Big Data Components

If we want to talk about Big Data, we have to talk about the elements it is composed of. We first have to collect data and then translated and stored. Then the final step is to analyze it so it is able to be presented in a comprehendible format.

1. Data sources (Ingestion)

The first step when it comes to collecting raw data is the ingestion layer. This layer comes from internal sources such as relational and non-relational databases. But, it can also come from other sources such as email, phone calls and social media. We can divide data ingestions into two types.

• Batch – here large data groups are collected and then delivered together. This collecting of data can be triggered by various conditions and even launched on a schedule.

• Streaming – this is a constant flow of data. It is important for real-time data analytics. The moment data is generated, it pulls it instantly. It needs more resources as it is constantly monitoring for changes.

Its goal is to get the data in the systems. The data here is neither organized nor parsed. That is why this comes with a lot of difficulties.

• Security and compliance – as a lot of data flows into these databases, it becomes an issue making sure that no data poses security problems. It is a hard thing to keep a track on as many legal regulations are impossible to apply to that data. The data has to follow the law but it is difficult as it comes in enormous quantities.

• Data Speed – a large number of data sources have different infrastructures when it comes to transporting data. If there happens to be a slow code, it will slow down the whole process and cause errors.

• Data Quality – a lot of the data that comes in is not as relevant as it seems. If there is too much of the irrelevant data, it can cause analysis issues and processing problems.

Organizing data

After the data successfully comes into the database, it has to be sorted next and translated appropriately. This has to be done before it is analyzed. Because of the fact that there is a lot of data that has to be analyzed, uniform organization is crucial.

As data comes in all kind of forms and formats, it is important for the ingestion layer to sort and organize all of the inbound data. This is not the same for all kinds of data, though.

If the data is unstructured, different types of data translation have to be applied. Natural language processing has to be used if the data comes from email, social media, letters and anything that is written.

If the data comes in the form of images and videos, techniques like log file parsing have to break down the pixels and audio. After the data has been successfully converted in a readable format, the next step is to organize it in a uniform schema.

We can say that the uniform schema is the set of defining characteristics of a dataset. For example, X and Y axes on a spreadsheet.

If the data is structured, this schema is all that is needed. If the data is unstructured or semi-structured, a lot of semantics have to be given before it can be organized. These semantics can come in the form of metadata and pre-loaded semantic tags.

After the data has been sorted, it has to be cleansed. What this means is that the database has to be clean of any irrelevant data.

2. Storage

The collected data has to be stored somewhere before it is processed. The data storage/ lake are considered as the most crucial part of the Big Data ecosystem. This is because it has to have clean and relevant data. This data is important as it is used to gain and make insights.

Data lakes are different from data warehouses as they conserve the original raw data that comes. This means that little to no transformation has been done. On the other hand, data warehouses are more concentrated on a specific task of the analyses. It is not as useful for other analysis efforts. This is why a lot of data warehouses contain a lot less data and produce faster results.

3. Analysis

Here is where all of the work is done. After the data has been found, collected and prepared the next step is to analyze it. In the analysis layer, data gets passed through several tools, shaping it into actionable insights. We can divide data analytics:

• Diagnostic

• Descriptive

• Predictive

• Prescriptive

Media and Entertainment

These three components are most important when it comes to managing Big Data properly. They are important as the data details complement one another in this process of data management.