Here's how you can create and execute a streamlined ETL process in Data Warehousing.

In the realm of data management, an effective ETL (Extract, Transform, Load) process is characterized by its modularity, performance, adaptability, flexibility, and alignment with data requirements. Drawing from my professional experience, I advocate for a design approach where the E, T, and L components of the ETL process are mutually exclusive, thereby enabling a plug-and-play functionality. This approach not only enhances the efficiency of data processing but also ensures the scalability of our data systems

Una canalización de datos si bien puede ser modular y altamente adaptable en la medida de lo posible; siempre tendra unos objetivos de negocio que cumplir a nivel técnico desde la infraestructura. Tener claro los objetivos de el procesamiento ETL; o ELT si es el caso; ayudara mucho desde la creación a optimizar costos, mantenimiento, optimización y diseño de la canalización de datos, además de el rendimiento del equipo de datos.

Identify the data sources, know what you want to do with the data, who the audience would be, and how fresh should the data be.

generally, we want to help companies or organizations to solve their problems, it depends on what they want! sometimes you design a perfect plan for data gathering, data integration or reduce data redundancy but it doesn't work so first of all, you have to listen carefully to make measures or reveal needs next step concider infrastructure limitations ,data space also recognize bottlenecks. finally, make a best plan to achievement

The ETL process is the core of any data strategy. It must be aligned with business needs, getting data from multiple systems that make sense and guarantee performance to deliver insights through dashboards and Machine Learning.

Give yourself plenty of leeway. You will need it. You will need it. You will need it. Your main run should fire at 12:01am. You want plenty of time to ensure dependencies are accounted for, and enough time that people aren’t suffering pages just in time to tell them they’re too late. Data that is needed at 7am shouldn’t be finishing at 6:30am. It should be finishing at two in the morning.

Data mapping is essential in ETL as it defines the relationships between source system fields and the target data warehouse schema. This process ensures accurate data flow and transformations, maintaining data integrity and making the data useful for analysis.

Data mappings are extremely critical to be done at the design phase in order to avoid critical failures that might impact multiple downstream systems/interfaces. It is also important to keep it up to date as it can come very handy in debugging production failures for complex mappings.

Mapping is the very first step of technical analysis. This is crucial to provide correct data in correct place of choice and also to minimize the storage and processing resources usage in the future in order to save on costs. You need to take care of choosing only the information you need, having it properly defined as a source of truth, map to staging outputs that then will be converted into fact & dimension tables.

Um aspecto primordial do processo de ETL é o mapeamento dos dados, que envolve a identificação e a correspondência dos dados de diferentes fontes com os requisitos específicos do sistema de destino. O mapeamento dos dados é fundamental para garantir a integridade e a exatidão das informações durante todo o processo. Para isso deve-se envolver uma análise cuidadosa dos dados disponíveis, a identificação de padrões e inconsistências, e uma definição de regras e transformações adequadas para garantir que os dados sejam compatíveis com o sistema de destino que utilizará essas informações. Um mapeamento de dados bem executado não apenas facilita o ETL eficiente dos dados, mas garante a confiabilidade dos insights gerados através dos dados.

The top items to be considered for ETL design is the right tool considering the data volume size and variety. Then proper data cleansing and staging for further processing. The design should be reusable and should implement future changes efficiently and easily with automation wherever possible considering retention and purging policies for growing data. The design should be simple enough and parameterized wherever feasible and convenient for effective monitoring and debugging with audit controls

Designing an effective ETL process involves selecting compatible tools that match your team's skills and planning detailed data workflows. Define workflows for data extraction, validation, cleansing, and transformation, ensuring each step aligns with your data mapping. Prioritize a scalable design to accommodate future data volume increases, choosing tools that can efficiently scale. This approach ensures your ETL system remains robust and adaptable to evolving data needs.

The number one rule is select the tool your team is familiar with, or have resources to quickly grasp knowledge. In proper data warehousing, cleansing and data validation should be another process which is a part of Data Quality and entire Data Governance Strategy. Be sure to consider Data Lineage in this part, so that the information flow will be trackable throughout the entire data journey.

ETL? I think it's more of an ELT world these days. Tools like Snowflake and dbt make ELT a dream. "Extract and load" so you have an exact (untouched mirror image) of your source data in your database (did someone mention Snowflake?) then dbt can do what it does best and transform the data into the best design for the visualisations that will follow.

