Spark

Spark: 

Installing spark

1. Choose a Spark release: 3.1.2 (Jun 01 2021)3.0.3 (Jun 23 2021)
2. Choose a package type:  Pre-built for Apache Hadoop 3.2 and later  Pre-built for Apache Hadoop 2.7  Pre-built with user-provided Apache Hadoop  Source Code 
3. Download Spark: spark-3.1.2-bin-hadoop3.2.tgz
4. Verify this release using the 3.1.2 signatures, checksums and project release KEYS.

Note that, Spark 2.x is pre-built with Scala 2.11 except version 2.4.2, which is pre-built with Scala 2.12. Spark 3.0+ is pre-built with Scala 2.12.

Spark Applications:

1. Processing Streaming Data

The most wonderful aspect of Apache Spark is its ability to process streaming data. Every second, an unprecedented amount of data is generated globally. This pushes companies and businesses to process data in large bulks and analyze it in real-time. The Spark Streaming feature can efficiently handle this function. By unifying disparate data processing capabilities, Spark Streaming allows developers to use a single framework to accommodate all their processing requirements. Some of the best features of Spark Streaming are:

Streaming ETL – Spark’s Streaming ETL continually cleans and aggregates the data before pushing it into data repositories, unlike the complicated process of conventional ETL (extract, transform, load) tools used for batch processing in data warehouse environments – they first read the data, then convert it to a database compatible format, and finally, write it to the target database. 

Data enrichment – This feature helps to enrich the quality of data by combining it with static data, thus, promoting real-time data analysis. Online marketers use data enrichment capabilities to combine historical customer data with live customer behaviour data for delivering personalized and targeted ads to customers in real-time.

Trigger event detection – The trigger event detection feature allows you to promptly detect and respond to unusual behaviours or “trigger events” that could compromise the system or create a serious problem within it.

While financial institutions leverage this capability to detect fraudulent transactions, healthcare providers use it to identify potentially dangerous health changes in the vital signs of a patient and automatically send alerts to the caregivers so that they can take the appropriate actions.

Complex session analysis – Spark Streaming allows you to group live sessions and events ( for example, user activity after logging into a website/application) together and also analyze them. Moreover, this information can be used to update ML models continually. Netflix uses this feature to obtain real-time customer behaviour insights on the platform and to create more targeted show recommendations for the users.

2. Machine Learning

Spark has commendable Machine Learning abilities. It is equipped with an integrated framework for performing advanced analytics that allows you to run repeated queries on datasets. This, in essence, is the processing of Machine learning algorithms. Machine Learning Library (MLlib) is one of Spark’s most potent ML components.

This library can perform clustering, classification, dimensionality reduction, and much more. With MLlib, Spark can be used for many Big Data functions such as sentiment analysis, predictive intelligence, customer segmentation, and recommendation engines, among other things.

Another mention-worthy application of Spark is network security. By leveraging the diverse components of the Spark stack, security providers/companies can inspect data packets in real-time inspections for detecting any traces of malicious activity. Spark Streaming enables them to check any known threats before passing the packets on to the repository.

When the packets arrive in the repository, they are further analyzed by other Spark components (for instance, MLlib). In this way, Spark helps security providers to identify and detect threats as they emerge, thereby enabling them to solidify client security.

3. Fog Computing

Fog Computing decentralizes data processing and storage. However, certain complexities accompany Fog Computing – it requires low latency, massively parallel processing of ML, and incredibly complex graph analytics algorithms. Thanks to vital stack components like Spark Streaming, MLlib, and GraphX (a graph analysis engine), Spark performs excellently as a capable Fog Computing solution.