You're facing unexpected technical issues in data integration. How will you navigate through the challenges?

Note: due to limit size of answers, feedback to the question is structured in two responses data_integration 1 of 2 - challenges of data integration data_integration 2 of 2 - best practices --- data_integration 1 of 2 --- As prerequisite: it is assumed that data integration process fits in the overall data management strategy. Most common challenges in data integration: - ever increasing data volumes - diverse data sources - haybrid cloud and on-premises environements - poor or inconsistent data quality -> accuracy, timeliness, completness, consistency - multiple use cases - monitoring and obeservability - handling streaming data - mixed tool sets and architecture

Root Cause Analysis: Investigate to identify the root cause of the issue, whether it’s related to software, hardware, data format, or external systems. Consult Logs: Review error logs, system alerts, and any relevant documentation to pinpoint the problem.

--- data_integration 2 of 2 --- One way to navigate above challenges, is to apply best practices, like - process data as close as possible to the data source - document integration processes and catalog integrated data (data catalogs) - Ensure your data integration processing is nondestructive (in datasets) - structure data lakes with a landing zone for raw data, staging area and integrated data area - focus on data integration techniques rather than tools - especially on design and/or prototyping phase(s) - use logging to track and monitor integration processes - use data observability techniques to ensure availability and quality of data are documented -> observability pillars: data quality, lineage, schema, volume and fressness

Develop a Strategy: Based on the identified cause, formulate a plan of action to address the issue. Consider both short-term fixes and long-term solutions. Involve Stakeholders: Communicate with relevant stakeholders, including IT teams and business users, to gather input and ensure alignment on the proposed plan.

Implement Fixes: Apply the necessary fixes or adjustments to the data integration process. This might involve patching software, correcting data formats, or reconfiguring settings. Test Changes: Thoroughly test the solution in a controlled environment before full implementation to ensure it resolves the issue without introducing new problems.