A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.
Why do you need a data lake?
Organizations that successfully generate business value from their data, will outperform their peers. An Aberdeen survey saw organizations who implemented a Data Lake outperforming similar companies by 9% in organic revenue growth. These leaders were able to do new types of analytics like machine learning over new sources like log files, data from click-streams, social media, and internet connected devices stored in the data lake. This helped them to identify, and act upon opportunities for business growth faster by attracting and retaining customers, boosting productivity, proactively maintaining devices, and making informed decisions.
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Characteristics |
Data Warehouse |
Data Lake |
|
Data |
Relational from transactional systems, operational databases, and line of business applications |
Non-relational and relational from IoT devices, web sites, mobile apps, social media, and corporate applications |
|
Schema |
Designed prior to the DW implementation (schema-on-write) |
Written at the time of analysis (schema-on-read) |
|
Price/Performance |
Fastest query results using higher cost storage |
Query results getting faster using low-cost storage |
|
Data Quality |
Highly curated data that serves as the central version of the truth |
Any data that may or may not be curated (ie. raw data) |
|
Users |
Business analysts |
Data scientists, Data developers, and Business analysts (using curated data) |
|
Analytics |
Batch reporting, BI and visualizations |
Machine Learning, Predictive analytics, data discovery and profiling |