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What is the purpose of a Read-Only Replica in the context of database connections?
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A Read-Only Replica reduces the load on the primary database, allowing reports to access a data copy without impacting the main transactional database's performance.
How does a Data Mesh facilitate 'Self-Serve Infrastructure'?
By allowing each domain to provision its own resources, a Data Mesh supports internal teams in managing their data-related tasks without needing central IT intervention.
How do OLAP systems benefit self-service BI using their distinct characteristics?
OLAP systems provide a simpler and intuitive data model due to data denormalization, enabling straightforward queries and making it easier for users to carry out self-service BI.
What are the limitations of data lakes in comparison to traditional data warehouses?
Data lakes, while flexible in storing unstructured data, often face issues with transactional support and concurrency, unlike traditional data warehouses.
What are the primary differences between OLTP and OLAP systems?
OLTP is transactional with fast response times for handling individual row operations and is normalized. OLAP is analytical, working with large datasets for analysis and is typically denormalized to support efficient querying.
Describe the hybrid approach of a Modern Data Warehouse.
A Modern Data Warehouse combines the benefits of data lakes and relational warehouses by ingesting data into a data lake, processing it with platforms like Databricks, and then storing it in a structured warehouse.
How does a Data Lakehouse improve upon traditional data storage and processing methods?
A Data Lakehouse merges the benefits of data lakes and databases by using formats like Delta, offering a single data copy with a SQL endpoint that provides user-friendly access and integrates both structured and unstructured data.
Explain the concept of 'Schema on Read' in data lakes.
In a data lake, 'Schema on Read' means that the data structure or schema is applied dynamically when the data is accessed, rather than when it is stored.
What was the historical significance of data warehouses originating in the 1980s?
Data warehouses were crucial for structured data handling, transforming data from source systems to provide integrated, consistent, and reliable reports for businesses.
What does treating 'Data as a Product' entail in a Data Mesh?
It means ensuring that data outputs are reliable, well-tested, high-quality, and easily accessible to meet user needs, much like any commercial product would be.
What are the key challenges associated with using traditional relational data warehouses?
Traditional relational data warehouses are slow to adapt to changes due to schema requirements and are not optimized to handle the volume, variety, and velocity of big data.
Why is it generally not ideal to connect reporting solutions directly to source databases?
Direct connections can result in complex queries and slow performance due to the normalized data structure and may risk blocking key OLTP operations, thereby impacting the main income sources.
Why does the Data Mesh approach emphasize 'Domain Ownership'?
Domain Ownership allows each business domain to manage its data processing, ensuring that experts closest to the data can maintain its quality and relevance.
What is 'Federated Governance' in a Data Mesh and why is it important?
Federated Governance maintains consistent standards and policies across diverse datasets and domains, ensuring cohesion and data quality without centralized control.
What are the four key principles of a Data Mesh?
The four principles are: Domain Ownership, treating Data as a Product, providing a Self-Serve Infrastructure, and maintaining Federated Governance.
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