32 Questions
What enabled the data lakehouse?
All of the above
What is the primary function of metadata layers in data lakes?
To track which files are part of different table versions
What is a major limitation of traditional data lakes?
They lack critical features from data warehouses
What is a benefit of data lakehouses for data scientists and machine learning engineers?
They can access the data in the lakehouse using popular tools
What is a common issue with two-tier data architectures?
Duplicate data, extra infrastructure cost, and significant operational costs
What is the primary purpose of data warehouses?
To support decision support and business intelligence applications
What is a benefit of optimized access for data science and machine learning tools in data lakehouses?
Improved reproducibility in machine learning
What is the purpose of the ETL process in a two-tier data architecture?
To load data from the data lake to the data warehouse
What is a key factor that enables data lakehouses to achieve performance on large datasets?
All of the above
What is the main advantage of data lakehouses over traditional data warehouses?
They can unify data warehousing and advanced analytics
What is the primary benefit of combining data lakes and data warehouses in a data lakehouse?
Enabling business intelligence and machine learning on all data
What is the primary characteristic of the storage used in a data lakehouse?
Low-cost and high-performance
What is the main advantage of merging data lakes and data warehouses into a single system?
Data teams can move faster and access data more easily
What is the primary goal of data lakehouses in terms of data availability?
Providing access to the most complete and up-to-date data
What is the key benefit of using a data lakehouse for data science and machine learning projects?
Access to complete and up-to-date data
What is the primary characteristic of a data lakehouse in terms of its architecture?
Open system design
Match the following data management systems with their primary characteristics:
Data Lakehouse = Combines flexibility and cost-efficiency of data lakes with data management of data warehouses Data Warehouse = Implements strict data management and ACID transactions Data Lake = Uses low-cost storage for large datasets Traditional Data Lake = Has limitations in terms of data management and ACID transactions
Match the following benefits with their corresponding systems:
Faster data access = Data Lakehouse Cost-effective storage = Data Lake Enhanced data management = Data Warehouse Improved data availability = Data Lakehouse
Match the following data lakehouse features with their descriptions:
Open system design = Enables implementation of data structures and data management features on low-cost storage ACID transactions = Ensures data consistency and reliability Low-cost storage = Allows for cost-effective data storage Metadata layers = Provides additional context to data
Match the following data science and machine learning applications with their benefits in a data lakehouse:
Faster data access = Enables faster machine learning and data science projects Complete and up-to-date data = Ensures accuracy of machine learning models Merging data lakes and warehouses = Provides a single system for data science and machine learning Optimized access = Improves performance of data science and machine learning tools
Match the following data management systems with their primary challenges:
Traditional Data Lake = Limited data management and ACID transactions Data Warehouse = High cost and complexity Data Lakehouse = Requires careful governance and management Two-tier Data Architecture = Inefficient data access and processing
Match the following data lakehouse benefits with their corresponding outcomes:
Improved data availability = Enhances business intelligence and decision-making Faster data access = Accelerates data science and machine learning projects Reduced costs = Low cost storage and infrastructure Simplified governance = Easier management and oversight of data
Match the following data storage systems with their primary purposes:
Data Warehouses = Business intelligence and decision support applications Data Lakes = Handling raw data in various formats for data science and machine learning Data Lakehouses = Unifying data warehousing and advanced analytics in a single system Operational Databases = Running applications and storing operational data
Match the following technologies with their roles in data lakehouses:
Metadata layers = Tracking file versions and enabling features like ACID-compliant transactions Optimized query engines = Enabling high-performance SQL execution on data lakes Open data formats = Facilitating access for data science and machine learning tools Vectorized execution = Improving query performance on modern CPUs
Match the following challenges with their corresponding data storage systems:
Expensive for handling unstructured data = Data Warehouses Lacking critical features like transactions and data quality = Data Lakes Duplicate data and extra infrastructure cost = Two-Tier Data Architecture Limited performance on large datasets = Traditional Data Lakes
Match the following benefits with their corresponding data storage systems:
Easy access for data scientists and machine learning engineers = Data Lakehouses Cost-effective storage for large datasets = Data Lakes Support for business intelligence and decision support applications = Data Warehouses Improved data consistency and isolation = Data Lakehouses
Match the following components with their roles in the data lakehouse architecture:
Open file formats = Enabling access for data science and machine learning tools Metadata layers = Tracking file versions and enabling features like ACID-compliant transactions Optimized query engines = Improving query performance on large datasets Data Validation = Ensuring data quality and consistency
Match the following limitations with their corresponding data storage systems:
Slow access to data = Traditional Data Lakes Limited support for machine learning and data science = Data Warehouses Lack of data consistency and isolation = Data Lakes High cost for handling large datasets = Data Warehouses
Match the following benefits with their corresponding data storage systems:
Improved data reproducibility = Data Lakehouses Cost-effective storage for large datasets = Data Lakes Support for business intelligence and decision support applications = Data Warehouses Easy access for data scientists and machine learning engineers = Data Lakehouses
Match the following technologies with their roles in improving query performance:
Caching hot data in RAM/SSDs = Improving query performance on large datasets Data layout optimizations = Clustering co-accessed data for faster queries Vectorized execution = Improving query performance on modern CPUs Auxiliary data structures = Providing statistics and indexes for faster queries
Match the following data storage systems with their primary characteristics:
Data Warehouses = Structured data storage for business intelligence Data Lakes = Unstructured data storage for data science and machine learning Data Lakehouses = Unified storage for data warehousing and advanced analytics Operational Databases = Transactional storage for running applications
Match the following limitations with their corresponding data architectures:
Duplicate data and extra infrastructure cost = Two-Tier Data Architecture Limited performance on large datasets = Traditional Data Lakes Lack of data consistency and isolation = Data Lakes High cost for handling large datasets = Data Warehouses
Study Notes
What is a Data Lakehouse?
- A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses.
- It enables business intelligence (BI) and machine learning (ML) on all data.
Key Features of a Data Lakehouse
- Combines the benefits of data lakes (flexibility, cost-efficiency, and scale) with the data management and ACID transactions of data warehouses.
- Enables business intelligence (BI) and machine learning (ML) on all data.
- Provides a single system for data teams to work on, eliminating the need to access multiple systems.
- Offers complete and up-to-date data for data science, machine learning, and business analytics projects.
Evolution of Data Storage
- Data warehouses: limited ability to handle unstructured data, semi-structured data, and data with high variety, velocity, and volume.
- Data lakes: emerged to handle raw data in various formats on cheap storage, but lacked critical features from data warehouses (transactions, data quality, consistency/isolation).
- Data lakehouses: combine the benefits of data lakes and data warehouses, enabling a single system for data teams.
Key Technology Enablers
- Metadata layers (e.g. Delta Lake) for data lakes, providing rich management features like ACID-compliant transactions.
- New query engine designs enabling high-performance SQL execution on data lakes.
- Optimized access for data science and machine learning tools.
Benefits of Data Lakehouses
- Performance: achieves performance on large datasets that rivals popular data warehouses.
- Simplified data access: easy for data scientists and machine learning engineers to access data in the lakehouse.
- Improved reproducibility: audit history and time travel features help with improving reproducibility in machine learning.
Challenges of Two-Tier Data Architecture
- Duplicate data, extra infrastructure cost, security challenges, and significant operational costs.
- Multiple ETL steps leading to data staleness, a significant concern of data analysts and data scientists.
Learn about the evolution of data storage, from data warehouses to data lakes and data lakehouses, and the key technologies enabling this shift. Explore metadata layers, query engine designs, and optimized access for data science tools.
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