What is a Data Lake?
A data lake is a centralized repository for all manner of structured, semi-structured, and unstructured data. It stores data in its original format with no transforming or cleansing and can cost-effectively scale to meet enterprise organizations’ needs.
A data lake’s primary goal is to provide data scientists and analysts with a single repository of all the organization’s data for deep analysis. Data lakes are rising in popularity, with some market analysts attributing nearly 33% of the value chain market to them.1
What is a Data Lakehouse?
A data lakehouse brings order to a data lake by enabling a modeled, relational representation of data stored in a data lake. It combines the flexibility and scalability of a data lake with the structure and management features of a data warehouse in a simple, open platform.
In contrast to a traditional data warehouse, a data lakehouse uses data directly from a data lake without requiring a copy, reducing data redundancy.
A data lakehouse’s primary goal is to provide analysts and report writers with a semantic, reportable layer of data. This allows them to combine disparate data sets and build and distribute reports that support organizational operations.
Benefits of a Data Lake + Data Lakehouse
Implementing a data lake enables an organization to quickly create a centralized repository of its data. Loading data into a data lake is straightforward and only requires a connection to the source systems.
Instead of a traditional ETL (Extract, Transform, Load) framework, a data lakehouse uses ELT (Extract, Load, Transform). Data transformation is handled by analysts, data scientists, and report writers after the data is loaded. This approach makes data available quickly, though it does require later modeling and cleansing for effective reporting.
Cloud storage is relatively inexpensive, making large-scale cloud storage an increasingly attractive option for enterprises.2 A data lake and lakehouse in the cloud can be far more cost-effective than traditional data warehouse storage.
For example, Microsoft Azure offers five terabytes (TB) of storage for approximately $200 per month.3 By comparison, storing five TB in a traditional data warehouse such as Azure Synapse Analytics can cost up to $1,200 per month.4
Data lakes also support machine learning (ML) and artificial intelligence (AI). Platforms like Azure provide tools that make ML and AI faster to implement, while big data processing tools such as Hadoop and Spark can run directly on top of a data lake.
Analysts, data scientists, and report writers can connect to both raw and modeled data using analytics tools like Power BI . With Azure Data Lake and Lakehouse, native Power BI connectors make it easy to locate and analyze data files.
A data lake’s design is driven by available data rather than predefined reporting requirements or fixed technology choices. Adding new data is simply a matter of ingestion, making data lakes an efficient way to expand access for analytics teams as needs evolve.
In Summary
The primary benefits of a data lake are centralized data storage and broad reporting support. While these benefits can contribute to organizational growth,5 a data lake alone should not be viewed as a replacement for a traditional data warehouse.
However, when paired with the computational power of a data lakehouse, this architecture can support most operational and analytical needs at a fraction of the cost of a traditional data warehouse.
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References
- Data Lake Market to hit US $24,308 million by 2025 (2020). Adroit Market Research. https://www.globenewswire.com/news-release/2020/11/24/2132790/0/en/Data-Lake-Market-to-hit-US-24-308-0-million-by-2025
- Data Storage Trends in 2020 and Beyond (2019). Spiceworks. https://www.spiceworks.com/marketing/reports/storage-trends-in-2020-and-beyond/
- Microsoft Azure Storage Pricing (2021). https://azure.microsoft.com/en-us/pricing/details/storage/
- Microsoft Azure Synapse Analytics Pricing (2021). https://azure.microsoft.com/en-us/pricing/details/synapse-analytics/
- Lock, M. (2017). Angling for Insight in Today’s Data Lake. Aberdeen Research. PDF
