What are the two important stages of data analytics?

Data analytics involves several stages, and two key stages essential to the overall process are data ingestion and data processing.

Data ingestion is the initial stage where raw data is collected from various sources. This could include databases, cloud storage, application logs, or streaming sources. The goal of this stage is to gather and consolidate data for further analysis. Effective data ingestion is critical because it ensures that all relevant data is captured, which sets the foundation for subsequent analysis.

Following data ingestion is data processing, which transforms the ingested raw data into a format that is suitable for analysis. This may involve cleaning the data, filtering out irrelevant information, aggregating data points, and performing various calculations or transformations. The processing stage is vital as it helps to prepare the data, making it more manageable and easier to work with when it comes time for analysis.

Together, data ingestion and data processing establish a strong foundation for meaningful insights in data analysis, ultimately enabling effective decision-making based on the processed information.