff with numbers on a computer screen.....curious to know if you guys ever dream in code, and if so, do you like it? I for one do not find it to be particularly enjoyable but want to hear what others have to say lol.

[scvnthorpe]

That time I restored a Database view

Recently at work we've been migrating an old database system to a new platform to save money - this kind of shit is what makes your business processes faster, cheaper and more correct - and this has entailed sifting through a lot of tables, views, and views made of tables and views!

As it happens the finance guy who does all the payroll and expenses is a great guy to work with and basically the one person who knows all the relevant business rules, but also basically treats databases like they're excel workbooks. As such you have a bunch of bits stitched to each other and we're just figuring out how to first move everything and then ease into a well-oiled relational model with no duplication and all together on a single database.

While we in the dev team were figuring out how to do this for finance we were recently testing out a modified version of a view built on top of the old version and accidentally deleted the old version and not the modified testing version.

Mistakes are bound to happen, but we needed to figure out how to either restore it or at least figure out how to work without it because finance people love their data views and reports. There are probably clever things you can do with any DBMS to find shit you just dropped and restore it from backup, but I then realised that I'd been tasked with generating all the scripts for the database objects. There had to be a script laying around!

Sure enough I went to dig up the build script for the dropped view, and I ran it.

I queried it, and everything was back in place.

Shit goes wrong sometimes, but having the right failsafes can really make a difference.

Script your shit, use backups, use version control!

[arshikasingh]

SQL Interview Questions

The following SQL interview questions and answers are designed to familiarize candidates with common interview questions.

[qhsetools2022]

Cloud Data Engineer SQL Python | Devoteam Maroc Nearshore

Job title: Cloud Data Engineer SQL Python | Devoteam Maroc Nearshore Company: Devoteam Job description: compréhension et maintenabilité Mettre en place les tests unitaires et d’intégration pour assurer la qualité du code et déboguer… Quels atouts pour rejoindre l’équipe ? Diplôme d’ingénieur ou équivalent Expert dans le domaine de la Data : 3 à 5 ans… Expected salary: Location: Rabat Job date:…

[projectchampionz]

SUSTAINABLE PRACTICES AND TOURISM DEVELOPMENT AT THE NATIONAL MUSEUM IBADAN AS A STUDY AREA

SUSTAINABLE PRACTICES AND TOURISM DEVELOPMENT AT THE NATIONAL MUSEUM IBADAN AS A STUDY AREA ABSTRACT This research explores the role of sustainable practices in tourism development, with a focus on the National Museum Ibadan, Nigeria. The study investigates the current sustainable practices at the museum, their impact on tourism development, the challenges faced in integrating sustainability, and…

[teklink]

Understanding SQL Query Execution: A Data Engineer’s Guide

As data engineers, we work with SQL daily, but how many of us truly understand the inner workings of a SQL query? Knowing the order of execution can significantly improve the way you write SQL queries. Let’s dive into the process with a practical example.

For more information, visit Teklink International LLC

[m-school0730]

高還元SESとうわさの『株式会社中野エージェント』の実態をまとめました。

[gradious-12]

What is SQL and why is it matters?

SQL (Structured Query Language) is the standard language used for managing and manipulating relational databases. It enables tasks such as retrieving data, updating or deleting records, and modifying database structures. SQL is widely supported across various database systems like MySQL, PostgreSQL, Oracle, and Microsoft SQL Server.

Why is SQL Important?

Data Management: SQL is vital for handling data, which is the backbone of any organization. It allows efficient storage, retrieval, and updating of data across databases. Whether retrieving customer data for a marketing campaign or updating employee records, SQL simplifies these operations.

Universality: Despite slight differences between database systems, SQL remains the universal language for relational databases. Once mastered, it can be applied to platforms like MySQL, Oracle, and PostgreSQL, offering flexibility across different environments.

Handling Large Data Sets: In today's data-driven world, businesses manage vast amounts of information. SQL enables efficient querying and manipulation of large datasets, helping users analyze trends, aggregate sales data, and generate reports.

