How can you tune queries for a hybrid BI environment?

This underscores the crucial importance of comprehending the diverse aspects of data sources in a BI environment. Recognizing variations in data types, formats, and query languages, alongside considerations like latency and security, is essential for effective query tuning and overall system performance. it's useful to focus on: 1.Data Characteristics: Grasp the types, formats, and schemas of your data to navigate effectively. 1.Query Languages: Familiarize with the query languages your data sources use for precise querying. 3.Integration Considerations: Be aware of how data sources can be joined, filtered, or aggregated to ensure seamless integration. 4. Performance Factors: Understand the impact of latency, availability, and security.

Tuning queries in a hybrid environment requires a strategic approach. Firstly, optimize database configurations for on-premises and cloud components. Utilize indexing judiciously and ensure statistics are up-to-date. Leverage query caching to reduce redundant processing. Minimize data transfer between on-premises and cloud by pushing down filters and aggregations to the data source. Implement intelligent partitioning and utilize materialized views for performance gains. Monitor query performance continuously, using cloud-based tools to identify bottlenecks. Consider horizontal scaling in the cloud for improved parallel processing. Emphasize parameterized queries for adaptability.

Antes de "entender fontes de dados", você precisa saber "quais as fontes de dados?", para ter essa resposta, você precisará saber "quais os dados que ser?o utilizados?", para ter essa resposta, você precisará saber "quais as necessidades do cliente, onde dados podem apoiar?", para ter essa resposta, você precisar saber "Como os processos de negócios dessa área funcionam?". Perceba que há um "caminho" a se percorrer até "entender fontes de dados". E para você adentrar nesse caminho com seguran?a, você precisará das técnicas e processos da Engenharia de Requisitos, onde você, passo a passo, conseguirá mapear processos e requisitos de negócio, para aí sim, chegar nas fontes de dados. Blz? Dúvidas? Chamaí!

1. Organize Data Smartly 2. Make a Plan for Queries 3. Split Data into Parts 4. Reuse Information 5. Check and Improve Network Speed 6. Use the Right Storage 7. Create Ready-Made Views 8. Adjust Resources 9. Keep an Eye on Performance

Optimizing queries in a hybrid BI environment involves strategies such as understanding data sources, implementing indexing and partitioning, utilizing query optimization techniques, employing caching mechanisms, and leveraging parallel processing. Additionally, monitoring, load balancing, and regular performance reviews are essential. Optimizing data transfer, utilizing indexing views, and evaluating query plans contribute to efficient performance. Strategic use of BI tools compatible with on-premises and cloud sources ensures a streamlined and effective query experience.

When choosing tools for hybrid Business Intelligence (BI), consider factors like seamless data integration (e.g., Microsoft Power BI, Tableau), scalability (e.g., Qlik Sense, Google Data Studio), robust security (e.g., MicroStrategy, IBM Cognos), user-friendly interfaces (e.g., Tableau, Microsoft Power BI), customization options (e.g., Looker, Tableau), collaboration features (e.g., Microsoft Power BI, Tableau), and cost considerations (e.g., Tableau, Microsoft Power BI). Additionally, assess support systems and active user communities (e.g., Tableau, Microsoft Power BI). Tailor your choice based on organizational needs, budget, and infrastructure, and consider running pilot projects for a comprehensive evaluation.

Data Source Optimization: Understand the characteristics of each data source in the hybrid environment. Optimize queries based on the specific capabilities and limitations of each source. Query Distribution: Distribute queries strategically between on-premises and cloud data sources to minimize latency. Leverage caching mechanisms for frequently accessed data. Indexing and Partitioning: Ensure that tables are appropriately indexed and partitioned to speed up query execution. Utilize indexing strategies based on the types of queries commonly run in the hybrid environment. Query Complexity: Simplify and optimize the complexity of queries to reduce processing time. Minimize unnecessary joins and aggregate functions unless essential

Optimizing query logic is crucial for ensuring efficient query performance in a hybrid BI environment. This includes techniques such as using appropriate indexes, avoiding unnecessary joins, and minimizing data retrieval. Additionally, consider using prepared statements whenever possible to improve performance and reduce the risk of SQL injection attacks.

Optimizing is my personal go-to, especially since I am a bit of a CTE user in building queries and SPROCs in SQL. The faster we are able to pull the data from our databases the quicker it is to get said data to our co-workers.

Vamos lá....só desenvolvimento de consulta, se antes foi possível compreender necessidades e projetar o desenvolvimento. Certamente se você está cumprindo essas etapas, os riscos de problemas ser?o reduzidos. Georgia Polya, matemático, em 1945 escreveu um livro intitulado "How to solve it?", onde ele descreve 4 pilares para resolu??o de desafios: 1 - Compreens?o do desafio: Quais s?o os desafios? 2 - Planejamento: Como resolvo esses desafios? 3 - Execu??o: Qual caminho percorro para implantar a solu??o que resolve os desafios? 4 - Avalia??o de resultado: O desafio foi resolvido? O caminho percorrido foi o melhor? Livro Top! Barato! Recomendo a leitura. Tem edi??o em português: "A arte de resolver problemas" Dúvidas? Chamaí!

