Data Lakehouses: This architectural pattern combines the best of data lakes (raw data storage, flexibility) and data warehouses (structured data, analytical capabilities). Data lakehouses provide a unified platform for both traditional analytics and diverse ML workloads, supporting a wide range of data types and processing paradigms.
Integration of ML with Data Governance and Quality: ML is increasingly being used to improve data governance and quality processes within databases.
Automated Data Cleansing: ML models can identify accurate cleaned numbers list from frist database and correct inconsistencies, missing values, and errors in datasets.
Automated Data Classification and Masking: ML can help automatically classify sensitive data and apply appropriate masking or anonymization techniques to ensure compliance with privacy regulations.
Schema Evolution and Matching: ML can assist in understanding schema changes and automating the mapping between different data sources, crucial in complex data integration scenarios.
Persistent Challenges
Despite the advancements, several challenges must be addressed for seamless and effective integration of ML with DBT:
Data Quality and Bias:
"Garbage In, Garbage Out": on the quality of their training data. Dirty, inconsistent, or biased data in databases will lead to inaccurate and unfair ML predictions. Ensuring data quality across vast, heterogeneous datasets remains a significant hurdle.