Support for Several Programming Languages : Spark code can be written in any of the four programming languages, namely Java, Python, R, and Scala. It also provides high-level APIs in these programming languages. Additionally, Apache Spark provides shells in Python and Scala. The Python shell is accessed through the ./bin/pyspark directory, while for accessing the Scala shell one needs to go to the .bin/spark-shell directory.
Speed : For large-scale data processing, Spark can be up to 100 times faster than Hadoop MapReduce. Apache Spark is able to achieve this tremendous speed via controlled portioning. The distributed, general-purpose cluster-computing framework manages data by means of partitions that help in parallelizing distributed data processing with minimal network traffic.
Machine Learning : For big data processing, Apache Spark’s MLib machine learning component is useful. It eliminates the need for using separate engines for processing and machine learning.
Lazy Evaluation : Apache Spark makes use of the concept of lazy evaluation, which is to delay the evaluation up until the point it becomes absolutely compulsory.
Multiple Format Support : Apache Spark provides support for multiple data sources, including Cassandra, Hive, JSON, and Parquet. The Data Sources API offers a pluggable mechanism for accessing structured data via Spark SQL. These data sources can be much more than just simple pipes able to convert data and pulling the same into Spark.
Real-Time Computation : Spark is designed especially for meeting massive scalability requirements. Thanks to its in-memory computation, Spark’s computation is real-time and has less latency.
Hadoop Integration : Spark offers smooth connectivity with Hadoop. In addition to being a potential replacement for the Hadoop MapReduce functions, Spark is able to run on top of an extant Hadoop cluster by means of YARN for resource scheduling.
Supports Spark GraphX for graph parallel execution, Spark SQL, libraries for Machine learning, etc.
Active Developer’s Community : Apache Spark has a large developers base involved in continuous development. It is considered to be the most important project undertaken by the Apache community.
Cost Efficiency : Apache Spark is considered a better cost-efficient solution when compared to Hadoop as Hadoop required large storage and data centers while data processing and replication.