Data Integrity and Security: SQL ensures data integrity with ACID (Atomicity, Consistency, Isolation, Durability) properties and offers powerful control over user access and permissions, making it essential for secure database management.

Cross-Industry Usage: SQL is used in various industries, including finance, healthcare, retail, and technology. Professionals like data analysts, developers, and system administrators rely on SQL to manage data effectively.

How Gradious Supports SQL and Database Management

Gradious Technologies offers comprehensive IT courses that equip learners with essential SQL and database management skills. Their Full Stack JS and DevOps courses also provide in-depth SQL training, focusing on real-world applications. Gradious helps students gain hands-on experience, learning how SQL integrates with backend development, infrastructure management, and DevOps practices.

Whether you're a beginner or looking to advance your SQL skills, Gradious provides industry-relevant training, helping you excel in:

Writing SQL Queries: Master data retrieval, updates, and complex operations like joins and subqueries.

Database Design and Optimization: Learn to design efficient schemas, normalize data, and optimize query performance.

Data Security and Integrity: Implement security measures, manage permissions, and ensure data consistency.

Integration with Modern Technologies: Discover how SQL interacts with tools and frameworks in full-stack and DevOps environments.

With Gradious, you'll not only master SQL but also develop a deep understanding of its role in modern tech ecosystems, setting you up for a successful IT career.

[intelliatech]

Top 10 ChatGPT Prompts For Software Developers

ChatGPT can do a lot more than just code creation and this blog post is going to be all about that. We have curated a list of ChatGPT prompts that will help software developers with their everyday tasks. ChatGPT can respond to questions and can compose codes making it a very helpful tool for software engineers.

While this AI tool can help developers with the entire SDLC (Software Development Lifecycle), it is important to understand how to use the prompts effectively for different needs.

Prompt engineering gives users accurate results. Since ChatGPT accepts prompts, we receive more precise answers. But a lot depends on how these prompts are formulated. 

To Get The Best Out Of ChatGPT, Your Prompts Should Be:

Clear and well-defined. The more detailed your prompts, the better suggestions you will receive from ChatGPT.

Specify the functionality and programming language. Not specifying what you exactly need might not give you the desired results.

Phrase your prompts in a natural language, as if asking someone for help. This will make ChatGPT understand your problem better and give more relevant outputs.

Avoid unnecessary information and ambiguity. Keep it not only to the point but also inclusive of all important details.

Top ChatGPT Prompts For Software Developers

Let’s quickly have a look at some of the best ChatGPT prompts to assist you with various stages of your Software development lifecycle.

1. For Practicing SQL Commands;

2. For Becoming A Programming Language Interpreter;

3. For Creating Regular Expressions Since They Help In Managing, Locating, And Matching Text.

4. For Generating Architectural Diagrams For Your Software Requirements.

Prompt Examples: I want you to act as a Graphviz DOT generator, an expert to create meaningful diagrams. The diagram should have at least n nodes (I specify n in my input by writing [n], 10 being the default value) and to be an accurate and complex representation of the given input. Each node is indexed by a number to reduce the size of the output, should not include any styling, and with layout=neato, overlap=false, node [shape=rectangle] as parameters. The code should be valid, bugless and returned on a single line, without any explanation. Provide a clear and organized diagram, the relationships between the nodes have to make sense for an expert of that input. My first diagram is: “The water cycle [8]”.  

5. For Solving Git Problems And Getting Guidance On Overcoming Them.

Prompt Examples: “Explain how to resolve this Git merge conflict: [conflict details].” 6. For Code generation- ChatGPT can help generate a code based on descriptions given by you. It can write pieces of codes based on the requirements given in the input. Prompt Examples: -Write a program/function to {explain functionality} in {programming language} -Create a code snippet for checking if a file exists in Python. -Create a function that merges two lists into a dictionary in JavaScript.  

