Did you know that the distinctions between data lakes and data lakehouses could significantly impact your Salesforce data management strategy?
1. Data Lakes
– Store vast amounts of raw data in its native format.
– Ideal for large-scale data storage and batch processing.
– Supports multiple data types including structured, semi-structured, and unstructured data.
2. Data Lakehouses
– Combines the benefits of data lakes and data warehouses.
– Provides structured data management for both analytics and machine learning.
– Ensures ACID transactions for reliable data operations.
3. Key Comparisons
– Data Lakes are scalable and cost-effective but may lack immediate data consistency.
– Data Lakehouses offer real-time data access with robust performance but can be more complex to implement.
– Data governance and security are more advanced in lakehouses due to integrated data management features.
4. Use Cases
– Data Lakes: Ideal for data scientists needing vast raw datasets for exploratory analysis.
– Data Lakehouses: Suitable for organizations requiring integrated data workflows for both analytics and operational processes.
Understanding these differences can help Salesforce Admins choose the right data architecture to enhance data handling and analytical capabilities.
You can read it here: https://sfdc.blog/dvdoZ
Source from salesforceben(dot)com