How can you design an ETL architecture that scales?

By getting the basics right. The database & ETL world is beautiful and relies on its standard operating procedures (SOP). To simplify, always try to follow these: - Define specific sources from where you will collect the data. - Match your target tables to your data sources. Column by column, data type by data dype. - In your target tables, make sure to have primary key, foreign key constraints right. - Create an exception mechanism so that if something goes wrong, your process can handle it. - Create indexing in your tables for faster efficient querying. These are some basics, but scalability depends on how well you have defined your processes.

It's crucial to document your data sources, including their strengths and weaknesses. This can be done through organizational SOPs. For data targets, it's important to clearly define the end use of the data; the design of the ETL system should align with the data consumption needs of the end-users or systems, be it data warehouses, data lakes, or other applications. This can involve determining the desired format, frequency of data refresh, and necessary transformations. Additionally, for scalability, the ETL architecture should be designed to handle increased data volume and complexity, which could involve cloud-based solutions, parallel processing, and modular design for easy adaptability and expansion.

To design an ETL that scales, you must identify the patterns. You need to know connecting to data sources, integration, checking and monitoring, auditing etc. all have patterns that can be pre-built as template and replicated to handle very large and complex environments. Knowing the patterns is the key to modularizing large and complex systems.

Just one rule here - GIGO - garbage in garbage out. Rest everything else can be worked upon. Not all data sources will be as per the requirements that we define. We will have to work with tough systems which are of legacy in nature, tough people who are of legacy in nature and not willing to share the proper use cases. However, as long as you think data and focus on the quality of it, you should be fine.

When considering tool selection consider if it will scale into the future and be compatible with its connected components. Also consider availability of support as you will need to work out any quirks. Using tech that has large user base usually means decent support for the near future.

As technologists, the tool set should be the last thing you need to worry about. Think about the requirement at hand and then work on the toolset. Don't start with a tool just because you are comfortable with it and not willing to learn. Technology is the easiest to learn. For every tool, there are a 1000 youtube videos on how to conquer it. so focus on the requirement and fit the tool around it, but not the other way.

This is one of the crucial decisions in designing ETL Architecture, if we fail here, too much cost is involved to rebuild all the ETL jobs in another tool. A few things to keep in mind, are the cost(Lic) of the ETL tools, the important features we need to handle our product scenarios as part of ETL, ease of using the tool, and whether we can quickly design ETL jobs. in my earlier experience, we had changed the ETL tools twice due to a license cost issue as middle /low-range customers could not afford the lic cost.

Look into options from different angles: - Tools Native to your Platform vs external - self-hosted or SAAS - Low-code or Development - Supported or OpenSource - part of an existing stack or something new Special attention needs to be paid to compliance or security requirements in the industry you work.

As we are talking about an ETL architecture that scales, we should choose a tool that scales. Some tools scale linearly, meaning if you double up the resources, your ETL jobs run at twice the speed. It is important to check and compare the tools carefully, as there are differences concerning the scaling and the connected licensing/pricing. As mentioned before, learning the actual toolset is probably not much different, even though some are easier than others.

When choosing ETL tools and frameworks for a scalable architecture, consider factors like flexibility, scalability, and ease of integration. Opt for tools that support parallel processing and distributed computing to handle increasing data volumes. Choose frameworks with a rich set of connectors for diverse data sources and targets. Prioritize solutions that offer automation and orchestration capabilities to streamline complex workflows. Evaluate the ability to scale horizontally by adding more processing nodes. Opt for tools compatible with cloud environments for elasticity. Keep an eye on community support and updates to ensure long-term viability of your chosen tools.

Integrations are very critical. Similar to tools advise that I gave above, focus on the requirements.. don't focus on the integration tool and try to retrofit the integration, instead carefully think about what fits in that particular integration pipeline and act accordingly. The code that I wrote and the integrations that I created couple of decades (not years) are in production to this day. There are people who are happy with parts of it and there are people who are bearing the burden of the wrong decisions that I have taken. So, choose the integrations and pipelines wisely and make it easy for the subsequent generations!

