Apache Spark: Real spark of the Big Data World
For the analysis of big data, the industry is extensively using Apache Spark. It is a project of Apache, popularly known as “lightning fast cluster computing”. What is Apache Spark? Spark is a general-purpose distributed data processing engine which provides a number of inter-connected platforms, systems and standards for Big Data projects. It was originally developed at UC Berkeley in 2009. Spark lets you run programs up to 100 times faster than Hadoop. Programming languages supported by Spark include: Java, Python, Scala, and R. Tasks most frequently associated with Spark include ETL and SQL batch jobs across large data sets, processing of streaming data from sensors, IoT, or financial systems, and machine learning tasks. Apache Spark is widely considered as the future of Big Data Platform. In this blog, we will discuss the various aspects of why Apache Spark is gaining more importance in the big data industry.
Key benefits of using Spark: Let’s go through some of its features which are really highlighting it in the Big Data world!
Ease of Use: Spark has easy-to-use APIs for operating on large datasets. These APIs are well-documented and structured in a way that makes it straightforward for data scientists and application developers to quickly put Spark to work.
Lightning Fast Processing: Using Apache Spark, we achieve a high data processing speed of about 100x faster in memory and 10x faster on the disk. It is because Hadoop use local memory space to store data but Spark uses in- memory (RAM) computing system.
Polyglot: Spark supports a range of programming languages, including Java, Python, R, and Scala. Furthermore, the Apache Spark community is large, active, and international.
Dynamic: We can easily develop a parallel application, as Spark provides 80 high-level operators.
In-Memory Computation: Instead of using local memory space for computation Spark uses in-memory computation which allow user to process data on ram which can be easily accessed when needed for use.
Re-usability: We can reuse the Spark code for batch-processing, join stream against historical data or run ad-hoc queries on stream state.
Fault Tolerant: Spark has fault tolerance capabilities because of immutable primary abstraction named RDD.
Real-Time Stream Processing: Spark helps us to analyze real time data as and when it is collected. This overcomes the limitation with Hadoop MapReduce was that it can only do batch processing, but not the real-time data. However, with Spark Streaming we 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.
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’s framework is built on top of the Hadoop Distributed File System (HDFS). So it’s advantageous for those who are familiar with Hadoop. MapReduce and Spark are used together where MapReduce is used to batch processing and Spark for real-time processing.
Spark GraphX: Spark has GraphX, which is a component for graph and graph- parallel computation. It simplifies the graph analytics tasks by its rich collection of graph algorithm.
Cost Efficient: Apache Spark is cost effective solution for Big data problem as in Hadoop large amount of storage and the large data center is required during replication.
Conclusion: Spark streaming houses within it the capability to recover from failures in real time. However, it falls though some pitfalls such as it provides near-real time processing, does not have a file management system, creates problem with small files. Hence, there are various other technologies which can overcome these. For instance, stream processing with Flink is Real Time due to its underlying architecture. Here is the list of top few Apache Spark Books:
Learning Spark: Lightning-Fast Big Data Analysis
High-Performance Spark: Best Practices for Scaling and Optimizing Apache Spark
Apache Spark Graph Processing
Advanced Analytics with Spark: Patterns for learning from Data at Scale
Spark GraphX in Action
Big Data Analytics with Spark
Here is the list of few helpful Apache Spark Certifications:
HDP Certified Apache Spark Developer
O’Reilly Developer Certification for Apache Spark
Cloudera Spark and Hadoop Developer
Databricks Certification for Apache Spark
MapR Certified Spark Developer