Apache Tutorial: Learn all about Apache Spark updated 2019
Apache Spark is an open-source parallel process computational framework primarily used for data engineering and analytics. It was built on top of Hadoop MapReduce and it expands the MapReduce model to efficiently use additional styles of computations which incorporates Interactive Queries and Stream process. This is a brief tutorial that explains the fundamentals of Spark Core programming.
Topics covered in Apache Tutorial: Learn all about Apache Spark updated 2019
Introduction to Apache Spark
Applications on Apache Spark
Features of Apache Spark
Components of Apache Spark
Apache Spark Use Cases
Using Spark with Hadoop
6 Important Reasons to Learn Apache Spark
Introduction to Apache Spark
Apache Spark is an open-source cluster computing framework for real-time operation. Apache Spark is of the most successful projects in the Apache Software Foundation. Spark has evolved because of the market leader for large processing. Today, Spark is being adopted by major players like eBay, Amazon, and Yahoo! various organizations run Spark on clusters with thousands of nodes. Apache Spark was introduced by Apache Software Foundation for speeding up the Hadoop computational computing software process.
It is designed in such a way that it integrates with all the big data tools. Like spark will access any Hadoop information supply, also can run on Hadoop clusters. Furthermore, Apache Spark extends Hadoop MapReduce to the next level. That also includes iterative queries and stream processing.
One more common belief about Spark is that it is an extension of Hadoop but that is not true. Spark uses Hadoop in two ways – one is storage and the second is processing. Since Spark has its cluster management computation, it uses Hadoop for storage purposes only.
Applications on Spark
The spark could be a widely-used technology adopted by most of the industries. Let us explore a number of the outstanding Apache Spark applications are-
Machine Learning – Apache Spark is equipped with a scalable Machine Learning Library called MLlib that can perform advanced analytics such as clustering, classification, dimensionality reduction, etc. Some of the prominent analytics jobs like predictive analysis, customer segmentation, sentiment analysis, etc., make Spark an intelligent technology.
Fog computing – With the influx of big data concepts, IoT has acquired a prominent space for the invention of more advanced technologies. Based on the idea of connecting digital devices with the assistance of tiny sensors this technology deals with a large quantity of knowledge emanating from various mediums. This requires parallel processing that is never attainable on cloud computing. Therefore Fog computing that decentralizes the information and storage uses Spark streaming as an answer to the present drawback.
Event detection – The feature of Spark streaming allows the organization to stay track of rare and weird behaviors for protecting the system. Institutions like financial institutions, security organizations, and health organizations use triggers to detect the potential risk.
Interactive analysis – Among the most notable features of Spark is its ability to support interactive analysis. Unlike MapReduce that supports execution, Apache Spark processes data faster because of which it can process exploratory queries without sampling.
Features of Apache Spark
These are features of Apache Spark
- Swift Processing
- Dynamic in Nature
- In-Memory Computation in Spark
- Reusability
- Fault Tolerance in Spark
- Real-Time Stream Processing
- Lazy Evaluation in Apache Spark
- Support Multiple Languages
- Active, Progressive and Expanding Spark Community
- Support for Sophisticated Analysis
- Integrated with Hadoop
- Spark GraphX
- Cost-Efficient
Components of Apache Spark
The following illustration depicts the various components of Spark.
Apache Spark Core: Apache Spark Core is the underlying general execution engine for the Spark platform that all other functionality is made upon. It provides In-Memory computing, referencing datasets in external storage systems.
Spark SQL: Spark SQL is a component on top of Spark Core that introduces a new data abstraction referred to as Schema RDD, which provides support for structured and semi-structured data.
Spark Streaming: Spark
streaming leverages Spark Core are quick scheduling capability to perform
streaming analytics. It ingests data in mini-batches and performs RDD
(Resilient Distributed Datasets) transformations on those mini-batches of data.
MLlib (Machine Learning Library): Machine Learning Library is a distributed machine learning framework above Spark because of the distributed memory-based Spark architecture. According to benchmarks, it is done by the MLlib developers against the Alternating Least Squares (ALS) implementations. Spark MLlib is nine times faster than the Hadoop disk-based version of Apache Mahout (before Mahout gained a Spark interface).
GraphX: GraphX is a distributed graph-processing framework on top of Apache Spark. It provides an API for expressing graph computation that can model the user-defined graphs by using the Pregel abstraction API. It additionally provides an optimized runtime for this abstraction.
Apache Spark Use Cases
1. Some industry-specific Apache Spark Use Cases
- Apache Spark use cases in the Finance Industry
- Spark use cases in e-commerce Industry
- Spark use cases in Healthcare
- Spark use cases in Media & Entertainment Industry
- Spark use cases in Travel Industry
2. Chief deployment modules that prove Use Cases of Spark
- Data Streaming
- Streaming ETL
- Data Enrichment
- Trigger event detection
- Complex session analysis
- Machine Learning
- Classification
- Clustering
- Collaborative Filtering
- Interactive Analysis
- Fog Computing
Using Spark with Hadoop
The best segment of Apache Spark is its compatibility with Hadoop. As a result, this makes a very powerful combination of technologies. Here, we will be viewing how Spark can benefit from the best of Hadoop.
Hadoop components can be used beside Spark in the following ways:
HDFS: Spark will run on top of HDFS to leverage the distributed replicated storage.
MapReduce: Spark can be used along with MapReduce in the same or separate Hadoop cluster as a processing framework.
YARN: Spark
applications are often created to run on YARN (Hadoop NextGen).
Batch & Real-Time Processing: MapReduce and Spark are used together where MapReduce is used for batch processing and Spark is used for real-time processing.
6 Important Reasons to Learn Apache Spark
To learn additionally about Apache Spark follow this comprehensive guide. Here are some reasons to find out Apache Spark currently and keep yourself moving technically before others:
- High compatibility with Hadoop
- Hadoop is dwindling while Spark is sparking
- Increased access to Big Data
- High demand for Spark professionals
- Diverse
- Apache Spark to make Big Money
