Introducing Apache Spark

To work with the huge amount of information available to modern consumers, Apache Spark was created. It is a distributed, cluster-based computing system and a highly popular framework used for big data, with capabilities that provide speed and ease of use, and includes APIs that support the following use cases:

• Easy cluster management
• Data integration and ETL procedures
• Interactive advanced analytics
• ML and deep learning
• Real-time data processing

It can run very quickly on large datasets thanks to its in-memory processing design that allows it to run with very few read/write disk operations. It has a SQL-like interface and its object-oriented design makes it very easy to understand and write code for; it also has a large support community.

Despite its numerous benefits, Apache Spark has its limitations. These limitations include the following:

• Users need to provide a database infrastructure to store the information to work with.
• The in-memory processing feature makes it fast to run, but also implies that it has high memory requirements.
• It isn't well suited for real-time analytics.
• It has an inherent complexity with a significant learning curve.
• Because of its open source nature, it lacks dedicated training and customer support.

Let's look at the solution to these issues: Azure Databricks.