7. For Code Review And Debugging: ChatGPT Can Review Your Code Snippet And Also Share Bugs.

Prompt Examples: -Here’s a C# code snippet. The function is supposed to return the maximum value from the given list, but it’s not returning the expected output. Can you identify the problem? [Enter your code here] -Can you help me debug this error message from my C# program: [error message] -Help me debug this Python script that processes a list of objects and suggests possible fixes. [Enter your code here]

8. For Knowing The Coding Best Practices And Principles: It Is Very Important To Be Updated With Industry’s Best Practices In Coding. This Helps To Maintain The Codebase When The Organization Grows.

Prompt Examples: -What are some common mistakes to avoid when writing code? -What are the best practices for security testing? -Show me best practices for writing {concept or function} in {programming language}.  

9. For Code Optimization: ChatGPT Can Help Optimize The Code And Enhance Its Readability And Performance To Make It Look More Efficient.

Prompt Examples: -Optimize the following {programming language} code which {explain the functioning}: {code snippet} -Suggest improvements to optimize this C# function: [code snippet] -What are some strategies for reducing memory usage and optimizing data structures? 

10. For Creating Boilerplate Code: ChatGPT Can Help In Boilerplate Code Generation.

Prompt Examples: -Create a basic Java Spring Boot application boilerplate code. -Create a basic Python class boilerplate code

11. For Bug Fixes: Using ChatGPT Helps Fixing The Bugs Thus Saving A Large Chunk Of Time In Software Development And Also Increasing Productivity.

Prompt Examples: -How do I fix the following {programming language} code which {explain the functioning}? {code snippet} -Can you generate a bug report? -Find bugs in the following JavaScript code: (enter code)  

12. Code Refactoring- ChatGPt Can Refactor The Code And Reduce Errors To Enhance Code Efficiency, Thus Making It Easier To Modify In The Future.

Prompt Examples –What are some techniques for refactoring code to improve code reuse and promote the use of design patterns? -I have duplicate code in my project. How can I refactor it to eliminate redundancy?  

13. For Choosing Deployment Strategies- ChatGPT Can Suggest Deployment Strategies Best Suited For A Particular Project And To Ensure That It Runs Smoothly.

Prompt Examples -What are the best deployment strategies for this software project? {explain the project} -What are the best practices for version control and release management?  

14. For Creating Unit Tests- ChatGPT Can Write Test Cases For You

Prompt Examples: -How does test-driven development help improve code quality? -What are some best practices for implementing test-driven development in a project? These were some prompt examples for you that we sourced on the basis of different requirements a developer can have. So whether you have to generate a code or understand a concept, ChatGPT can really make a developer’s life by doing a lot of tasks. However, it certainly comes with its own set of challenges and cannot always be completely correct. So it is advisable to cross-check the responses. Hope this helps. Visit us- Intelliatech

[wait4tech]

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[ibarrau]

[Python] PySpark to M, SQL or Pandas

Hace tiempo escribí un artículo sobre como escribir en pandas algunos códigos de referencia de SQL o M (power query). Si bien en su momento fue de gran utilidad, lo cierto es que hoy existe otro lenguaje que representa un fuerte pie en el análisis de datos.

Spark se convirtió en el jugar principal para lectura de datos en Lakes. Aunque sea cierto que existe SparkSQL, no quise dejar de traer estas analogías de código entre PySpark, M, SQL y Pandas para quienes estén familiarizados con un lenguaje, puedan ver como realizar una acción con el otro.

Lo primero es ponernos de acuerdo en la lectura del post.

Power Query corre en capas. Cada linea llama a la anterior (que devuelve una tabla) generando esta perspectiva o visión en capas. Por ello cuando leamos en el código #“Paso anterior” hablamos de una tabla.

En Python, asumiremos a "df" como un pandas dataframe (pandas.DataFrame) ya cargado y a "spark_frame" a un frame de pyspark cargado (spark.read)

Conozcamos los ejemplos que serán listados en el siguiente orden: SQL, PySpark, Pandas, Power Query.

