Data Profiling tools like Informatica Data Explorer provides an intuitive interface IBM InfoSphere Information Analyzer has advanced data profiling capabilities Exemplified data validation rules must be specific to a system or dataset depending on the business requirements, the nature of the data being processed Apache NiFi, Talend, Oracle, SQL Server, MySQL may be used to save network bandwidth and computational resources while maintaining data freshness Define batch, streaming data- parallel processing pipelines using Apache Beam Google Dataflow supports execution of various data processing patterns Consider integrating with monitoring and alerting solutions like Splunk, ELK Stack, centralize error logs Automation is needed

Identify Bottlenecks: Track metrics to find and optimize heavy steps. Incremental Loading: Load only new or changed data. Partitioning and Indexing: Use partitions and indexes to speed up processing. Parallel Processing: Handle multiple data streams simultaneously. Data Caching: Cache frequently accessed data. Filter Data: Process only relevant data. Regular Maintenance: Maintain tables regularly for optimal performance. These strategies can help reduce errors and improve ETL efficiency.

When we are facing this situation We can do incremental load For the rest of data or the data that is left And how to analyze this thing just check the pipeline where and which process is heavy or taking lot of time try to optimize that or remove with other properties

The efficiency of an ETL process is influenced by many factors, which will depend on the data volume, the sources of the data, etc. However, in any case, it is very important to perform incremental loading, processing only the newly added or modified data, thereby reducing the volume of data to be processed. Using cache for transformations, parallel processing, and having quality controls at the end of the process are also very important. These are just some of the aspects to keep in mind.

1. Split large datasets not smaller portions. Focus on incremental loading. 2. Check transformation and ETL logic. Remove redundant steps. 3. Prioritise loads. Get the largest loads executed during off-peak times. These steps should help.

When facing ETL process errors and failures optimizing for faster data loading involves a few key steps. First monitor and log errors to quickly identify and address root causes and validate the data before loading by using staging areas and ensuring schema compatibility. Implement incremental loads to only process changed data, reducing the amount of data moved and speeding up the process. Adjust batch sizes for better performance ,use parallel processing where possible and minimize data movement. Disabling indexes during large loads, optimizing resource allocation and continuously reviewing ETL tool settings will further enhance efficiency and reliability.

Start by describing the problems and identifying the bottleneck, following these steps: - Spliting large datasets and optimizing queries - Orchestrate your flows in parrallel mode - Make sure to use incremental or delta load wherever possible - Use temporary tables - Review your indexes and remove unnecessary ones - Avoid full data extraction If none of the above steps works... it's important to re-evaluate your used technologies and tools.

Basic but useful Identify the correct CDC (Change Data Capture) columns and primary keys to enable incremental data loading. Review the schema structure, including data types and column sizes. A dynamic approach can also be implemented in the pipeline to handle errors more efficiently.

Identify delta or enable change data capture at source level ensure only required data and attributes are being pulled incorporate appropriate batch size optimize parallelism and use non-blocking transformations evaluate if ELT is better for you or ETL Scale up compute as needed