Migrating to a data lake can be considered for centralizing large volumes of structured and unstructured data, enabling advanced analytics such as AI. A successful migration involves several key considerations including architectural decisions that ensure scalability, cost-effectiveness, and compatibility with the organization's data needs. Key architectural decisions include cloud storage options like AWS S3, Azure Data Lake Storage, or Google Cloud Storage, table formats such as Iceberg or Delta Lake, catalogs like AWS Glue or Unity Catalog, and query engines like Snowflake or Databricks. An interoperable approach is crucial to ensure flexibility in tailoring the architecture to specific needs of the team and use cases. Fivetran's data lake architecture is designed for interoperability, supporting both Iceberg and Delta formats, and allowing integration with third-party catalogs like AWS Glue or Fivetran's Iceberg REST Catalog. Once set up, a data lake can be populated with data using tools like Fivetran Managed Data Lake Service, which provides a straightforward setup guide and supports historical syncs on SaaS and database sources as well as syncing directly from data warehouses. Querying options for existing data stacks include flexible query integration patterns, an Extract, Load, Transform (ELT) approach, and dbt Core-compatible data models that can be used with supported query engines like BigQuery or Databricks. With modern tools and technologies, migrating to a data lake has never been easier, offering automation, flexibility, and interoperability.