seq-datasource-v2 VS Apache Spark

PySpark is the Python API for Apache Spark, an open-source distributed computing system that enables fast, scalable data processing. PySpark allows Python developers to leverage the powerful capabilities of Spark for big data analytics, machine learning, and data engineering tasks without needing to delve into the complexities of Java or Scala.

If you’re stepping into the world of Big Data, you have likely heard of Apache Spark, a powerful distributed computing system. PySpark, the Python library for Apache Spark, is a favorite among data enthusiasts for its combination of speed, scalability, and ease of use. But setting it up on your local machine can feel a bit intimidating at first.

According to the Apache Spark official website, PySpark lets you utilize the combined strengths of ApacheSpark (simplicity, speed, scalability, versatility) and Python (rich ecosystem, matured libraries, simplicity) for “data engineering, data science, and machine learning on single-node machines or clusters.”

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Apache Spark is a powerful and widely used framework for distributed data processing, beloved for its efficiency and scalability. At the heart of Spark’s magic lies the RDD, an abstraction that’s more than just a mere data collection. In this blog post, we’ll explore why RDDs are immutable and the benefits this immutability provides in the context of Apache Spark.

The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as we’ve explored here, but Ray Clusters can also be created independent of Kubernetes.

We all know how easy it is to overlook small parts of our code, especially when we have powerful tools like Apache Spark to handle the heavy lifting. Spark's core engine is great at optimizing our messy, complex code into a sleek, efficient physical plan. But here's the catch: Spark isn't flawless. It's on a journey to perfection, sure, but it still has its limits. And Spark is upfront about those limitations, listing them out in the documentation (sometimes as little notes).