The toughest challenges in business intelligence today can be addressed by Hadoop through multi-structured data and advanced big data analytics. Big data technologies like Hadoop have become a complement to various conventional BI products and services. Thus, several organizations are preparing for hadoop usage to get the most business value by integrating it into their business intelligence products or services. Business intelligence professional’s interest in Hadoop has exponentially increased over the years as big data and business intelligence are a perfect match made in Hadoop. As big data meet business intelligence, professionals need to understand why this is the right time to pursue a career in big data hadoop.
In a short span of time, Apache Hadoop, a technology that supports big data and analytics has witnessed a remarkable interest from BI professionals. This is essentially due to the fact that Hadoop has established its efficacy in handling the biggest challenges of the BI processes. Viz. Big data, multi-structured data, and advanced analytics.
In the near future, Hadoop is sure to develop itself as a supplement to traditional products and business practices like Business Intelligence (BI), Data Integration (DI), Data Warehousing (DW) and Analytics. Organizations across the globe are now preparing to integrate Hadoop with their BI, DI, DW and analytics technology platforms. Let us ponder over five reasons as to why it is essential for BI professionals to learn Hadoop.
Industry studies reveal that Hadoop products like MapReduce, Java, Pig, HDFS, Hbase and Hive have gained a strong popularity.Other products like Mahout, Zookeeper and Hcatalog would be getting on real shortly.
Professionals who have learnt Hadoop have now started integrating Hadoop with DW's, analytic tools, web servers, data visualization tools, reporting tools and analytic databases. Big organizations across the world are witnessing a spurt in their data volumes and this is a chain reaction that is unstoppable.
Under such circumstances, slower analysis results in delaying the processing of the data. Hadoop is fast catching up as it allows rapid data crunching through a cluster of nodes.
Although, the percentage of organizations that have implemented Hadoop technology is not over 10% as on date, but within the next 4 years this will cross the 51% mark, for sure. In short, the Hadoop trend is will be impacting at least half of the BI/DW segment of the IT sector and this makes it necessary for BI professionals to learn Hadoop.
A survey conducted by TWDI, considered as a premier source for Business Intelligence, suggests that almost 78% of users consider Hadoop as a major value addition. The survey volunteers accepted that Hadoop is beneficial when it comes of Big Data analytics.
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One of the toughest parts of BI process is storage of Big Data, handling unstructured data and advanced analytics. BI professionals use various tools to draw useful data that are used to generate customized reports and this is where the Hadoop File Distribution System (HDFS) proves itself.
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The HDFS is designed to allow storing and managing of all data types. The specific advantages of Hadoop over traditional RDBMS are-
Hadoop is scalable: Hadoop is highly scalable and is designed to store and distribute huge data through multiple servers operating in parallel. Contrary to traditional RDBMS's that are un-scalable, Hadoop applications can run on thousands of nodes, thus allowing the processing of thousands of terabytes of data at lightning speeds. So the day that BI professionals would be staring at many Hadoop loaded nodes is not far and this makes it essential for them to learn Hadoop.
Hadoop is inexpensive: When it comes to data explosion, Hadoop has established itself as the most inexpensive storage solution. When it comes to processing massive amounts of data, scaling of traditional RDBMS's proves extremely expensive.
Hadoop is Flexible: This is another compelling reason as to why a BI professional should learn Hadoop. Be it social media, clickstream data or email conversations, Hadoop has the ability to derive useful data from all types of data resources. Further, Hadoop can be efficiently used for recommendation systems, marketing campaign analysis, fraud detection and data warehousing as well.
Hadoop is fail proof: When using Hadoop, data sent to an individual node also replicates on all the other nodes in the cluster, so in case of failure on one node, there is always a backup copy of the data available in the cluster.
Relational databases are dominant at present and they integrate well with other information systems. The present day RDBMS are perfect for querying structured data and people are well acquainted with their technicalities. One good thing about Hadoop is that it can work in parallel as well as in conjunction with rational databases. What's more? Storing of archived data on Hadoop is inexpensive and data can be drawn out from the relational database at regular intervals and restore it back in the DB whenever needed.
Apache sqoop is a wonderful tool that integrates Hadoop with databases like PostgreSQL, MySQL or Oracle through JDBC which is the regular API to connect databases with Java. Sqoop runs a query on the relational databases and exports the resultant rows in one of the file formats like Binary, Text, Sequence files or Avro. These files can be saved on Hadoop HDFS. Conversely, Sqoop also allows importing formatted file again into the relational database.
Once the data is analyzed, Hadoop integrates with a plethora of BI tools like Tableau, Pentaho, Datameer, BIRT to create interactive dashboards and diagrams for data visualizations.These BI tools connect with Hadoop to present the data in an accessible and to easy to comprehend manner, which facilitates informed consumer and business decisions.
"It's common that Hadoop is used in conjunction with databases. In the Hadoop world, databases don't go away. They just play a different role than Hadoop does," says Charles Zedlewski, VP of product at Cloudera.
These advantages of integrating Hadoop with traditional databases will keep motivating businesses to do so. As such, Business Intelligence professionals working with traditional databases will be required to learn Hadoop.
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Handling extraordinary data volumes with traditional technologies is particularly challenging from a technological point of view and it proves extremely expensive, as well. The traditional technologies demand vertical scalability that in-turn demands powerful hardware. Contrary to this, Hadoop offers horizontal scalability that allows using cost effective hardware and scalability, of course, is the nerve center of Big Data projects.
If they learn Hadoop, BI professionals will be delighted to be able to store and ask questions to the tune of 100 petabytes of data.This is something that BI professionals could possibly never have thought of? Hadoop is 10 times better in terms of scalability over the next available option. A Cloudera Executive asserts Hadoop systems, including hardware and software, cost about $1,000 a terabyte or as little as one-twentieth the cost of other data management technologies.This implies that more and more businesses across the world would not be using Hadoop because they Want to, but will adopt Hadoop technologies in the near future because the Have to. This situation in turn suggests that BI professionals have to learn Hadoop.
Learning Hadoop is synonymous with a flourishing career in BI. Hadoop has proven its usefulness in various segments of business. Today, the manufacturing sector uses Hadoop for assessment of product quality. The telecommunication Industry uses Hadoop for content conciliation. Various government organizations use Hadoop for security, Geo-spatial data, search as well as location based push of data.
A survey conducted by Karmasphere, a portal that is considered to be a leader in Big Data Insights reveals that there is an acute shortage of Hadoop skilled professionals. Almost 70% of participants that volunteered for the survey agreed that the data analysts in their respective segments lacked the technical expertise to analyze data on Hadoop.
Companies like Amazon, IBM, MapR, Cloudera and Hortonworks are propelling Hadoop technology to the next frontier. The global market for Hadoop based technologies will be reaching $8.74 billion by 2016 and it is growing at a rapid pace of over 55%. No doubt, this is one of the most important reasons for BI professionals to learn Hadoop!
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