En SQL:

SELECT TOP 5 * FROM table

En PySpark

spark_frame.limit(5)

En Pandas:

df.head()

En Power Query:

Table.FirstN(#"Paso Anterior",5)

Contar filas

SELECT COUNT(*) FROM table1

spark_frame.count()

df.shape()

Table.RowCount(#"Paso Anterior")

Seleccionar filas

SELECT column1, column2 FROM table1

spark_frame.select("column1", "column2")

df[["column1", "column2"]]

#"Paso Anterior"[[Columna1],[Columna2]] O podría ser: Table.SelectColumns(#"Paso Anterior", {"Columna1", "Columna2"} )

Filtrar filas

SELECT column1, column2 FROM table1 WHERE column1 = 2

spark_frame.filter("column1 = 2") # OR spark_frame.filter(spark_frame['column1'] == 2)

df[['column1', 'column2']].loc[df['column1'] == 2]

Table.SelectRows(#"Paso Anterior", each [column1] == 2 )

Varios filtros de filas

SELECT * FROM table1 WHERE column1 > 1 AND column2 < 25

spark_frame.filter((spark_frame['column1'] > 1) & (spark_frame['column2'] < 25)) O con operadores OR y NOT spark_frame.filter((spark_frame['column1'] > 1) | ~(spark_frame['column2'] < 25))

df.loc[(df['column1'] > 1) & (df['column2'] < 25)] O con operadores OR y NOT df.loc[(df['column1'] > 1) | ~(df['column2'] < 25)]

Table.SelectRows(#"Paso Anterior", each [column1] > 1 and column2 < 25 ) O con operadores OR y NOT Table.SelectRows(#"Paso Anterior", each [column1] > 1 or not ([column1] < 25 ) )

Filtros con operadores complejos

SELECT * FROM table1 WHERE column1 BETWEEN 1 and 5 AND column2 IN (20,30,40,50) AND column3 LIKE '%arcelona%'

from pyspark.sql.functions import col spark_frame.filter( (col('column1').between(1, 5)) & (col('column2').isin(20, 30, 40, 50)) & (col('column3').like('%arcelona%')) ) # O spark_frame.where( (col('column1').between(1, 5)) & (col('column2').isin(20, 30, 40, 50)) & (col('column3').contains('arcelona')) )

df.loc[(df['colum1'].between(1,5)) & (df['column2'].isin([20,30,40,50])) & (df['column3'].str.contains('arcelona'))]

Table.SelectRows(#"Paso Anterior", each ([column1] > 1 and [column1] < 5) and List.Contains({20,30,40,50}, [column2]) and Text.Contains([column3], "arcelona") )

Join tables

SELECT t1.column1, t2.column1 FROM table1 t1 LEFT JOIN table2 t2 ON t1.column_id = t2.column_id

Sería correcto cambiar el alias de columnas de mismo nombre así:

spark_frame1.join(spark_frame2, spark_frame1["column_id"] == spark_frame2["column_id"], "left").select(spark_frame1["column1"].alias("column1_df1"), spark_frame2["column1"].alias("column1_df2"))

Hay dos funciones que pueden ayudarnos en este proceso merge y join.

df_joined = df1.merge(df2, left_on='lkey', right_on='rkey', how='left') df_joined = df1.join(df2, on='column_id', how='left')Luego seleccionamos dos columnas df_joined.loc[['column1_df1', 'column1_df2']]

En Power Query vamos a ir eligiendo una columna de antemano y luego añadiendo la segunda.

