During Data profiling assess structure of data source, content, metadata while generating statistics, summaries that define their features, quality Modify or improve data using rules, functions or algorithms during Data Cleansing Compare, test, confirm data prior to, during and after the ETL process to discover, resolve any mistakes or inconsistencies through Data Validation Monitor regularly, continuously Metadata helps in driving accuracy of reports,validates data transformation and ensures accuracy of calculations Metadata can be categorised: Business metadata: has data ownership information Technical metadata: includes primary, foreign key attributes and indices Operational metadata: includes currency of data, data lineage

Balancing speed and quality in data warehousing involves optimizing ETL processes through techniques like incremental loading, parallel quality checks, and cloud scalability. By automating workflows, ensuring data lineage, and aligning business needs, teams can deliver fast, accurate data without compromising integrity. Speed and quality in data warehousing thrive when innovation meets integrity—optimizing processes to deliver timely, accurate insights

Quality is never an accident; it is always the result of intelligent effort. Balancing speed and quality in data warehousing seems difficult. It’s a tough task, especially when managing ETL workflows. Achieve perfect balance in just 3 steps: 1. Automate the boring stuff: Leverage ETL tools to handle repetitive tasks. It cuts down on manual errors and keeps things moving faster. 2. Double-check your data: Build validation checks at every stage of the ETL process to keep the data clean and reliable. 3. Focus where it matters: Prioritize tasks that have the biggest impact on business outcomes first—you’ll see better results with less wasted effort.

One strategy I rely on is optimizing data preparation, which streamline repetitive processes and ensure scalability without sacrificing accuracy. To maintain quality, I prioritize integrating validation steps throughout the pipeline—for instance, setting up automated checks for data consistency and completeness at each stage. This approach has been invaluable, in projects where I needed to deliver reliable dashboards. Finally, task prioritization based on business impact has been essential. I focus first on workflows that directly affect critical KPIs or decision-making, then iterate on lower-priority processes. Of course this response is overall subjective and related to my previous experiences, but might be helpful for the community.

A wise man said: It does not matter how quickly you get a wrong result. If you cannot trust the quality of your data it’s irrelevant. All the processes you implemented to get that data are irrelevant. Bad data is misleading. Therefore it’s better to have no data than low quality data. I hope my answer is clear.

To achieve the optimal balance between speed and quality in a data warehouse, we can use the power of data itself. By rigorously analyzing our data, identifying issues like outliers, and inconsistencies. This data cleansing process ensures data integrity and reliability. Also parallel processing techniques allows us to distribute the workload across multiple nodes accelerating data loading .By implementing incremental data loads on a set cadence, we can efficiently process only the changes to the data, minimizing processing time.optimizing source queries plays a crucial role in improving overall performance. By writing efficient SQL queries, we can reduce query execution time and maximize the efficiency of data retrieval.

Speed Optimization Strategies 1. Parallel Processing 2. Data Partitioning 3. Optimized SQL Queries 4. Cloud-Based ETL Quality Assurance Strategies 1. Data Validation 2. Data Profiling 3. Automated Testing 4. Data Lineage Balancing Speed and Quality 1. Prioritize Critical Data 2. Implement Incremental Processing 3. Monitor and Optimize 4. Adopt Agile Methodologies

In ETL workflows, balancing speed and quality is crucial. Leverage automation tools to reduce manual errors and accelerate processing, but never compromise data integrity. Implement strategic validation checks and prioritise tasks based on business impact. This approach ensures efficient, reliable data pipelines that deliver meaningful insights without getting tangled in unnecessary complexity.

In my experience, if there is more ETL logic implemented, then the system creates more target DB schemas for different purposes and then the user's queries don't need to be complex and will be fast enough. Data Quality is another topic managed by Data Governance program incl Master Data Management ETL logic and more.

Quality > Speed Quality data covers - Insight, value & key metrics Speed - can be mitigated, negotiated for different audiences/use case.