Big data is a term used to refer to datasets that are too large or complex for traditional data processing applications. It is used to describe the massive volume of structured and unstructured data that is being generated from sources such as social media, online transactions, and satellites. Exploring big data and mining for information often leads to the discovery of powerful new insights.

If your new to big data here’s a few things you need to consider:

  1. Storage: The first challenge is accessing the dataset typically >1TB so downloading directly to a regular computer is not feasible in most situations. Accessing the data is usually done remotely from the source or using cloud-based storage solutions such as Amazon S3.

  2. Cleaning up the data: Assuming the data collection was done properly, there should be a lot of useful insights to be discovered in the data. Since big data is significantly bigger than normal data there will also be a large amount of noise / low quality data. We can clean this up

  3. Data visualisation: Once we have access to our stored data, we need to see what we’re working with. Data visualisation involves creating visual representations of data, such as charts, graphs, and maps, making it easier to identify patterns. This can help to uncover insights that may not be immediately apparent from the raw data or tables. But due to the sheer size of the data to improve the visibility of clusters and their separation, quality filtering can be applied to the data. For example, say one of your columns measures uncertainty if that uncertainty is similar in magnitude to the measurement, that data point is low quality and can be filtered out.

  4. Analysis: Now that we can see our data clearly, we need to extract some meaningful insights. Data analysis involves using statistical and computational techniques to uncover patterns and relationships within the data. This can involve anything from simple line of best fit to complex machine learning algorithms. The goal of data analysis is to identify key insights that can be used to inform decision-making and drive action. With big data, analysis can be a time-consuming and resource-intensive process, but the potential benefits are significant. By extracting insights from big data, we can gain a deeper understanding of complex phenomena.

  5. Choosing the right tools: There are various tools and technologies available to help process and analyze big data, including Hadoop, Spark, and NoSQL databases. Choosing the right tool depends on the specific needs of the project and the type of data being analyzed. Other tools such as TOPCAT are used in more niche areas such as Astronomy research.

In conclusion, big data is transforming the way we approach decision-making and problem-solving. It presents exciting opportunities for businesses, researchers, and individuals to gain new insights and make better-informed decisions. By understanding the challenges and considerations involved in working with big data, we can harness its power to drive innovation and growth beyond current boundaries.z