#"Origen" = #"Paso Anterior"[[column1_t1]] #"Paso Join" = Table.NestedJoin(#"Origen", {"column_t1_id"}, table2, {"column_t2_id"}, "Prefijo", JoinKind.LeftOuter) #"Expansion" = Table.ExpandTableColumn(#"Paso Join", "Prefijo", {"column1_t2"}, {"Prefijo_column1_t2"})

Group By

SELECT column1, count(*) FROM table1 GROUP BY column1

from pyspark.sql.functions import count spark_frame.groupBy("column1").agg(count("*").alias("count"))

df.groupby('column1')['column1'].count()

Table.Group(#"Paso Anterior", {"column1"}, {{"Alias de count", each Table.RowCount(_), type number}})

Filtrando un agrupado

SELECT store, sum(sales) FROM table1 GROUP BY store HAVING sum(sales) > 1000

from pyspark.sql.functions import sum as spark_sum spark_frame.groupBy("store").agg(spark_sum("sales").alias("total_sales")).filter("total_sales > 1000")

df_grouped = df.groupby('store')['sales'].sum() df_grouped.loc[df_grouped > 1000]

#”Grouping” = Table.Group(#"Paso Anterior", {"store"}, {{"Alias de sum", each List.Sum([sales]), type number}}) #"Final" = Table.SelectRows( #"Grouping" , each [Alias de sum] > 1000 )

Ordenar descendente por columna

SELECT * FROM table1 ORDER BY column1 DESC

spark_frame.orderBy("column1", ascending=False)

df.sort_values(by=['column1'], ascending=False)

Table.Sort(#"Paso Anterior",{{"column1", Order.Descending}})

Unir una tabla con otra de la misma característica

SELECT * FROM table1 UNION SELECT * FROM table2

spark_frame1.union(spark_frame2)

En Pandas tenemos dos opciones conocidas, la función append y concat.

df.append(df2) pd.concat([df1, df2])

Table.Combine({table1, table2})

Transformaciones

Las siguientes transformaciones son directamente entre PySpark, Pandas y Power Query puesto que no son tan comunes en un lenguaje de consulta como SQL. Puede que su resultado no sea idéntico pero si similar para el caso a resolver.

Analizar el contenido de una tabla

spark_frame.summary()

df.describe()

Table.Profile(#"Paso Anterior")

Chequear valores únicos de las columnas

spark_frame.groupBy("column1").count().show()

df.value_counts("columna1")

Table.Profile(#"Paso Anterior")[[Column],[DistinctCount]]

Generar Tabla de prueba con datos cargados a mano

spark_frame = spark.createDataFrame([(1, "Boris Yeltsin"), (2, "Mikhail Gorbachev")], inferSchema=True)

df = pd.DataFrame([[1,2],["Boris Yeltsin", "Mikhail Gorbachev"]], columns=["CustomerID", "Name"])

Table.FromRecords({[CustomerID = 1, Name = "Bob", Phone = "123-4567"]})

Quitar una columna

spark_frame.drop("column1")

df.drop(columns=['column1']) df.drop(['column1'], axis=1)

Table.RemoveColumns(#"Paso Anterior",{"column1"})

Aplicar transformaciones sobre una columna

spark_frame.withColumn("column1", col("column1") + 1)

df.apply(lambda x : x['column1'] + 1 , axis = 1)

Table.TransformColumns(#"Paso Anterior", {{"column1", each _ + 1, type number}})

Hemos terminado el largo camino de consultas y transformaciones que nos ayudarían a tener un mejor tiempo a puro código con PySpark, SQL, Pandas y Power Query para que conociendo uno sepamos usar el otro.

[gpsinfotechme-blog]

SQL With POWER BI

[qhsetools2022]

Cloud Data Engineer SQL Python | Devoteam Maroc Nearshore

Job title: Cloud Data Engineer SQL Python | Devoteam Maroc Nearshore Company: Devoteam Job description: compréhension et maintenabilité Mettre en place les tests unitaires et d’intégration pour assurer la qualité du code et déboguer… Quels atouts pour rejoindre l’équipe ? Diplôme d’ingénieur ou équivalent Expert dans le domaine de la Data : 3 à 5 ans… Expected salary: Location: Rabat Job date:…