In the current economic scenario, data is the lifeblood for every business aspect, allowing companies to understand and analyze patterns and derive useful insights for developing strategies, which ultimately benefit various business areas. As a result, companies are shifting to a data-driven business model for a competitive edge and better profit margins. Some of the analytics use cases where data plays a key role are listed below.
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Data and analytics work hand in hand in the success of any enterprise, be it an SME or a large enterprise. Organizations are thus more dependent on datasets than ever before; however, one aspect introduces complexity in operations. Data volumes and their types are difficult to handle and analyze. Hadoop, a set of software utilities, offers a solution to this hurdle as it helps in handling and computing massive volumes of data.
Globally, a number of organizations are on the path of adopting big data strategies and architectures as a part of their data-driven business models for maximizing data insights and increasing business value. This has primarily escalated the demand for Hadoop as it offers higher data scalability, coupled with low data storage.
TrueCar and Dell are two such organizations that have adopted Hadoop for data processing. TrueCar provides pricing information about old and new cars. It collects high volumes of car price data to power its online car buying business. The complexity involved in data management has compelled the company to shift its data to Hadoop, which helped the company in reducing the operational cost from USD 19/GB to USD 2.3 cents/GB. Dell Secureworks, which processes around 20 billion events per day, adopted Hadoop, which slashed its monthly data processing cost from USD 17/GB to USD 0.21 cents/GB.
Although Hadoop has emerged as a leading tool in managing big data, some security glitches that persist in its operational model hinder the growth of the Hadoop market. A few of these security glitches are listed below.
Fragmented Data: Hadoop primarily allows the creation of multiple data copies across various systems to ensure redundancy and resiliency. Data fragmented is available across all networks, which poses a security issue owing to the absence of strong security features.
Node-to-Node Communications: Hadoop doesn’t operate with a secure communication protocol; it is highly dependent on remote procedure calls over the TCP/IP model.
There has been a colossal growth in data volumes across every commercial sector, with prominent adoption in manufacturing, retail, and telecom sectors; however, this trend does not apply to the healthcare sector. Though there is a large amount of data growing at an exponential rate in the sector, only 10% of healthcare firms adopt tools for analysis.
In the healthcare sector, data collected by the firm is restricted to be kept within with the limits of healthcare provider owing to strict data privacy and security policies. This scenario restricts the scope of further analysis, thereby exhibiting a lower growth rate within the sector. However, the ongoing transition towards the value-based care model makes it mandatory for various stakeholders to get a complete picture of the treatment methods of a specific patient. The only way to achieve this vision is to aggregate all the separate data sources across the healthcare sector, which in turn will prove to be beneficial in gaining meaningful insights and develop a price-efficient sustainable model.
The integration of such separate data sources involves complexities in terms of volume, variety, and velocity of data sets, which creates scope for big data technologies such as Hadoop and MapReduce.
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The Nordic countries hold immense growth potential in the Hadoop market, primarily backed by the latest initiatives focusing on the overall technology sector. A few of the key initiatives are listed below.
Digital Hub Denmark: Under this initiative, the Danish government aims to strengthen its research in big data, artificial intelligence, and the Internet of Things (IoT) to attract various conferences and knowledge seminars, transforming Denmark as a pioneer to attract foreign investments.
The Technology Pact: The Danish government focuses on setting up a technology hub in cooperation with industry and educational institutes to strengthen the technical and digital skills among citizens.
Hadoop possesses a significant growth opportunity in Denmark due to the high reliance on data across all commercial sectors for the generation of meaningful insights. The various use cases of data analysis across commercial sectors are listed below.
Manufacturing: IoT has been playing a key role in the manufacturing sector for collecting real-time information regarding the production facility and flagging faults within the production facility. Combining data around the sale and manufacturing optimizes production and improves product quality; thus, Hadoop plays a key role in data aggregation and management.
Energy and Utilities: IoT and artificial intelligence-based platforms will prove beneficial for establishing the effect between energy consumption and capacity, thereby deriving patterns necessary to adjust the energy price to a cheaper amount. Hadoop eases the management of such large volumes of data, thereby aiding the overall recognition of data patterns within.
Canada also holds immense potential for the growth of the Hadoop market, primarily propelled by the higher adoption of mobile and cloud services, which is leading to colossal accumulation of data. Canada’s big data market was valued at USD 1.5 billion, with the potential to achieve double growth till 2021. The figure below depicts the revenue growth of Canada’s big data services market.
Within Canada, Ontario accounts for more than half the big data service providers, followed by Quebec and British Columbia. Ontario has large industries that depend on colossal volumes of structured and unstructured data, making it a fertile market for big data analytics and its technology providers. With escalating growth in data volume, there is a significant demand for real-time intelligence services, which had led to a public-private partnership between the Government of Canada, Ontario Centres of Excellence, academic institutions, and IBM. This union leads the establishment, research, and development initiative, boosting the infrastructure related to data analytics and cloud computing, fostering a lucrative scenario for the Hadoop market. The figure below portrays the regional breakdown of big data companies across Canada.
Some of the key companies operating in the Hadoop market are Amazon Web Services, Cloudera, Inc., Dell, Hortonworks, HPE, IBM Corporation, MapR technologies, Memsql Inc, and Pentaho Corporation.
Cloudera: Cloudera offers an SQL-for-Hadoop tool integrated with Impala. It offered the first visual cluster management tool and continues to put significant efforts into key features such as security, high availability, governance, and administration. Some of the key products that record significant demand among customers include Cloudera Manager, Cloudera Navigator, and Impala.
MapR Technologies: The company focuses on delivering higher product performance and reliability at scale. MapR Technologies’ strategy has been to persuade a distribution that would allow Hadoop to reach its full performance and scale potential with minimal effort. MapR filesystem comprises HDFS API that possesses the feature of fully read/write and store trillions of files.
IBM: The company integrates a wide range of key data management components and analytics assets into the open-source core of its Hadoop distribution. It has also launched an ambitious open-source project, Apache SystemML, for machine learning on Apache Spark from its newly minted Spark Technology Center.
The table below depicts vendor scores based on factors with a rating from 0 (weak) to 5 (strong).