![]() Integration with different data sources through connectors.Structured, semi-structured, and unstructured data support.The preliminary validation involves checking for the elementary features that every data lakehouse provides. Organizations in the banking and financial services industry receive data from internal and external sources with varied formats and frequencies. Identifying the right data lakehouse solution When it comes to the adoption of data lakehouses, organizations must begin with choosing the right platform from a market that is inundated with solutions, including Google BigQuery, Apache Drill, Snowflake, Amazon Athena, Delta Lake, and Microsoft Azure Data Lake. ![]() Deployed both on-premises and in the cloud, data lakehouses cater to banking and insurance use cases centered around customer centricity, regulatory adherence, and product innovation. While it can be integrated with traditional business intelligence, data lakehouses can also serve as the foundation for artificial intelligence, machine learning, data science, and other data analytics solutions. A hybrid implementation of a data warehouse within a data lake, this novel data repository utilizes the cloud to store data in a cost-effective way. In recent times, data lakehouses have emerged as the preferred data platform architecture option for the BFSI industry. ![]()
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