The seven must-have data quality checks in ETL: NULL values test Volume tests Numeric distribution tests Uniqueness tests Referential integrity test String patterns Freshness checks Data Observability Not checking for unique records could lead to flawed decision making Make sure that foreign keys in data match the corresponding primary keys Freshness tests can be created manually using SQL rules or within certain ETL tools like the dbt source freshness command Data observability should be at the heart of the data stack as it is more effective than testing Some commonly utilised validation techniques: Data Type Checks Range Checks Constraint Checks Consistency Checks Consider automating data validation processes

Here are some key strategies to achieve this balance: 1.Incremental Data Loading 2.Parallel Processing & Distributed Computing 3.Efficient Data Validation & Error Handling 4.Optimized Data Transformation 5.Data Partitioning & Indexing 6.Performance Monitoring & Load Balancing

Achieving a balance between speed and accuracy in ETL processes can be done by following three principles: 1. Data Quality at Source: Ensure accurate, complete and consistent data. 2. Hyper Automation: Leverage automation technologies like RPA, AI and ML. 3. Human-less Monitoring by Leveraging AI: Use AI-powered monitoring tools. Implementing these principles streamlines ETL processes, improves data quality, increases efficiency and balances speed and accuracy.

I balance speed and accuracy in ETL by automating repetitive tasks to reduce manual errors and improve efficiency.Distribute workloads across multiple servers to prevent bottlenecks.Optimizing transformations with parallel processing and efficient algorithms.Implementing robust validation checks at each stage for data integrity. Load high-priority data first to ensure timely availability for decision-making.Using incremental loads instead of full loads to minimize processing time.Monitoring and logging to detect and resolve issues quickly.Leveraging cloud and distributed computing for scalability and faster processing.

Here's a breakdown of how i have achieved it in simple terms: The Challenge: Speed: Businesses need data quickly to make timely decisions.This means ETL processes should run fast. Accuracy: Data must be correct and reliable.Errors can lead to costly mistakes. The Reality: Yes, it is possible to achieve both speed and accuracy, but it requires careful planning and the right tools. Here's how: Automation: Automating as much of the ETL process as possible reduces manual errors and speeds things up. Strong Testing: Implementing robust testing procedures at every stage ensures data accuracy. This includes Data validation checks, Data profiling & Regression testing. Additionally we can use Modern cloud based tools, Optimization and Monitoring.