As a massive data processing engine, Apache Spark is a fantastic choice due to its many features. These characteristics demonstrate Apache Spark's superiority to competing Big Data processing engines. This article will discuss "What are the Features of Apache Spark?". Join Spark Training Institute in Chennai at FITA Academy and acquire knowledge on all the aspects of Spark.
Fault Tolerance
Apache Spark is built to handle failed worker nodes. RDDs (Resilient Distributed Datasets) and DAG are used to achieve this fault tolerance. The lineage of each transformation and action required to execute a job is contained in the DAG. Therefore, the same outcomes can be obtained by repeating the steps from the existing DAG in the event of a worker node failure.
Dynamic nature
More than 80 high-level operators provided by Spark make it simple to create parallel applications.
Lazy Evaluation
Spark does not immediately analyze any transformation. A lazy evaluation is performed on each transition. The DAG is updated with the transformations; only actions are called to access the final computation or results. Because all the modifications are accessible to the Spark engine before taking any action, Spark can make optimization decisions.
Real-Time Stream Processing
With the help of Spark Streaming, Apache Spark's language-integrated API can be used to construct streaming jobs in the same way that batch jobs are written.
Speed
Hadoop applications can now run up to 100 times quicker in memory and up to 10 times faster on disc because of Spark. To do this, Spark reduces the number of disc read/write operations for intermediate results. It keeps information in memory and only uses the disc when necessary. DAG, a query optimizer, and a highly optimized physical execution engine are used by Spark to accomplish this.
Reusability
Spark code can be utilized for batch processing, ad hoc streaming state queries, and merging streaming data with historical data. You can join Spark Training Academy Chennai and learn all the concepts of Spark.
Advanced Analytics
The official standard for big data processing and data sciences across numerous industries has quickly been established as Apache Spark. Companies from several industries use Spark's machine learning and graph processing libraries to solve challenging issues. The strength of Spark and highly scalable clustered processors make all of this simple to accomplish. With Spark, Databricks offers an advanced analytics platform.
Supporting Multiple Languages
Spark generally supports multiple languages. Most Java, Scala, Python, and R APIs are present. R language for data analytics also has additional functionalities. Additionally, Spark has SparkSQL, which has a feature similar to SQL. Because of this, it is straightforward to use for SQL developers, and the learning curve is significantly lowered.
Integrated with Hadoop
Apache Spark and Hadoop's HDFS file systems work together very effectively. It supports various file formats, including Parquet, JSON, CSV, ORC, and Avro. Spark can simply use Hadoop as an input data source or as a data destination.
Cost Efficient
Since Apache Spark is free software, there are no licencing costs. Users only need to worry about the expense of the tools. Due to its built-in support for stream processing, machine learning, and graph processing, Apache Spark also lowers many additional costs. Organizations can choose from a broad capacity of Spark capabilities based on their specific use cases because there are no vendor-specific restrictions on Spark.
Spark is the most sophisticated and well-liked Apache solution for processing big data. It features many components for processing structured and unstructured data, streaming, and machine learning. If you want to learn Spark, join Spark Course in Chennai, which provides the best Certification Training with Placement Support.
