Spark is an open-source cluster of computer frameworks that optimally process real-time data. The system significantly contributes to the real-time analytics of data collected hence the proficiency of certain tech-based companies, such as Amazon. It is easier to use Spark to scrutinize current data to determine a pattern and establish measures for short-term changes and improvements (Dayananda, 2020). Therefore, the mainframe operates based on data partitioning across the short span overview.
Spark’s Operability and The Features
Technological advancement intensified the use of social media platforms among people globally. The concept led to the emergence of an international village, boosting the amount of data collected per minute across different domains. An excellent example is that Facebook collects 4,166,667 posts while Instagram follows with a recorded 1,716,111 data per minute (Dayananda, 2020). In this case, organizations use the Spark system since it efficiently compiles real-time data and relays optimal analysis. However, functionalism relies on the ability to partition the dataset into small packs for effectiveness and feasibility. Spark’s features include polyglot, speed, multiple formats, lazy evaluation, real-time computation, machine learning, and Hadoop integration (Hajoui & Talea, 2018). The interoperability of the distinctive elements fosters prominent output and preferential baseline of Spark technology by various companies.
Interdependence between Spark and Hadoop
Spark works with Hadoop due to the necessity to compound datasets for optimal analysis. The main aim of both systems encapsulates establishing distinctive factors influencing the productivity and scalability of the concepts. The key difference between Hadoop and Spark is the approach to the data analytics process. On the one hand, the Hadoop structure only processes a batch of datasets meaning that it is efficient in analyzing information stored for a longer period of time. On the other hand, Spark is a system that performs its tasks on a real-time basis while optimally using dynamic batches for excellent outcomes. Apache Spark processes data one hundred times faster than the Hadoop MapReduce tool, thus increasing the data analytics mainframe (Dayananda, 2020). Therefore, Spark is an improved Hadoop framework, prioritizing other organizations, namely Amazon and Google.
Spark’s Components
Different components of the Spark technology contribute to the efficiency scale during dataset processing. The distinctive apparatus enshrine core, streaming, GraphX, SQL, and MLlib (machine learning) (Dayananda, 2020). It is the responsibility of technical experts to incorporate the intersectionality and operability of the contraptions to boost the effectiveness. The Spark core acts as the processor’s baseline engine, hence providing a platform for the ETL application development (Hajoui & Talea, 2018). The main role of Spark streaming engulfs the processing of real-time incoming data. Spark SQL plays a vital lead by rendering effective transformation from queries to different codes as a tool. GraphX contributes to the Spark API’s graph-parallel computations, hence the significance of the component in the system. The machine learning device enhances the flow of operations based on the recorded processes and coded platforms.
MongoDB and Its Operability
MongoDB is an open-source document database that is utilized in the development of dynamic internet applications. The system significantly contributes to the effective storage of data across different platforms and in various formats. Unlike the SQL database, the MongoDB develops a binary representation of the dataset as a document mainly named BSON, and software retrieves the document in the JSON format (Hajoui & Talea, 2018). Notably, it is an approach that fosters the retention and protection of the details among key players based on a stable and standardized document format for applications. The core benefit of using MongoDB is the ability to increase the database pool both vertically and horizontally without compromising the structure and performance of the framework. Therefore, it is a prominently flexible mainframe for application and internet developers in utilizing crucial databases for programming.
Below is an example of the JSON document demonstrating a historical figure.
{
“_id”: 1,
“name”: {
“first”: “Ada”,
“last”: “Lovelace”
},
“title”: “The First Programmer”,
“interests”: [“mathematics”, “programming”]
}
Recommendations to the CEO
I would recommend that the CEO approves the use of both Hadoop and Spark due to the optimal efficiency and scalability provided across different fields of operations. While Hadoop focuses on a batch of databases collected over a period of time, Spark optimizes on the real-time dataset. However, Spark performs a hundred times faster than Hadoop but with a partitioned database (Dayananda, 2020). It is vital to intersect the systems’ functionalities since they elevate productivity under various groundworks, thus intensifying the necessity for competitive advantage using technological tools. Ideally, Spark and Hadoop complement each other under the spectral view of dynamism in data analysis on real-time and batch datasets.
Although MongoDB and DynamoDB offer high efficiency in data storage, the former poses significant relevance. It is recommended that the CEO uses MongoDB since it provides a stable and standard format for documents during storage and extraction process. DynamoDB is a system mainly used by Amazon Web Services to manage stored data in the cloud. However, the framework is optimally limited to the internet domain than MongoDB, that is easily accessible to various parties. Therefore, it is crucial that the CEO invests in the MongoDB system to boost the security level in dataset storage and analysis across distinctive platforms.
References
Dayananda, S. (2020). Spark Tutorial | A Beginner’s Guide to Apache Spark | Edureka. Edureka. Web.
Hajoui, O., & Talea, M. (2018). Which NoSQL database to combine with spark for real time big data analytics?. International Journal of Computer Science and Information Security (IJCSIS), 16(1). Web.