What are the most effective ways to extract data from data lakes and warehouses?

FEw points to take into consideration while pursuing batch extraction : - Batch processing may require important consideration for data versioning and historical tracking. This is especially true when some data might get repeated overtime, we need to ensure it doesnt report while writing in Database - Ensuring consistency across different batches can be challenging, particularly when dealing with updates, inserts, or deletions. This is true when data source makes some changes. Without monitoring this might lead to failures if left unmonitored -Batch extractions often rely on specific extraction windows during which data is pulled and processed.

FEw consideration to be made in stream extraction: - Dealing with late-arriving data or out-of-order events is often underestimated. Organizations should implement strategies to handle such scenarios - Scalability is crucial for handling increasing data volumes in stream processing. Organizations might overlook the need for a scalable architecture that can efficiently distribute - Robust monitoring and observability tools are essential for identifying issues, monitoring system health, and gaining insights into the performance of the stream processing pipeline. - We need to incorporate mechanism for changes in data schema may occur. Stream processing systems should be designed to handle schema evolution gracefully to avoid disruptions

These points are to be taken into consideration in Incremental extraction - Dealing with deleted records is often underestimated. Organizations may overlook the need to implement mechanisms to detect and handle deleted records during incremental extraction. - Considerations like optimizing network bandwidth, using compression, and selecting appropriate transfer protocols can impact the overall performance. AS the data might be unpredictable sometime. - Organizations may overlook the importance of maintaining metadata to track extraction timestamps, execution logs, and other relevant information.

If you have to ask what method you need: you need batch. Use incremental sparingly on high-velocity fact tables. Don't use streaming unless you have a good reason. You think you need it, but you probably don't.

POints to take into consideration: - The chosen method of data extraction should be adaptable to changes in data sources, including new systems, formats, or data providers. and to future changes. An extraction method that is too rigid may become obsolete as the business landscape evolves. - As data governance and compliance requirements become more stringent, the chosen extraction method should support and adhere to these standards. - The data extraction process is typically part of a larger data pipeline. Ensure that the chosen method integrates seamlessly with both upstream data sources and downstream analytics, storage, or processing components.

Know your audience. What query, frequency, an ad-hoc approach or are they wanting to run the same query over and over again? In-addition, are they looking for signals in the data, or a more complete BI report? As data professionals we tend to focus on how we can use SQL to extract data, or use a very similar technology like Pandas (before you disagree, just look at the basic Pandas syntax and tell me it doesn't look like a reformated SQL statement) Technology is no longer as separated as it once was. AWS S3 can trigger a Lambda function on change of data in a Lake, just like a DB CDC event. We now need to think of extraction as an event, not just a scheduled SQL query. Batch and realtime require two very different approaches

- Overlooking the need for proper data versioning and historical tracking mechanisms can hinder the ability to analyze and understand changes in the dataset over time. Ensure that the chosen method supports accurate versioning and historical data tracking. - The data extraction process often involves collaboration across different teams, including data engineers, analysts, and business users. Consider the collaborative aspects of the chosen method, including ease of use, accessibility, and support for diverse user roles.