What are the most effective ETL workflows for speed and efficiency?

Most data pipelines I've built have been Batch ETL pipelines. The reason I like Batch ETL pipelines is because (1) it reduces the number of I/O operations since I can read once and load once (or at least a fewer times). (2) Parquet truly helps with data compression when you have a good amount of rows and most Batch ETL processes have a significant amount of data whose size can be reduced by parquet formats. Yes, the latency and resource consumption would be slightly higher but if the latency isn't a problem (downstream users don't need data immediately) and cost isn't a major factor, then batch pipelines could be extremely helpful.

In my work with Batch ETL, designed for large data volumes, I've set up pipelines to run either manually or on a schedule. Each run involves extracting data from the source, processing it, and then loading it into a data sink, completing once all data is processed. Execution time varies, often based on data size, ranging from minutes to a few hours, even with parallel processing. A key learning for me has been the impact on the data source's load. To minimize this, I schedule these jobs during low-activity periods, like at 1 AM, to avoid affecting other workloads. This approach ensures batch processing doesn't disrupt system performance during peak hours, maintaining overall efficiency

In my Stream ETL work, handling infinite datasets, I've valued its ability for rapid insights into recent data changes. It's great for near-real-time, almost accurate insights. Typically, I use Stream ETL for quick initial results, followed by a detailed end-of-day batch process for full accuracy. Key to this is understanding two time aspects: Event Time (when events actually happen) and Processing Time (when they're processed). Ideally, these should match, with immediate event processing. Yet, in practice, network delays and system limits often cause a lag between these times, a common hurdle in my experience with Stream ETL, necessitating strategic planning to address.

I can contribute here because we use Hybrid in my organization. We have a combination of DMS tools bringing in data in a Data lake through our pipeline. And then on scheduled timing, we transform them through bespoke ETL procedures into our DWH. The downside that i see with this approach is that our DWH is always stale "since its last successful transformation". The way i would improve this would be to add an event based mechanism from lake to invoke appropriate ETL transformation in DWH. Things to consider with this approach would be the completeness of "related" data elements from Lake into DWH. But that can be take care of and we can have a good "event based" bespoke transformation from data lake into DWH.

In our Hybrid ETL system, DMS feeds all data changes into an S3 bucket. We then split this into two streams: real-time processing for key tables and batch processing for others. This ensures quick data access for critical needs while managing resources efficiently for less urgent data. This dual-path approach optimizes both immediate response and cost-effectiveness. Real-time processing addresses urgent data needs, and batch processing handles the rest, scheduled based on business requirements. It's a practical blend of immediacy and efficiency, leveraging DMS for data capture and smartly dividing processing tasks in our Hybrid ETL framework.

Some useful features of Serverless ETL include: -Leverages platforms like AWS Lambda, Azure Functions, or Google Cloud Functions. Developers write code as functions and the cloud provider handles execution and scaling. -Event-Driven Architecture: ETL processes triggered by events like data arrival or scheduled time from sources such as cloud storage or message queues. -Decoupled and Modular Design: ETL tasks broken into independent functions. Improves flexibility and maintainability. -Allows the use of multiple programming languages or technologies. Enables flexibility in choosing tools for specific ETL tasks. -Seamless integration with cloud storage, databases, and data warehouses, Auto-Scaling and Pay-Per-Use Pricing.

In my IoT projects, serverless ETL has been a standout, adeptly handling unpredictable data from numerous devices without constant server capacity. This flexibility is a major plus. For mobile and web analytics, where user traffic and data loads vary, serverless ETL's dynamic resource scaling ensures efficient processing, a critical factor in maintaining performance. In e-commerce, especially during sales events with data spikes, serverless ETL's quick scaling up and down aligns perfectly with demand, optimizing costs. Its elasticity and cost-effectiveness are invaluable, allowing me to focus on insights rather than infrastructure. This makes serverless ETL ideal for dynamic, data-heavy environments.

The primary consideration in choosing an ETL workflow should always be the business need and the cost-benefit analysis. If you have a real-time ETL process that doesn't align with business requirements or doesn't add tangible value, it can lead to unnecessary resource expenditure and technical debt. It's crucial to evaluate the business needs thoroughly and develop a matrix to assess the impact and necessity of the ETL process. This approach ensures that the chosen ETL strategy is not only technically sound but also cost-effective and aligned with business objectives. Making an informed decision based on this matrix can help avoid wasteful spending and focus resources on processes that genuinely benefit the business.

Definitely would consider "any of the above" approaches based on the following criteria: 1. Customer expectation (timeframe) on how frequent do they want to see the data refresh in DWH. 2. Customer expectation from source to DWH destination in terms of how soon the data should travel ? 3. Budget available for setting up the infrastructure for DWH.