Use scripting languages, SQL queries, or built-in transformation functions provided by your ETL tool to perform data manipulation tasks.

With your ETL design ready, the next step is to build the process. This involves writing scripts or configuring your ETL tool to handle data extraction from source systems, applying necessary transformations according to your mapping, and then loading the transformed data into your data warehouse. It's essential to integrate robust error handling and logging mechanisms to monitor the process and address any issues promptly. Start by testing your process on a subset of data to ensure everything functions correctly before rolling it out on a full scale. This approach helps catch any errors early and fine-tune the process before it becomes critical to operations.

Build Process: Implement the ETL process according to the design. This involves coding the data extraction, transformation, and loading routines, as well as setting up schedules for regular data updates.

Executing ETL is the easy bit and can be automated. What you need to consider as part of this is error handling and automated testing. What happens in the ETL fails, does it restart, from where, who's notified sort of questions? Then how can you automate testing on the ETL to check if say, data is missing or incorrect? Then can it be automatically fixed? What's the downstream effect of failure and how to roll back. All this needs to be considered as part of execution.

Execute ETL: Once the ETL process is built, start executing it to move data from source systems to the data warehouse. This step may involve testing and debugging to ensure the process runs smoothly and reliably.

Executar ETL, significa ativar os fluxos de trabalho para transferir dados dos sistemas de origem para o data warehouse. Agende as tarefas de ETL para fora do horário de pico, minimizando assim o impacto nos sistemas em operação. Monitore de perto nas primeiras execuções para detectar erros e garantir a precisão dos dados carregados. Ajustes podem ser necessários para otimizar o desempenho e os resultados ao longo do tempo.

Executing your ETL process means running the established workflows to transfer data from the source systems to the data warehouse. It's advisable to schedule these ETL jobs during off-peak hours to reduce the impact on your operational systems. Initially, it's important to closely monitor the process to identify and correct any errors quickly, ensuring that the data loads correctly. As you observe the process, you may need to make adjustments to optimize performance and ensure the desired outcomes are achieved. This continuous monitoring and tweaking help maintain the efficiency and accuracy of your ETL process.

Share insights, findings, and success stories with the broader team through internal knowledge-sharing sessions, wiki pages, or collaborative platforms to facilitate cross-functional learning and continuous improvement.

In my experience, you need to set up proper alerting. Just because your processes can catch errors and save information about it (which is crucial) doesn't mean that relevant people will be notified of that event. I suggest here to keep the logs also in special database table that will be kept not only on entire job step level, but also one level deeper on all processing steps.

One of the most critical steps in ensuring the smooth operation of your ETL (Extract, Transform, Load) Jobs is establishing a robust monitoring platform. From my years of experience, I've found that setting up alerts and leveraging database tables to flag each step and its nuances is indispensable. However, the true power lies in what comes next. By harnessing this data, you can construct dynamic dashboards that provide real-time insights, allowing you to monitor job performance at a glance, rather than delving into individual database tables. This proactive approach empowers you to identify and address issues swiftly, ensuring the seamless flow of data throughout your ETL processes.

Alerting is extremely critical when it comes to ETL processes especially in OLTP systems that handle major interfaces related to the application. One must also not avoid monitoring the resources, runtime, utilisation etc.

1. Having runbook to capture the job details and scheduled details helps the support team a lot. 2. Logging framework to capture logs for better debugging (e.g. kibana dashboard, any open source logging framework,etc) helps to resolve issue easier.

In my experience, it is vital to evaluate if the ETL automation is saving time AND making the process simpler, at times automation makes it time efficient but plugs more complicated processes which is an overburden - the mantra is to keep it Simple and Straightforward !

Security: - Encrypt data both at rest and in transit to guard against unauthorized access. - Use role-based access controls to ensure only authorized employees can access data. - Keep detailed logs of all ETL operations to monitor and review for any suspicious activity. Compliance: - Make sure the ETL process complies with industry regulations like GDPR, as well as internal policies. - Protect sensitive data, cross-border data and adhere to privacy policies throughout the extraction, transformation, and loading stages.

other point to consider is reusability. If there are use cases which requires us to reuse an object or a process make sure it is parameterised for reusability.

Consider crafting micro functions or stored procedures that offer versatility across various processes. These modular components can be dynamically integrated into multiple workflows, enhancing reusability and streamlining development efforts. By embracing this approach, you not only promote code efficiency but also foster scalability and maintainability in your system architecture.