Due to the increased computing cost of analysis and the difficulty in extracting meaningful insights from the data, high cardinality data might be more challenging to efficiently analyse than lower cardinality data.
The “cardinality” of a set is the number of its individual parts. Information on the weather with only a handful of values such as bright, overcast, rainy, etc. or the ages of a group of people with only a handful of distinct age groups would both be examples of low cardinality data sets.
What Makes High Cardinality A Problem?
You should know why high cardinality is a problem. When there are many distinct values for a certain column or attribute in a dataset, we say that it has high cardinality. While the increased specificity that comes with a high cardinality might be useful, it also has the potential to complicate data processing and storage. Some challenges may arise due to a high cardinality, and they include the following:
Demand for More Room
When a feature has a high cardinality, more storage space is required because each value must have its own dedicated spot. This becomes a significant problem when dealing with massive datasets.
Decreased Query Performance
Queries’ performance may suffer, especially when joining tables or filtering based on attributes with high cardinality. It is necessary for the database to handle a considerable number of unique values, which could slow down the rate at which data is retrieved.
Statistics demonstrates that a skewed data distribution, in which certain values appear less frequently than others, can result from attributes with a large cardinality. As a result, doing trustworthy statistical analysis or arriving at actionable insights may prove difficult.
In machine learning, the “curse of dimensionality” describes a potential problem with the usage of high cardinality features. As the number of features diminishes, the difficulty of building reliable models and spotting significant patterns rises. Data practitioners frequently resort to dimensionality reduction, feature selection, or feature engineering when confronted with characteristics with a high cardinality.
The Best Approach
Improving the effectiveness of full table scans lies at the centre of the present strategy for addressing challenges with large cardinality data analytics?
The practice of defining data as vectors (arrays of numbers) and then conducting mathematical operations on those vectors in order to solve problems is one way to increase the efficiency of entire table scans. In the corporate world, this tactic for boosting performance is known as vectorization, which is another name for data level parallelism.
Instead of processing queries on separate data elements, a vectorized query engine processes queries in parallel on the vectors that make up the data. These are referred to as vectors and have a predetermined length. Algorithms have progressed from processing a single value at a time to processing multiple values simultaneously, which greatly improves data processing speeds. It’s the capacity to carry out a single mathematical operation on a list (or “vector”) of numbers in a single step at any given time. This makes it possible for the query engine to process several data elements simultaneously, which improves the efficiency of table scans overall.
Conclusion
Traditional distributed analytic databases, on the other hand, do the processing locally on each node, row by row. Overall, this approach takes longer and uses more computational power. Using advances in graphics processing units (GPUs) and vectorized central processing units (CPUs), Kinetic is able to analyse data in massive chunks, allowing for faster and more accurate query execution. This drastically reduces the time and effort needed to perform joins on extremely large datasets with a high cardinality.
