Share and Growth 2018 of Apache Spark Market - Global Industry Analysis, Size, Trends
Press
Release - 25 April 2018
Global
Research and Development News
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Report
Description-
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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.
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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:
- Market segments and sub-segments
- Market trends and dynamics
- Supply and demand
- Market size
- Current trends/opportunities/challenges
- Competitive landscape
- Technological breakthroughs
- Value chain and stakeholder analysis
The regional analysis covers:
- North America (U.S. and Canada)
- Latin America (Mexico, Brazil, Peru, Chile, and others)
- Western Europe (Germany, U.K., France, Spain, Italy, Nordic countries, Belgium, Netherlands, and Luxembourg)
- Eastern Europe (Poland and Russia)
- Asia Pacific (China, India, Japan, ASEAN, Australia, and New Zealand)
- Middle East and Africa (GCC, Southern Africa, and North Africa)
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:
- A complete backdrop analysis, which includes an assessment of the parent market
- Important changes in market dynamics
- Market segmentation up to the second or third level
- Historical, current, and projected size of the market from the standpoint of both value and volume
- Reporting and evaluation of recent industry developments
- Market shares and strategies of key players
- Emerging niche segments and regional markets
- An objective assessment of the trajectory of the market
- Recommendations to companies for strengthening their foothold in the market
– More
Clear Details get Table of Contents
https://www.researchmoz.us/apache-spark-market-global-industry-analysis-size-share-growth-trends-and-forecast-2017-2025-report.html#table-of-content
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