What you can do? You can achieve faster results, which were very difficult than the traditional approaches. If you want to perform some kind of machine learning algorithms on top of large datasets. Then you should definitely go for Spark. And use the library of Spark which has certain built in machine learning capabilities, forgetting the result.
Data processing in Apache Spark?
More Introduction to Apache Spark is that first the data is ingested. We perform some kind of data cleaning some kind of transformation. Which is required for running our algorithms. Any kind of reprocessing which is required for running our machine learning algorithms. Then we'll perform some kind of training of on the basis of our machine learning model. At the end, we'll be performing some kind of testing and validation depending upon the model selection. Finally, we'll be deploying our application. This is how the flow of execution takes place in Apache Spark.
Data Frame and Dataset:
What is a Data frame and dataset? What is the difference between them? The difference between them is not much to be worried about. For Example, you can say that you have three cars.
- Car A
- Car B
- Car C
Now I can see that, I have a dataset of car names. If you consider it as a data. This data type is string. You can take this as a dataset. Dataset is a collection of data of some specific. Now, what is data frame. We increase our dataset.
| Car Name | Color | Model |
| Car A | Red | 2012 |
| Car B | Blue | 2009 |
| Car C | Black | 2014 |
In this data, we can say that, Column 1 (Car name) is a string type of data or this is a dataset and its type are string.
If we consider all columns, that is a data frame, it is also a data set and its type is row. That is what Spark considers it. When we define the data.