What tools can you use to handle data migration errors and exceptions?

Tools like Informatica Data Quality or Trifacta for data quality assurance, Microsoft Data Migration Assistant or AWS Database Migration Service for database migrations, and Apache NiFi or Talend for ETL operations are good choices for handling data migration faults and exceptions. Use monitoring software for real-time error tracking, such as Splunk or ELK Stack. For effective mistake prevention and recovery, make use of version control systems like Git, backup plans, and automated testing tools like Selenium. Slack and other communication technologies enable prompt issue resolution. These tools, when used in a comprehensive manner, guarantee dependable and effective data migration procedures.

Managed full-cycle data migration using Microsoft BI tools. Leveraged SSIS for ETL processes, ensuring data integrity via meticulous cleansing. Utilized SSMA for seamless data transfer to SQL Server, minimizing downtime. Monitored migration with SSMS and Azure Monitor, quickly identifying and resolving issues. Employed Azure DevOps for version control, enhancing project coordination. Finalized by deploying Power BI dashboards for real-time migration tracking, post-migration analytics and QA.

Expert in utilizing Microsoft BI and Azure for data transformation and migration. Orchestrated complex ETL tasks using SSIS, ensuring seamless data integration into SQL Server. Leveraged Azure Data Factory and Data Lake for robust, large-scale data processing. Employed Azure SQL Database for high-performance data handling, complemented by Azure Analysis Services for agile data modeling. Skillfully utilized Power Query and DAX in Power BI for advanced data shaping and real-time insights. Streamlined the entire process with Azure Synapse Analytics, integrating data warehousing and Big Data analytics for comprehensive data management.

Data transformation technologies are essential for resolving data migration issues. Strong ETL capabilities are offered by programs like Talend and Apache NiFi, which make data loading, transformation, and extraction easier. These tools have error handling features to efficiently handle exceptions and provide visual interfaces for creating data flows. Teams can use them to assure compatibility during migration, validate content, and alter data types. The success of data transfer initiatives is aided by the efficient use of such data transformation technologies, which also improve data quality and make mistake resolution easier.

Tools like Glue are proven ETL technologies and there is no doubting their ability to extract, transform and load, and at scale. Glue for example is built upon Apache Spark, which has established itself as the bastion of Bug Data ETL. Spark comes into its own though when combined with open source libraries like Amazon's Deequ, Great Expectations or Splink, which together offer leading capabilities for ensuring data quality, regardless of the need to harden ETL data pipelines or even test the quality of data.

I have found iCEDQ a powerful tool in data testing. Why risk your ETL project? We should test data pipelines before production. iCEDQ software automates 100% end-to-end ETL testing. Enabling you to test complex data transformation, test billions of records with no sampling needed. iCEDQ also helps with identifying process dependency issues, identifying Data Mapping Errors. Some of the core features of iCEDQ is - - Automated test generation and powerful scripting - In-memory and spark based cluster processing - Seamless integration with 150+ connectors - Fully compatible with CI/CD pipelines

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