The top 15 methods to know in Apache Beam to transform your data.

Learning to transform your data in a pipeline

Apache Beam is a unified programming model for creating large-scale, parallel data-processing pipelines. It provides a high-level abstraction for expressing data-processing pipelines as a series of steps, allowing users to express complex data-processing pipelines in a concise and declarative way. Apache Beam pipelines are composed of multiple steps, called PCollections, which are lazily evaluated and can be chained together in a variety of ways. Apache Beam provides a number of built-in PCollections for common data-processing tasks, such as filtering, mapping, and sorting. It also provides a number of built-in operators for combining PCollections, such as union, intersection, and difference.

In this article, we will discuss the top 15 functions to use in Apache Beam. These functions are essential for any Apache Beam developer and will help you create efficient and scalable data-processing pipelines.

1. map - The map function is used to apply a function to each element in a PCollection. The function is applied to each element in turn, and the result is a new PCollection containing the results of the function. You can also use flatmap The FlatMap function is similar to the Map function, but it maps each input element to zero or more output elements
2. filter - The filter function is used to remove elements from a PCollection that do not meet a certain criteria. The criteria is specified as a function, and the function is applied to each element in turn. If the function returns true, the element is kept in the PCollection. If the function returns false, the element is removed from the PCollection.
3. reduce - The reduce function is used to combine elements in a PCollection into a single value. The function is specified as a binary function, and the function is applied to each pair of elements in turn. The result is a single value, which is the result of applying the function to each pair of elements.
4. groupByKey - The groupByKey function is used to group elements in a PCollection by their key. The key is a function, and the function is applied to each element in turn. The result is a PCollection containing a group of elements for each key.
5. join - The join function is used to join two PCollections based on their keys. The keys are specified as a function, and the function is applied to each element in both PCollections. If the function returns the same key for both elements, the elements are joined in the resulting PCollection.
6. window - The window function is used to divide a PCollection into windows based on a time or size criteria. The time or size criteria is specified as a function, and the function is applied to each element in turn. The result is a PCollection containing a window of elements for each element in the original PCollection.
7. orderByKey - The orderByKey function is used to order a PCollection by key. The key is a function, and the function is applied to each element in turn. The result is a PCollection containing the elements in sorted order by key.
8. serde - The serde function is used to serialize and deserialize elements in a PCollection. The serialization and deserialization functions are specified as a function, and the function is applied to each element in turn. The result is a PCollection containing the elements in serialized and deserialized form.
9. cache - The cache function is used to cache the results of a PCollection. The results are cached in memory, and the PCollection can be retrieved from the cache in subsequent runs of the pipeline.
10. snapshot - The snapshot function is used to take a snapshot of a PCollection. The snapshot is stored in a file, and the PCollection can be retrieved from the snapshot in subsequent runs of the pipeline.
11. zip - The zip function is used to combine two PCollections into a single PCollection. The elements in each PCollection are combined into a single element in the resulting PCollection.
12. union - The union function is used to combine two PCollections into a single PCollection. The elements in both PCollections are included in the resulting PCollection.
13. intersection - The intersection function is used to combine two PCollections into a single PCollection. The elements that are present in both PCollections are included in the resulting PCollection.
14. difference - The difference function is used to combine two PCollections into a single PCollection. The elements that are present in the first PCollection but not in the second PCollection are included in the resulting PCollection.
15. write - The write function is used to write the contents of a PCollection to a destination, such as a file or a database.

While this is not everything to know in Apache Beam this will tackle a lot of use cases you will run into. Learning these will get you well on your path to using your data in data analytics or data science.