Data observability tools are essential for modern data engineering, providing broad visibility into the health and state of data within a system. These tools help identify, predict, and prevent data issues in real-time, ensuring high-quality and error-free data. With so many options available, choosing the right tool can be challenging. Key features to look for include end-to-end connectivity, automated monitoring, broad coverage of data quality dimensions, rich context for root cause analysis, incident management and alerting, proactive testing and prevention, scalability and performance, ease of use, and customization capabilities. Top data observability tools in 2025 include Metaplane, Monte Carlo, Bigeye, Soda, Great Expectations, Datafold, and Acceldata. When choosing a tool, consider your primary goals, tech stack compatibility, team size and expertise, budget and pricing model, balance automation vs control, and try before you buy. Data observability tools address challenges such as data downtime, reduced productivity due to reactive troubleshooting, complexity in modern data environments, and the cost of bad data. By adopting the right tool, organizations can catch issues before they impact users, understand root causes faster, and deliver more reliable data products.