Data Lake and Data Management

Data is everywhere and comes in many forms. Today, terabytes and petabytes of data are being generated every second, and finding storage solutions for these massive data volumes is of utmost importance. Complex machines and technologies now collect an incredible breadth of data; this is where the Data Lake and Data Warehouse come in. What are they? Let’s look at them in a none technical way. Date lake can be seen as an open mind, open to many things, many sources, unstructured and more compatible. A data warehouse can be seen as a closed mind; not available to many things, does not allow many sources, and is structured and less compatible. Data warehouses and Business Intelligence tools support reporting and analytics on historical data. In contrast, data lakes support newer use cases that leverage data for machine learning, predictions, and real-time analysis. Data Lake has emerged as a robust platform business can use to manage, mine, and monetise vast unstructured data stores for competitive advantage. As a result, the rate of adoption of Data Lake platforms by companies has increased dramatically. Data warehouses work well for specific workloads and use cases, and data lakes represent another option that serves other workloads. We are at a point now where we will be able to use data not only to review the past but understand the present and even to predict the future. The data and tools will continuously evolve to help us get there in almost real time. In reality, if you can have multiple uses of the same data. And so, you never know what you may use that data for. So, if you start with just getting the data in first and then figuring out what you want to do with it, it generally leads to a more positive outcome with that data.