The 7 Costly and Complex Challenges of Big Data Analytics` highlights seven major challenges that enterprise DevOps teams, SREs, and data engineers face when navigating the growing costs and complexity of big data analytics. These challenges include `Data Pipelines`, where modern tools often run into scalability issues; `Data Preparation`, which can consume up to 60-75% of resources in an organization's data analytics program; `Data Destination` and `Governance`, where data governance strategy and controls must span the entire data pipeline, but localized around the final destination element. The challenges also include `Data Platforms`, where most platforms provide a trade-off between generic power and flexibility; `Data Analytics`, where operational and business analytics are often run separately by two different departments with big data silos; and `Data Lifecycle`, where long-term data retention is a complex challenge to manage at scale. ChaosSearch addresses these challenges by leveraging the power of cloud object storage, delivering an idealized data destination and optimized approach to governance, and transforming your cloud storage into a hot analytical data lake.