In a recent project, I integrated data from multiple ERP systems into a centralized data warehouse. The main challenge was handling inconsistent data formats and ensuring data integrity across systems. I implemented a robust ETL process using Python and SQL, which included data validation and transformation steps. This ensured that the final dataset was accurate and consistent, despite the initial discrepancies.

I prioritize data quality by implementing a multi-step validation process. This includes data profiling to identify anomalies, data cleansing to correct errors, and data enrichment to enhance completeness. Additionally, I use automated checks and alerts to monitor data quality post-integration, ensuring that any issues are promptly addressed.

I am proficient in a variety of tools and technologies, including ETL tools like Talend and Informatica, scripting languages such as Python and PowerShell, and database technologies like SQL Server and Oracle. I also have experience with cloud-based integration platforms such as AWS Glue and Azure Data Factory.

I ensure data security by implementing encryption both at rest and in transit, using secure authentication methods, and adhering to data masking and anonymization practices. Compliance is maintained by following industry standards and regulations, such as GDPR and HIPAA, and by conducting regular audits and risk assessments.

For real-time data integration, I use technologies like Apache Kafka and Apache Flink to handle high-velocity data streams. I design the architecture to ensure low-latency processing and minimal data loss. Real-time data validation and transformation are also critical, which I achieve using stream processing frameworks and custom scripts.