Curse of Dimensionality:

One of the most commonly faced problems while dealing with data analytics problem such as recommendation engines, text analytics is high-dimensional and sparse data. At many times, we face a situation where we have a large set of features and fewer data points, or we have data with very high feature vectors. In such scenarios, fitting a model to the dataset, results in lower predictive power of the model. This scenario is often termed as…

In our last blog we saw the key benefits of Data Lake, but let’s deep dive in to the internals of a Data Lake via discussing the key considerations and compositions.

Architecture Considerations

Take in any solution considerations it is practical difficult to arrives with a one-size-fit-all architecture; hence it applies for a Data Lake too. Hence the Data Lake architecture considerations…