As more and more data getting generated in these days, we require efficient Big Data applications to process this enormous amount of data for valuable insights. Even though numerous open source and commercial platforms are available in the current market such as Apache Hadoop, Apache Tez, Apache Flink, Apache Spark, and others, however, among them, Apache Spark consulting is capturing much attention. What’s that core which makes Spark extremely popular? Here are the top 8 cutting-edge features:
1) Spark replaces Hadoop:
The Hadoop has been supported by My Reduce programming model, which facilitate the smooth processing of massive data and is capable of writing data analysis tasks in multiple languages such as Python, Java, Scala, Ruby, and C++ with the help of 80 high-level operators.
2) Spark well-suitable for HDFS:
The Spark is capable of working with Hadoop Distributed File System (HDFS) developed by the Apache Foundation (vanilla HDFS), Hortonworks (HDP), Cloudera (CDH) and other Hadoop vendors. The usage CDH (Cloudera Distributed Hadoop) and HDP (Hortonworks Data Platform) definitely illustrates the most significant integration points that exist between these data platforms and Spark. The inherent flexibility in terms of distributed file system usage makes Spark stand out among numerous available commercial HDFS alternatives at the business as well as technical front.
3) Spark capable of running on YARN (Yet Another Resource Negotiator):
It is one of the notable integration points that offered by both Cloudera as well as Hortonworks between Hadoop and Spark open-source distributed frameworks. The workloads of Spark can utilize scheduling policies of Symphony and execute those on YARN. Today, several feasible and fascinating YARN alternatives exist which are completely independent setups as they don’t include Work-Load Manager components. Most of the latest decentralized alternatives such as Sparrow (pdf) are best suitable for managing parallel workloads with lower latency. For instance, IBM’s Platform Symphony supported by an Application Service Controller can easily deploy Spark via Docker platform.
4) Spark can’t be fully monitored and managed:
Irrespective of file systems and workload managers, in most cases, the deployment of Spark mainly remains as a manual process as it requires to be installed as well as configured through manual work. Although it meant that Spark application has to be built from the basic point, however, Cloudera Manager comes to rescue as it offers advanced software management construct also known as parcels. With the support of SaaS-based technique, Databricks attempted to address this challenge associated with monitoring and management of Spark. Though Apache Ambari – management toolkit of Hortonworks does not need to address this problem, however, Bright Cluster Manager is capable of deploying Spark within a bare metal environment. Specially built to fit Spark’s requirements along with Spark’s specific-metrics, Bright surely will find the best solution for Spark’s effective management as it the need of the hour.
5) Spark offers proficient analytics platform:
Well-equipped with powerful MLlib, Application Programming Interface (Open API) for graph analytics, and SQL-based streaming applications, Spark provides a converged platform for analytics that enables the users to write their own code for workflows with the help of multiple languages such as Scala, Java, and Python. These workflows can be easily executed in both batches as well as real-time processing modes using the in-built interactive support. As the Spark is supported by notable R Stats Package component, it can easily access any data source of the Hadoop such as HDFS, Apache Cassandra, and Apache HBase. This data can be utilized via various Spark workflows as well as applications.
6) Spark owns swift data processing reputation:
Spark offers high-speed data processing as compared to Hadoop as it facilitates in-house memory storage of binary data and efficient use of that memory. In addition, Spark is supported by Resilient Distributed Datasets (RDDs). As indicated by the name, RDDs are comparatively advanced abstraction available in the market for in-memory computing and are fault tolerant/parallel data structures best suitable for in-memory cluster computing. These RDDs are distributed across the infrastructure of a Big Data to ensure the optimal placing of the data and minimum loss of data in case any of the work nodes fails within the cluster.
7) Spark witnessing excellent adoption:
Ever since its inception, Spark is witnessing impressive growth as most of the well-established organizations are incorporating Spark into their infrastructure. As in the current situation, Spark cannot be overlooked, few of the renowned players within the ecosystem of Hadoop are forced to ‘compete’ cooperatively. On the one hand, integration issues associated with workload managers, data sources, and management are absolutely non-controversial, while the synergies related to analytic applications such as machine learning, graphs, and streaming still remain problematical. In these days, as well-established enterprise-software vendors, namely, Microsoft, IBM, HP, Oracle, and SAP are re contextualizing themselves for Big Data Analytics, the Spark’s disruptive impact might influence substantially on this industry.
8) Spark’s outcomes are exceptionally phenomenal:
Spark offers outstanding advantages when compared to Hadoop for example, utilization of binary data, in-memory HDFS, high-speed data processing, user-friendly APIs, and efficient security.
In recent times, outstanding advantages such as scalability, flexibility, and easy-to-use applications make Spark the “Next Big Thing” in the sector of information technology. Most of the well-established organizations have been integrated Spark into their infrastructure and several firms are researching on the consequences of its adoption via Google as suggested by Google Trends. It seems that, sooner or later, Spark will emerge as the most significant platform for Big Data.