Apache Spark is an open source processing engine used for faster performance, ease of use and sophisticated analytics. Apache Spark can collectively process huge amount of data present in clusters over multiple nodes. Parallel processing framework of Apache Spark enables users to run large scale data analytics applications. It supports in-memory processing boosting the performance of applications based on big data analytics; however, it can also perform disk-based processing when available system memory is unable to store the data sets.
Spark has evolved as an effective alternative for Hadoop. Its speed of processing is high compared to Hadoop as a result of its bottom-up engineering. Its popularity has increased recently for on-disk sorting, involving large data sets. Easy-to-use application program interface (API) is developed to handle large amount of data. It includes multiple operators for manipulating semi-structured data by revamping all the information and familiar data APIs. At the heart of Apache Spark is a unified engine, it includes support for SQL queries, higher-level libraries, machine learning, streaming data and graph processing. Also, these libraries can be combined seamlessly to create complex workflows. Apache Spark can be deployed in the cloud on the Amazon Elastic Compute Cloud (EC2) service or as a standalone application. Due to its advanced features and functionality, popularity of Apache Spark has increased within the developers, integrators and end-users. It supports multiple languages so that the developers can write applications in Java, Python, Scala or R, further increasing the popularity of Apache Spark. Furthermore, adoption and deployment of Spark has been faster as it came on the back of Hadoop. It integrates seamlessly with Hadoop data sources such as Hadoop distributed file system (HDFS), Hive, HBase and Cassandra and Hadoop ecosystem. Spark has matured and it has become a mainstream solution at a perfect time when Internet of Things (IoT) devices are proliferating in the market. IoT devices are anticipated to drive the Apache Spark market during the coming years as the need for processing large data sets is expected to increase.
Apache Spark supports advanced analytics such as streaming data, machine learning (ML), SQL queries and graph algorithms. These four components also form the core of Apache Spark. Full recovery from faults and failures is possible as the objects are stored in resilient distributed datasets (RDD). Real-time queries are enabled with the help of Apache Spark, increasing the efficiency of the data processing system. Spark clearly differentiates between importing data and distributed computation. With quick and iterative product development it will reduce the time-to-market for new products. Also, prototyping of solutions without the need of submitting the code every time improves the iterative development and feedback process. Decentralization of data center functions such as storage and processing has resulted in a new concept of fog computing. Demand for Apache Spark is projected to rise in the near future as the popularity of fog computing is anticipated to increase.
Major players associated with the Apache Spark market include IBM Corporation, Databricks, MapR Technologies Inc., Qubole, Inc. and Cloudera, Inc.
The report offers a comprehensive evaluation of the market. It does so via in-depth qualitative insights, historical data, and verifiable projections about market size. The projections featured in the report have been derived using proven research methodologies and assumptions. By doing so, the research report serves as a repository of analysis and information for every facet of the market, including but not limited to: Regional markets, technology, types, and applications.
The study is a source of reliable data on:
The regional analysis covers:
The report has been compiled through extensive primary research (through interviews, surveys, and observations of seasoned analysts) and secondary research (which entails reputable paid sources, trade journals, and industry body databases). The report also features a complete qualitative and quantitative assessment by analyzing data gathered from industry analysts and market participants across key points in the industry’s value chain.
A separate analysis of prevailing trends in the parent market, macro- and micro-economic indicators, and regulations and mandates is included under the purview of the study. By doing so, the report projects the attractiveness of each major segment over the forecast period.
Highlights of the report:
Note: Although care has been taken to maintain the highest levels of accuracy in TMR’s reports, recent market/vendor-specific changes may take time to reflect in the analysis.
Please fill the Request TOC form below.
Make an enquiry before buying this Report
Please fill the enquiry form below.