Apache Spark- a unified big data platform
Are you known to the myths like, data is the new raw material, data is the fuel of the 21st century? Then, you are familiar with the importance of data. Data has changed the way of doing business. And, most importantly it is not limited to the IT and related sectors but, has spread wings across the entire business sectors.
What is Big data platform?
Big data means a large volume of data that is produced in business activities and can be of type structured or unstructured. Big data platforms are tools that combine several big data applications along with its features and capabilities and come up with a single solution.
What is Apache Spark?
Apache Spark is a unified Big data platform that performs Big data processing and distribution of data processing tasks across different systems. These systems can be of its own or other distributed computing tools. It provides easy-to-use APIs so that programmers can easily give relief for working in distributed computing and big data processing.
The AMP Lab product invented at UC Berkeley,since its invention in 2009, has been one of the leading Big data distributed processing frameworks. The banking sector, gaming companies, telecommunication companies, and other technology giant names like Apple, Facebook, Microsoft have relied on this miracle framework.
Apache Spark ecosystem
The Apache Spark is in-built with additional libraries other than core API. They are an integral part of the Apache Spark that will provide additional capabilities for Big data analytics and machine learning.
- Spark Streaming
- Spark SQL
- Spark MLib
- Spark GraphX
Apache Spark Architecture
Apache Spark offers easy-to-use, high-level APIs in various leading programming languages like Java, Python, R, and Scala, and SQL. Apache Spark architecture is based on three major components:
- Data storage
- Managed framework
Apache Spark architecture has two major abstractions:
- Resilient Distributed datasets (RDD)
- Directed Acrylic Graph (DAG)
Apart from this, Apache Spark can also run in a standalone cluster mode that runs on the Apache Spark framework and a JVM on every machine of the cluster.
Also Read: Introducing Apache Spark 3.0 on Qubole
What are the best features of Apache Spark?
Apache Spark is widely popular because of its exceptional features. It has dominated the industry and left behind the other competitors like Hadoop, Storm also. Check out some striking features of Apache Spark:
- Ultra-fast processing speed
- Easy-to-use API
- Real-time stream processing
- Boost for machine learning
- Advanced analytics
- Flexibility and reusability
- Fault tolerance
Apache Spark Use cases
The widely spread e-commerce industry can be a great use case of Apache Spark. The real-time transactions are processed, filtered and results are then combined with other unstructured data sources. The output of this can be used for improving and adapting recommendations over time and that too with the latest market trends.
In the finance or security industry, Apache Spark can be applied to identify fraud or intrusion detection.
In the gaming industry, it can be used to process and discover patterns and respond instantly. It helps in player retention, auto-adjusting complexity level, and target advertising, and so on.
Apache Spark is a new emerging big data platform that has gained applause from most of the business users. Its lightning speed, flexible and developer-pro features contribute for being a most sellable option for machine learning, stream processing, and other important aspects.