Sometimes it may be good to separate your query into multiple real tables, which will be joined together later to generate your final table, because writing multiple sub queries can be more expensive and complicated to understand. Plus during the data errors you can always check these little tables to understand from where it started. And I wouldn’t put hints as the last one, because they can significantly improve execution plan!

Acredito que otimizar a lógica de consulta é uma prática crucial no desenvolvimento de sistemas de banco de dados, especialmente em ambientes onde o desempenho é crítico. Alguns pontos para se levar em considera??o s?o: -Eficiência do Tempo de Execu??o: Uma lógica de consulta otimizada visa reduzir o tempo de execu??o das consultas. -índices e Estatísticas: Certifique-se de que as tabelas envolvidas nas consultas têm índices apropriados. -Evite Consultas Redundantes: Se uma mesma consulta é executada frequentemente, considere armazenar o resultado em cache ou criar vis?es materializadas para evitar recálculos desnecessários. -Uso Eficiente de Joins: Evite usar muitos joins se n?o forem necessários.

To monitor and test your SQL query performance effectively, follow these key steps: Query Profiling: Use query profiling tools to analyze the execution plan and identify bottlenecks in your SQL queries. Execution Time Analysis: Measure the execution time of your queries to pinpoint which queries are taking the most time. Index Usage: Ensure that indexes are being utilized efficiently by your queries, and consider adding indexes for frequently queried columns. Database Statistics: Monitor database statistics to understand the distribution of data, helping in query optimization decisions. Database Health Checks

Actually, deep understanding of how execution plan is built and good sql development tool will do the job here. After learning that it becomes a good manner for you to always check your query before executing, to see the plan and costs. But my experience can be one sided here, because Oracle Pl SQL developer has good UI and tools to measure everything fast

Monitoramento e busca de melhorias é constante, principalmente quando nos referimos a um requisito n?o funcional implícito, ou seja, desempenho na execu??o de consultas. é premissa que as consultas sejam executadas em alto desempenho, logo uma vez que, na etapa de desenvolvimento, você realizou testes unitários, integrados, de sistema e de aceite, todos eles apresentando desempenho bom, isso quer dizer que já pode ser colocada em produ??o, mas isso n?o é garantia que sempre será bom, por isso é preciso monitorar para garantir sempre um alto desempenho. Dúvidas? Chamaí!

Antes de realizar qualquer a??o de otimiza??o na solu??o final (relatório), acabe com qualquer lentid?o ou problema na sua consulta no banco, é importante todo o pipeline, mas o princípio é agir n origem, deste modo será uma preocupa??o a menos.

As your data sources and query requirements evolve, you may need to adjust your query strategy accordingly. This may involve changing the data sources that are being queried, the query tools that are being used, or the optimization techniques that are being employed. Continuously refining your query strategy ensures that you are extracting the most value from your data while minimizing performance impact.

To have a great performance is important to push down the filters to the DB, aggregation before calculation and transformation when it is possible and read only the columns you need.

This article was well thought out and logical. EXCELLENT ARTICLE TO GO BSCK AMD SAVE AS A REFERENCE AND EDUCATE OTHER TEAM MEMBERS.

While building the strategy it is important to also use filtering, since usually historical data remains unchanged after a certain period of time, which can be already stored at your destination point without need to query it again. And also using indexes and partitions is highly recommended when needed, because it is also a situational thing which can bring harm when implemented incorrectly. Finally it is always good to have automatic backup creation configured, because mistakes happen to everyone and truncating your historical table is never good :)

One thing I could not see it mentioned is about Data Source filters. We need be aware what kind of reports we are working with, know the boundaries and why/how the date will be used. This will help us to use data source filters and bring in only required data. This avoids unnessarily bringing more attributes into your query, there by helping with your performance of query. For eg: If someone is interested to look timesheet completion or timesheet approval pending details of organisation, it makes sence to have only limited set of data for last 3-4 weeks instead of pulling in one year worth of data.

To optimize queries in a hybrid BI (Business Intelligence) environment, consider the below points: Understand Data Sources Use Efficient Joins Filter Data Locally Indexing Caching Query Pushdown Partitioning Parallel Processing Query Optimization Tools Monitoring and Profiling Data Compression Network Optimization Materialized Views Regular Maintenance Load Balancing

Performance improvement is driven by three aspects. Influx of data and changes to its pattern, storage design and lastly access behaviour of users and processes. While consumption side query and model optimisation helps solve tactical response time challenges, the approach to it should be focused at generalising solutions while picking one bottleneck at a time and classifying the impact of the solution to the rest of the bottlenecks. This helps arrive at an optimum performance ecosystem in a predictable duration and sustain the same.

Storytelling is algo a crucial element in data analysis, turning it into powerful insights that can inspire change and drive innovation. It serves as the bridge connecting the world of data to the realm of human decision-making. By effectively conveying where business opportunities and gaps lie, it helps generate actionable plans. When data is at your disposal, the inability to communicate the opportunities it can unveil for the business makes the data lose part of its value.