The key benefit of a data lake is that you can store any and all data in one place incurring a low cost, pulling it as analytical needs arise. But what can an organization gain from all of this data in one place? Let’s see!
A data lake can make data available to the whole organization. It is what we call data democratization. Currently, only the top executives have the luxury to ask various departments for reports, get a sense of things from those and then make a decision. But what about the middle management and others? They don’t have the luxury to ask for all kinds of data they need from other departments. Even if they eventually get the data, it will cost time.
We know if we take a lot of time in making decisions, it can render the whole exercise futile. With necessary data readily available, all can take viable decisions at their level. For example, the Janitorial staff of a unit can decide what supplies to buy based on the price of the supplies and their needs. A real-world example of enabling everyone to make their own decisions is LinkedIn. On LinkedIn, everyone decides whom they want to connect with, what content they want to see. More benefits are listed below.
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.
It offers scalability and is relatively inexpensive compared to a traditional data warehouse when we take scalability into account.
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.
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 allows 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.
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. A 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.
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.