- Lectures 30
- Quizzes 0
- Duration 15 day
- Skill level Mid Level
- Language English
- Students 5
- Assessments Yes
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- Knowledge of Scala Language is essential.
Benefits to learn Apache Spark Framework
- Swift Processing Using Apache Spark, we achieve a high data processing speed of about 100x faster in memory and 10x faster on the disk. This is made possible by reducing the number of read-write to disk.
- Dynamic in Nature We can easily develop a parallel application, as Spark provides 80 high-level operators.
- In-Memory Computation in Spark With in-memory processing, we can increase the processing speed. Here the data is being cached so we need not fetch data from the disk every time thus the time is saved. Spark has DAG execution engine which facilitates in-memory computation and acyclic data flow resulting in high speed.
- Reusability we can reuse the Spark code for batch-processing, join stream against historical data or run ad-hoc queries on stream state.
- Fault Tolerance in Spark Apache Spark provides fault tolerance through Spark abstraction-RDD. Spark RDDs are designed to handle the failure of any worker node in the cluster. Thus, it ensures that the loss of data reduces to zero. Learn different ways to create RDD inApache Spark.
- Real-Time Stream Processing Spark has a provision for real-time stream processing. Earlier the problem with Hadoop MapReducewas that it can handle and process data which is already present, but not the real-time data. but with Spark Streamingwe can solve this problem.
- Lazy Evaluation in Apache Spark All the transformations we make in Spark RDD are Lazy in nature, that is it does not give the result right away rather a new RDD is formed from the existing one. Thus, this increases the efficiency of the system. Follow this guide to learn more about Spark Lazy Evaluation in great detail.
- Support Multiple Languages In Spark, there is Support for multiple languages like Java, R, Scala, Python. Thus, it provides dynamicity and overcomes the limitation of Hadoop that it can build applications only in Java.
- Active, Progressive and Expanding Spark Community Developers from over 50 companies were involved in making of Apache Spark. This project was initiated in the year 2009 and is still expanding and now there are about 250 developers who contributed to its expansion. It is the most important project of Apache Community.
- Support for Sophisticated Analysis Spark comes with dedicated tools for streaming data, interactive/declarative queries, machine learning which add-on to map and reduce.
- Integrated with Hadoop Spark can run independently and also on Hadoop YARN Cluster Manager and thus it can read existing Hadoop data. Thus, Spark is flexible.
- Spark GraphX Spark has GraphX, which is a component for graph and graph-parallel computation. It simplifies the graph analytics tasks by the collection of graph algorithm and builders.
- Cost Efficient Apache Spark is cost effective solution for Big dataproblem as in Hadoop large amount of storage and the large data center is required during replication.
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