You need to streamline your organization's data warehousing. How can you do it most efficiently?

Agile in design, design that follows foreseeable business requirements. Starting with a thorough needs assessment is crucial for a successful data warehouse. Beneficiaries often aim for over-engineered, "future-proof" solutions, unnecessarily inflating costs and complexity. Focusing on immediate requirements first ensures effective resource allocation and avoids building out features that might never be used. Example, if regulatory reporting (typically monthly or less frequent) is the primary use case, streaming capabilities become superfluous. I remember speaking with a Belgian bank CFO whose main objective was IFRS reporting. He even chuckled at the suggestion of needing real-time data for such static reports.

The first and the most important step is to perform a detailed assessment of the existing DWH landscape including: 1. Various data sources involved 2. Data silos that exist in current landscape 3. Technical debt and legacy tools involved 5. Challenges that exist in current landscape from scalability, cost and performance perspective 6. Upstream and downstream systems involved 7. Key challenges which the data consumers are facing and asks from them 8. History / incremental data requirements 9. Batch / real-time needs 10. Data Security including PII data, etc. All these and many other aspects will provide a clear perspective on existing data landscape challenges and provide a foundation in designing your DWH landscape & target architecture.

Perfeito, antes de qualquer otimiza??o/simplifica??o temos que entender o todo para conseguirmos ser mais efetivos! Se já for direto para o técnico, é furada! #ficaadica

Position the data warehousing design and operation within the appropriate enterprise architecture lifecycle (for example make sure phases Preliminary and A through C have been followed if adhering to TOGAF) and ensure alignment to the business goals. The design of data warehouse has a dependency on business architecture and capabilities, however this dependency is neither unidirectional - a good data warehouse is at the very least an enabler for what the business needs but could expand into changing the very business model - nor sequential, as the business capabilities and data warehouse work best when they continually shape each other, even if the definition of business capabilities has a headstart.

Start by defining a solid data strategy. Starting from various types of data, sources, lineages, scope followed by identifying proper access controls. Also identifying different use cases such as analysis and reporting.

Escolher a plataforma certa para seu data warehouse é como decidir entre morar em uma casa, um apartamento ou uma combina??o dos dois. Uma casa (plataforma on-premise) oferece controle total e seguran?a, mas requer mais manuten??o e custos iniciais. Um apartamento (plataforma em nuvem) proporciona flexibilidade e é mais fácil de gerenciar, mas pode ter limita??es de personaliza??o e desafios de privacidade. Uma op??o híbrida é como ter uma casa com acesso a algumas comodidades de um condomínio, oferecendo uma mistura de controle e conveniência, mas necessitando de coordena??o eficaz entre os dois ambientes. O que achou da analogia?

It is important to simplify your tech portfolio by avoiding trendy tools that do not contribute to business capabilities. Duplicated capabilities across different tools should also be avoided, and standards should be looked for. Different data teams do not necessarily need to have different ways of working, as standardization can be achieved through governance.

To find the best platform you need to consider these factors: 1. Assess current and future data needs. 2. Consider costs, scalability, and technical requirements. 3. Analyze security and compliance. 4. Evaluate flexibility and organizational capabilities. Based on these factors, the company may choose a cloud platform like Amazon Redshift for scalability, cost-effectiveness, and robust security features.

If not already in the cloud, consider migrating to a cloud-based data warehouse solution for better scalability, performance, and reduced physical infrastructure complexity. Where possible, modularize the data warehouse architecture to allow for easier maintenance, scalability, and updates.

Understanding cost factors can be a challenge. For on-premise solutions, some factors are: hardware, licenses, utilities, and staffing. For cloud solutions: compute, storage, and data transfer costs need to be understood. For example, loading data into your data lake is usually low to no cost, as is transferring data within a region. Reading (or querying) data from your cloud platform comes with a cost that can add up quickly.

Outra analogia... Implementar qualidade e governan?a de dados em um data warehouse é como manter a ordem e a eficiência em uma biblioteca. Assim como uma biblioteca classifica e organiza seus livros e garante que todas as informa??es sejam precisas e atualizadas, a qualidade e a governan?a de dados garantem que as informa??es no data warehouse sejam corretas, confiáveis e usadas de maneira apropriada. Estabelecer padr?es e regras para a qualidade dos dados é como definir um sistema de cataloga??o para os livros, enquanto a governan?a de dados, incluindo a defini??o de papéis e responsabilidades, é como ter bibliotecários que gerenciam e supervisionam o uso e a circula??o dos livros.

Implementing robust data quality measures and governance protocols significantly enhanced the reliability of our stored information, ensuring accuracy and consistency. Establishing clear policies, data validation routines, and regular audits contributes to a cleaner, more efficient data repository. This, in turn, boosts confidence in decision-making processes. Automate data quality checks and integrate governance practices into the data lifecycle. Regularly review and update governance policies to align with evolving business needs and industry standards.

Establish strong data governance policies. Standardize data definitions and formats. Implement data quality rules to ensure consistency and accuracy. Implement processes for ongoing data cleaning and quality checks. High-quality data reduces complexity in data handling and analysis.

Overall data management, including strong data governance, data quality, metadata management, data security, reference and master data management, BI and Analytics, data modeling, data storage, etc., becomes a very strong foundational consideration when designing a data warehouse solution. If right data governance principles are not implemented, the DWH solution will eventually start consuming the data which is either not of good quality, or does not comply to security standards, or it's not relevant for end user consumption, etc. thus leading to a large failure impacting huge cost and dissatisfied data consumers. Hence, strong data governance principles should be critical part of any DWH solution implementation and should not be ignored.