Design to scale 1. See if there is a potential for data growth and implement batching logic if there is a possibility of heavy data traffic though the ETL. 2. Batching logic should have a throttle available to the DBA or the production support team. This way they can control the batch size when the server gets over utilized - CPU or Memory spike. 3. Plan partitioning for your tables with data growth in mind if you need the ETL to scale. 4. Proper index and query architecture for all the tables and queries involved to avoid bottle necks as the data grows 5. Performance cannot be an after thought of the design but part of it from the get go. 6. Modular and reusable design than confusing not repeatable design.

In-memory processing is a powerful tool for enhancing the performance of ETL tasks, especially when dealing with large datasets. In traditional disk-based systems, data is read from disk into memory before it can be processed, and then written back to disk. This time-consuming I/O operations can become a bottleneck in the ETL process. In-memory processing can significantly reduce I/O operations, leading to much faster processing times, because data is not stored or moved back and forth from the disk to the memory. It’s particularly beneficial in the ETL process, where speed and efficiency are crucial. However, it's important to balance the need for speed and efficiency with the costs when deciding whether to use in-memory processing.

As the target is a scalable ETL architecture, it's worth to create reusable components to tackle common issues: - mechanisms to portion data into suitable chunks and provide bookkeeping - general structures to handle the chunks in parallel, with an adjustable degree of parallelism - standard ways to enable re-processing and check summing

When designing your ETL pipeline for scalability, focus on modularization and parallel processing. Break down the pipeline into smaller, independent tasks that can be executed concurrently, allowing for efficient resource utilization and scalability. Implement a fault-tolerant architecture to handle potential failures gracefully. Leverage distributed storage and processing solutions, such as cloud-based platforms, to scale resources dynamically based on data volume. Ensure data integrity through proper error handling and logging mechanisms. Regularly optimize the pipeline for performance by fine-tuning configurations and monitoring resource usage. Prioritize a well-documented and flexible design to accommodate evolving data requirements.

Scalability is critical and I cannot stress on how important it is! I once started writing an adapter that generates reports pulling the data out of business objects. It was supposed to be a stopgap solution in place supporting a dozen reports per day. it bubbled to a requirement in just a few months to create 100's of reports on an hourly basis. Scalability is not an after thought.. it has to be built in, similar to quality! Following good devops principles, these days, pretty much takes care of the deployment aspects. There are devops teams that help you with it. Help yourself by asking for help. Teams are there to support you!

Utilize containerization technologies like Docker for seamless deployment across different environments. Implement an orchestration tool, such as Apache Airflow or Kubernetes, to manage and scale your ETL workflows efficiently. Leverage auto-scaling capabilities to dynamically adjust resources based on workload fluctuations. Regularly monitor performance metrics and scale resources proactively to accommodate growing data volumes. Consider implementing a load balancing mechanism to distribute workloads evenly across processing nodes. Continuous testing and automation are essential for ensuring a smooth deployment and scalability process.

Injecting intelligence capability into your ETL architecture will help you scale. ETL Intelligence capabilities include leveraging the same resources to achieve better results via sys objects metadata and logs. You can expect more efficient use of resources, boosted performance, faster speed and less costs. Intelligent ETL dictates scaling for different scenarios as follows: horizontal (scale up), vertical (scale out), selective (scale in some steps where necessary), elastic (scale in some scenarios when necessary). Expect to design an orchestrator which can handle these complex needs.

The most important factor to consider for successful results is the error handling mechanism. It is based on severity, recurrence and prevention complexity. The optimum performance should be achieved by comparison, analysis and filter options.

In the spirit of contributing perspective, while I love these suggestions the days of the ETL frameworks are pretty far behind us. Low level data mapping, pipelines, and conditional logic is mostly boilerplate in common frameworks (dbt Core, etc.), the use-cases and integration patterns are fairly standard, and the Data Fabric / Lakehouse models greatly reduce the complexity of the ETL layer. More significantly, the emergence of operational AI and the ability to use generative learning based agents for data integration and the advanced capabilities in inference, context mapping, and logic greatly exceeds what we can do procedurally with pipelines. I embrace our new machine overlord masters.

Remember, with data, it is GIGO - garbage in, garbage out. It will be the case till the end of time. With the recent AI proliferation, quality of data is becoming all the more important!

It's important to scale only the operations that benefit from scaling. One can through a lot of resources at certain tasks, which anyway will run at the same speed, because they are bound to external factors for example.