Advantages of Apache Spark
Now, let us discuss the advantages of Apache Spark.
Developer Friendly
Apache Spark has developer-friendly tools. It has APIs that are easy to work with over large datasets. It has above 80 high-level operators making complex computations fast and making parallel simulations.
Speed
Apache Spark is quick. And when dealing with Big Data, computation time matters greatly. It uses RAM(in-memory) computation. It can work with petabytes of data faster than Hadoop.
Advanced Analytics
Along with ‘MAP’ and ‘reduce’, it supports ML(Machine Learning) algorithms, graphs, SQL queries, streaming data, etc.
Dynamic
It is dynamic. It can make applications run in parallel. It is because Apache Spark offers more than 80 high-level operators.
Multi-Language engine
It supports multiple languages like Java, Scala, Python, etc.
Powerful Engine
It has low latency in-memory data processing ability and thus can handle analytical and computational challenges. It has built libraries for ML and graph algorithms.
Disadvantages of Apache Spark
Let us discuss some drawbacks of Apache Spark.
File Management System
It does not have a file management system of its own. It depends on other cloud-based systems or Hadoop for file management.
Lesser Algorithms
The Machine Learning library MLib in Apache Spark has fewer algorithms. The number of available algorithms is less.
Manual Optimization Process
There is no automatic optimization process in Apache Spark. You have to optimize the code or dataset by yourself. A fixed number of partitions is required for parallelization. This is to be fixed and passed manually.
Small File Issues
If one uses Apache Spark with Hadoop, small file problems arise. This is because Hadoop Distributed File System supports only a limited number of large files and a large number of small files.
Window Criteria
All the data in Apache Spark is divided into time intervals. So, there will be time-based criteria rather than record-based criteria.
Handling Back Pressure
Back Pressure occurs when much data is stacked in the input/output switch when the buffer is already full to receive any data. Once the buffer is empty, only then further data is processed. Apache Spark cannot deal with this back pressure automatically, which is to be done manually.
Also see, Recursive Relationship in DBMS
Frequently Asked Questions
What is Apache Spark?
Apache Spark is an engine for data analysis for large-scale datasets. It is a multi-language and open-source system. It is an interface for data science to program clusters, data parallelism, and fault tolerance. It executes machine-learning algorithms on single-node machines.
What is the primary difference between Apache Spark and Hadoop?
The main difference is that Apache Spark has MLib, its machine-learning library. In contrast, Hadoop is interfaced with external sources for it. Also, Apache Spark is faster than Hadoop.
What is the main use of Apache Spark?
The primary use of Apache Spark is that it is an open-source engine that is a processing system for big data workloads. It uses in-memory caching and does query optimization for fast analysis of data.
What is the main difference between Spark and Apache Spark?
Spark is a Framework that comes under Microframeworks. In comparison, Apache Spark is an engine for the analysis of big data workloads. It is a Big Data Tool.
What are the disadvantages of Apache Spark?
It has no file management system of its own, no real-time processing support, has issues with small files, and has a lesser number of algorithms. These are the key disadvantages of Apache Spark.
Conclusion
Apache Spark is an engine for the analysis of data for large-scale datasets. It is a multi-language and open-source system. In this article, we studied the benefits and drawbacks of Apache Spark.
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