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You're mentoring a junior engineer in database engineering. What are some common mistakes you might make?

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Database engineering is a complex and rewarding skill that requires attention to detail, logical thinking, and best practices. As a mentor, you want to help your junior engineer avoid some of the common pitfalls that can compromise the quality, performance, and security of your data. Here are some of the mistakes you might make and how to prevent them.

本文章的要点总结

Emphasize hands-on practice:

Ensure your mentee gets plenty of real-world experience. This builds competence and confidence, making theoretical knowledge practical and tangible.
Encourage thorough testing:

Implementing comprehensive testing with actual business data can reduce post-deployment issues, ensuring the database performs reliably under real conditions.

本摘要由 AI 和以下专家提供支持

Syed Gazanfar

DevOps Engineer @ PrismX | AWS Cloud…
Suresh Jami

Experienced Technology Leader Driving…

1 Not normalizing data

Normalization is the process of organizing your data into tables and columns that minimize redundancy and dependency. It helps you avoid data inconsistency, duplication, and update anomalies. A normalized database also improves query efficiency, data integrity, and scalability. You should follow the rules of normalization, such as ensuring each table has a primary key, eliminating partial and transitive dependencies, and avoiding repeating groups.

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2 Not using indexes

Indexes are data structures that speed up the retrieval of data from a database. They act like pointers that help the database engine find the rows that match your query criteria. Without indexes, the database engine has to scan the entire table, which can be slow and resource-intensive. You should use indexes on columns that are frequently searched, filtered, sorted, or joined. However, you should also avoid over-indexing, as it can increase the storage space and the time required to insert, update, or delete data.

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Federico Razzoli

Vettabase Founder | Database Automation & Services
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Indexes must be created to solve problems. For every query there is a perfect index, but without it, will the query use another existing index? Is it fast enough? If so, creating a new index to slightly improve a fast query is a bad idea. Analyse query time statistics. Find slow queries, and queries that are executed most often. Those need to be optimised. But every index should bring a valuable benefit, not one that is irrelevant.

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Khaled M.

Opinions are personal
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Learn what is the difference between primary and secondary indexes. Also, index scan, full table scan and bitmapped index scan.

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Miriam F. Alfonso

Cloud Engineer | Azure Expert | Database Administrator | Driving Performance and Reliability in Cloud Solutions
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For many years, I have focused on optimizing databases. I believe that indexes can either significantly enhance your database’s performance or lead them to disruptions. When I get notified that the database is slow, I analyze the query type and verify that no indexes are missing. However, I also consider that the overuse of index creation can be catastrophic.

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Eduardo Bona

Cofundador @ Vivaworks || CPTO @ Vivaintra || Vivaweber && Professor Universitário
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Assim como entender se uma tabela passa por mais escritas ou consultas para uma estratégia adequada, a cria??o de índices passa inicialmente por um feeling no projeto do banco e dados e posteriormente por um monitoramento e performance. é fundamental entender isso, saber avaliar e n?o cometer o erro da falta de índices necessários ou cria??o de índices desnecessários que impactam as consultas.

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3 Not securing data

Security is a crucial aspect of database engineering, as you want to protect your data from unauthorized access, modification, or deletion. You should implement security measures such as encrypting sensitive data, using strong passwords, limiting user privileges, and auditing data access. You should also follow the principle of least privilege, which means granting users only the minimum permissions they need to perform their tasks. Additionally, you should backup your data regularly and test your recovery plan.

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4 Not documenting data

Documentation is the process of creating and maintaining records that describe your data, such as its structure, meaning, source, quality, and usage. Documentation helps you and other users understand your data, track its changes, and ensure its consistency and accuracy. You should document your data using tools such as data dictionaries, metadata repositories, entity-relationship diagrams, and data lineage diagrams. You should also update your documentation whenever you modify your data.

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Syed Gazanfar

DevOps Engineer @ PrismX | AWS Cloud, Azure Cloud |Project Management
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Common mentoring mistakes in database engineering: 1. Assuming prior knowledge 2. Overloading with information 3. Lack of hands-on experience 4. Neglecting data integrity/security emphasis 5. Not encouraging questions

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Federico Razzoli

Vettabase Founder | Database Automation & Services
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The first documentation tool is naming. Whenever someone needs further documentation, there is probably a bad name. Make names concise but also descriptive and consistent. The next tool is comments. Columns, tables, schemas and everything else can have an associated comment. For specific doubts, one should not have to look farther than that. Generic and approvimated ER diagrams can be useful where the schema is complex enough. But trying to include every column or index is a waste of time. A week after you complete the diagram, it will be obsolete. Dictionaries can be very useful. Especially if different departments use different terminology and communications are misinterpreted. In that case, make multiple dictionaries.

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Kevin Underwood

Financial Application Developer
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I employ camel case in my names for readability. For example: GetInvoicesFromOcrProcessor. Some like to see the first character in lowercase but i never fully embraced that pattern.

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5 Not testing data

Testing is the process of verifying that your data meets the specifications and requirements of your project. Testing helps you identify and correct errors, bugs, or anomalies in your data, such as incorrect formats, missing values, invalid constraints, or inconsistent results. You should test your data using methods such as unit testing, integration testing, regression testing, and performance testing. You should also use tools such as validators, debuggers, and analyzers to automate and simplify your testing process.

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Suresh Jami

Experienced Technology Leader Driving Innovation, Delivering Results, Building High-Performing Teams and Transforming Talent
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Full volume testing with business data is missed in many projects due to complexity in handling large volumes as well phi data. But lot of production issues does occur because of data

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6 Not optimizing data

Optimization is the process of improving the efficiency and performance of your data and queries. Optimization helps you reduce the processing time, resource consumption, and cost of your data operations. You should optimize your data by using techniques such as partitioning, compression, caching, and archiving. You should also optimize your queries by using techniques such as avoiding subqueries, using joins instead of unions, and applying filters and limits.

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Eduardo Bona

Cofundador @ Vivaworks || CPTO @ Vivaintra || Vivaweber && Professor Universitário
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N?o entender ou n?o se preocupar com isso custará a médio prazo o escalonamento vertical da infraestrutura do banco de dados e isso envolverá custo e desperdício de recursos.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Eduardo Bona

Cofundador @ Vivaworks || CPTO @ Vivaintra || Vivaweber && Professor Universitário
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Atualmente ferramentas automatizadas, muitas delas disponíveis na própria nuvem provedora, se habilitadas corretamente, fornecem dados referentes à insights sobre uso e performance do banco de dados, consultas lentas, índices, etc. Conhecer tais ferramentas e fazer uso delas podem economizar horas e mais horas de trabalho de um DBA.

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Marcus Aurélius Minervino Júnior
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Estudar primeiro o básico : hardware, sistemas operacionais, sistemas de arquivos. teoria dos conhuntos, programa??o, história da computa??o. Isso dará contexto e estrutura. Depois, estudar, arquitetura básica de banco de dados: arquivos, blocos, extens?es, transa??es, áreas de memória, processos. SQL básico e avan?ado, paralelismo, bancos de dados NOSQL e Tuning.

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You're mentoring a junior engineer in database engineering. What are some common mistakes you might make?

1

2

3

4

5

6

7

1 Not normalizing data

2 Not using indexes

3 Not securing data

4 Not documenting data

5 Not testing data

6 Not optimizing data

7 Here’s what else to consider

Database Engineering

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