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Introduction to the Databricks Lakehouse platform
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Data Warehouse:
Traditional data warehouses have been essential for Business Intelligence (BI), reporting, and Extract, Transform, Load (ETL) processes.
They excel in handling structured data and providing support for SQL-based queries.
Limitations include challenges with unstructured data at scale.
Data Lake:
Data lakes emerged as a solution for handling large volumes of structured, semi-structured, and unstructured data.
Enabled data science and machine learning workloads.
However, data lakes faced issues related to governance, quality, and performance for analytics due to the lack of structured organization.
Lakehouse:
The lakehouse architecture aims to combine the strengths of data warehouses and data lakes.
It addresses the limitations of traditional data warehouses for unstructured data and governance challenges in data lakes.
Key attributes include support for asset transactions, schema enforcement, open formats on low-cost object storage, and separation of storage and compute.
Unified support for batch processing, streaming, SQL, and machine learning workloads.
Enterprise capabilities such as security, access control, and auditing are emphasized for compliance and data governance.
Benefits of Lakehouse:
Radically simplified architecture by consolidating specialized systems.
Faster time to insight without data movement.
Cost savings by operationalizing a single system’s infrastructure.
Flexibility for diverse analytics, ranging from BI to AI and ML.
Limitations and Future Outlook:
Early in maturity, potential lag in query performance compared to older systems.
Need for better support for non-SQL tools and diverse user personas.
Anticipated improvements over time, closing gaps while retaining simplicity and cost benefits.
General Considerations:
Data lakehouse represents an evolutionary step, a common trend in the history of technology innovation.
Continuous improvement is expected to address current limitations and enhance overall system performance and usability.
Traditional data warehouses have been essential for Business Intelligence (BI), reporting, and Extract, Transform, Load (ETL) processes.
They excel in handling structured data and providing support for SQL-based queries.
Limitations include challenges with unstructured data at scale.
Data Lake:
Data lakes emerged as a solution for handling large volumes of structured, semi-structured, and unstructured data.
Enabled data science and machine learning workloads.
However, data lakes faced issues related to governance, quality, and performance for analytics due to the lack of structured organization.
Lakehouse:
The lakehouse architecture aims to combine the strengths of data warehouses and data lakes.
It addresses the limitations of traditional data warehouses for unstructured data and governance challenges in data lakes.
Key attributes include support for asset transactions, schema enforcement, open formats on low-cost object storage, and separation of storage and compute.
Unified support for batch processing, streaming, SQL, and machine learning workloads.
Enterprise capabilities such as security, access control, and auditing are emphasized for compliance and data governance.
Benefits of Lakehouse:
Radically simplified architecture by consolidating specialized systems.
Faster time to insight without data movement.
Cost savings by operationalizing a single system’s infrastructure.
Flexibility for diverse analytics, ranging from BI to AI and ML.
Limitations and Future Outlook:
Early in maturity, potential lag in query performance compared to older systems.
Need for better support for non-SQL tools and diverse user personas.
Anticipated improvements over time, closing gaps while retaining simplicity and cost benefits.
General Considerations:
Data lakehouse represents an evolutionary step, a common trend in the history of technology innovation.
Continuous improvement is expected to address current limitations and enhance overall system performance and usability.
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