Define data quality rules and policies that address specific aspects of data quality. This may include validation rules, formatting standards, and business rules that ensure data adheres to defined criteria.

Uma analogia para quem tá iniciando... O carregamento e armazenamento de dados em um data warehouse é como organizar uma cole??o de livros em uma estante. Você pode carregar os dados em grandes lotes, incrementalmente ou em tempo real, dependendo da quantidade e frequência dos dados. Para armazenar os dados de forma eficiente, você os organiza, comprime, indexa e armazena em se??es, assim como organiza os livros na estante. Ferramentas de automa??o facilitam todo o processo, assim como usar uma ferramenta de organiza??o para seus livros. Isso melhora a velocidade e eficiência do seu data warehouse.

While revamping our data warehousing strategy, we faced challenges with slow data loading and storage bottlenecks. By optimizing our loading processes and adopting efficient storage solutions, we achieved significant performance improvements, reducing latency and enhancing overall system responsiveness. Implementing parallel loading, utilizing data compression techniques, and leveraging cloud-based storage options have consistently led to faster data ingestion and retrieval. Utilize partitioning and indexing strategies based on the data usage patterns. Consider adopting columnar storage formats for analytical workloads, as they facilitate faster data scans and retrieval.

Optimize data loading performance by parallelizing the loading process, especially for large datasets. Implement strategies for archiving historical data that is not frequently accessed.

Data Loading being one of the core functions of the DWH, depending on the volume and frequency of data, the right load strategy can be used, e.g., batch load, incremental load, real time / near real time loads, etc. Additionally, the way you have designed your DWH layers and choice of tools selection also plays important role in optimizing load strategy, especially bringing functionalities like CDC, Parallel loads, etc. From data storage perspective, you can always decide the way to partition / index your data for faster accessibility. Additionally, having a good data archival policy plays important role, so that warm and cold data remains in cheaper storage, whereas, regularly accessed data remains in hot storage.

Designing effective data access and security measures is paramount for a streamlined data warehousing process. Incorporating encryption for data at rest and in transit, enforcing stringent authentication mechanisms, and establishing role-based access controls have consistently fortified data security. Regular audits and updates to security policies are essential to adapt to evolving threats. Implementing a comprehensive access review process aids in identifying and rectifying potential security gaps. Additionally, leverage advanced authentication methods, such as multi-factor authentication, to add an extra layer of protection. This proactive approach ensures a robust and secure data access environment.

In DWH world, data access and data security both play a very critical role to ensure that right stakeholders get access to the data they need and the data remains secure while in transit or at rest. This is also required to ensure regulatory compliance of the DWH. Data in the DWH can be accessed using multiple channels, be it a BI tool, direct queries, intermediate tables, etc., hence data access management via strategy like group-based access policy can be implemented, to ensure managing users and scale and also controlling the access. More such strategies exist. For data security, right level of encryption, authentication and authorization mechanisms can be implemented to ensure that the data remains secured and avoid any leakages.

Performance Testing is one of the tests we do, before deploying Data Integration jobs for a Data Warehouse. This needs to be done especially when dealing with large volume of data. A specific set of test cases for performance testing should be built and make observations. Based on the results, we need to find ways to improve the performance. Most important thing would be to identify the bottleneck in the data pipelines. We may need to take help from DBAs and other database engineers for suggestions. I remember adding hints to the SQL Queries and creating different types of Indexes on the base table improved the performance. Simple rearrangement of tables (in joins) in source SQL query and sort-before-aggregate can also help.

Review and simplify your data models. Transition to simpler schema designs like star or snowflake schemas, which are easier to understand and query. Normalize data to reduce redundancy but be cautious of over-normalization which can complicate queries. Review and refactor ETL (Extract, Transform, Load) processes. Eliminate unnecessary transformations and consolidate ETL jobs where possible. Automate ETL processes to reduce manual intervention and errors.

Regularly evaluating data warehouse performance is essential for maintaining efficiency. Conducting periodic performance assessments, monitoring query execution times, and identifying resource-intensive processes contribute to proactive issue resolution. It's crucial to collaborate with database administrators and data engineers to implement optimizations and ensure optimal system performance. Utilizing performance monitoring tools and implementing query profiling has been instrumental. These tools provide insights into query execution plans, resource utilization, and overall system health. Continuous monitoring and fine-tuning are key to sustaining peak performance in a data warehouse environment.

Data in the DWH is generally meant to address complex analytical requirements, especially dealing with large data retrieval and analysis. Such queries scan large volume of data and also need lot of computational power to process the data. When monitoring or dealing with DW, performance needs to be evaluated at multiple levels, including 1. DW hardware, e.g., memory and disk reads, support for parallelism, etc. 2. Physical storage: Row storage, column storage, data compression, etc. 3. Query optimization techniques: parallel query optimization and execution, query cost and size estimation, etc. 4. Improving data searching efficiency: Implementing indexing, clustering, partitions, etc. These strategies guide in DWH performance evaluation.

Design a data model that suits your business requirements and query patterns, define the granularity, dimensions, and measures of your data, and implement proper indexing, partitioning, and query optimization

Cost benefit analysis : “streamlining” can mean completing overhaul or incremental changes. The biggest hurdle is always the cost or rather lack of focus on value. DWH projects tend to be IT/tech driven and lack business value articulation. Prepare a robust business case and if it doesn’t stack up, don’t bother streamlining.