Data storage in native format – A data lake eliminates the need for data modeling at the time of ingestion. We can do it at the time of finding and exploring data for analytics. It offers unmatched flexibility to ask any business or domain questions and to glean insights.
Scalability – It offers scalability and is relatively inexpensive compared to a traditional data warehouse when we take scalability into account.
Versatility – a data lake can store multi-structured data from diverse sources. In simple words, a data lake can store logs, XML, multimedia, sensor data, binary, social data, chat, and people data.
Schema Flexibility – Traditionally schema necessitates the data to be in a specific format. For OLTP (Application Data), this is great as it validates data before entry. But for analytics, it’s an obstruction as we want to analyze data as is. Traditional data warehouse products are schema based. But Hadoop data lake allow you to be schema free, or you can define multiple schemas for the same data. In short, it enables you to decouple schema from data, which is excellent for analytics.
Supports not only SQL but more languages – Traditional data-warehouse technology mostly supports SQL, which is suitable for simple analytics but for advanced use cases, we need more ways to analyze data. Data lake provides various options and language support for analysis. It has Hive/Impala/Hawq which supports SQL but also has features for more advanced needs. For example, to analyze the data in a flow, you can use PIG or to do machine learning you can use Spark MLlib.
Advanced Analytics – Unlike a data warehouse, a data lake excels at utilizing the availability of large quantities of coherent data along with deep learning algorithms. It helps in real-time decision analytics.