At this moment I do not have a personal relationship with a computer.
– Janet Reno
There is often we hear terms like Data Lake, Delta Lake, and Data Lakehouse, which might be confusing at times. In this blog, we’ll demystify these terms and talk about the differences between each of the technologies and concepts,
Highlights
Key terms
- Staging Layer
- Persistent staging area
all different approaches to storing and managing large volumes of data. Here’s a brief overview of each with an example:
- Data Lake: A Data Lake is a centralized repository that allows organizations to store all their structured and unstructured data at any scale. Data Lakes are typically built on distributed storage systems such as Hadoop or Amazon S3 and can be accessed by a wide range of tools and applications. Data Lakes are used to store raw data without any predefined schema, making it easier to integrate new data sources.
Example: A retail company that stores all its transactional data, customer data, and marketing data in a central repository, and uses it for analysis and reporting.
- Delta Lake: Delta Lake is an open-source storage layer that provides ACID transactions, scalable metadata management, and data versioning for big data workloads. Delta Lake is built on top of existing data lake storage systems, and it provides features such as schema enforcement, data validation, and auto-compaction. Delta Lake is used to provide data reliability, consistency, and performance for data pipelines.
Example: An e-commerce company that uses Delta Lake to store and manage its product catalog, sales data, and customer feedback, and uses it to run machine learning models for product recommendations and customer segmentation.
- Data Lakehouse: Data Lakehouse is a combination of Data Lake and Data Warehouse architectures that combines the best of both worlds. Data Lakehouse provides the scalability and flexibility of a Data Lake with the performance and reliability of a Data Warehouse. Data Lakehouse provides features such as schema enforcement, query optimization, and indexing to provide fast and reliable access to data.
Example: A healthcare company that uses Data Lakehouse to store and manage patient data, medical records, and clinical trial data, and uses it to generate reports for regulatory compliance and research.
Details can be found here!
- Data Warehouse: A Data Warehouse is a central repository of structured data that is used for reporting and analysis. Data Warehouses are designed to support fast and complex queries, and they provide features such as data modeling, indexing, and query optimization. Data Warehouses are typically built on relational databases such as Oracle or SQL Server.
Example: A financial services company that uses a Data Warehouse to store and manage transaction data, customer data, and market data, and uses it to generate reports for risk management and compliance.
| Data Lake | Delta Lake | Lakehouse | Warehouse | |
| Data | Raw(Structured/Un-Structured) | Structured | Structured | Structured |
| Storage system | Build on Distributed FS | Build on existing Data Lake storage | Build on existing Data Lake storage | Build on existing Data Lake storage |
| Technology | BigData | Data Brics | Redshift/Snowflake/Informatica | |
| Schama enforced | No | Yes | Yes | Yes |
| ACID | No | Yes | Yes | No |
In summary, the choice between Data Lake, Delta Lake, Data Lakehouse, and Data Warehouse depends on the specific use case and requirements of the organization. Each architecture provides different features and benefits, and the right choice depends on factors such as scalability, performance, reliability, and cost.