How To Analyze Big Data Using Hadoop?

What is Hadoop?

An open-source software framework, Hadoop, stores huge volume of data and runs applications on clusters of product hardware. Its enormous benefits include massive storage of any type of data, quick processing, and handling infinite concurrent tasks.

If your organization has a huge workload related to Big Data, i.e. a huge volume of data generated from you can implement Hadoop tools for easy and quick data management. Many enterprises are using this tool for the purposes of data management and answering complex queries.

Benefits

Hadoop has gained immense popularity with the rise of Big Data platforms that are capable of managing huge volumes of data.

Using Hadoop, you can avail following benefits for your enterprise.

• Cloud or on-premise services: Choosing between deployments of on-premise services or transferring these services over cloud is the starting point. Technology advancements and skill development is crucial to deploy necessary infrastructure for your Big Data projects. Sooner you deploy cloud services, you receive higher business value. 
• Advanced analytical tools: Open-source as well as larger vendors develop data integration tools which work well with Hadoop. These tools allow integration of structured as well as big data to receive valuable insights. 
• Evolution of predictive analytics presents a huge scope for Hadoop. Data visualization is already in use but now advanced tools such as Hadoop expands business value by extracting meaningful insights from big data. Textual analytical reports, extensive data mining, and data visualizations are beneficial for decision-making processes.
• Improve Efficiency: Hadoop provides enhanced capabilities with less programming compared to the conventional platforms. Hadoop eases the process of big data analytics, reduces operational costs, and quickens the time to market. 
• Expertise: A new technology often results in shortage of skilled experts to implement a big data projects. Advanced Hadoop tools integrate several big data services to help the enterprise evolve on the technological front. Emerging trends and best practices are being integrated with big data platforms to achieve desired results.

5 Things Not Known about Hadoop

• Hadoop – Future of Raw Data: Hadoop architecture can handle huge volumes of raw data storage; say up to petabytes through the means of Hadoop clusters. Data clusters increases scalability and cost effectiveness of data storage. Install Hadoop for analyzing raw data, organize into actionable insights; often it requires implementation of additional tools or professional advice. 
• Hadoop database is an affordable solution for enterprises: Enterprises gain access to enormous amount of raw data and semi-structured data, a base for invaluable big data insights. Regardless of size of your organization, you will definitely benefit from Hadoop analytics. Likely, global enterprises will be stand maximum advantage because they have to organize huge volumes of data. Early adopters will enter the competition soon; however they must acquires sufficient skills to translate raw data into actionable, accurate business analytics. 
• Security challenges of Hadoop:  Methods of storage and distribution of raw data in Hadoop presents security challenges; traditional firewalls may not address the security threats appropriately. 
• Turnkey solutions supporting Hadoop: Every day vendors introduce new IT solutions to enable efficient business analytics using Hadoop advantages of data retrieval, organization, and analysis. Companies lacking in-house trained Hadoop specialists can utilize these BI tools for a shorter learning curve. In this way, the learning curve shortens and enterprises yield a quicker ROI to gain a competitive edge. For a successful Hadoop implementation, you need to work within a framework having defined scenarios affecting the organization’s existing structure and basic operations. Turnkey solutions enable these structures quicken the process data analytics and applications. Managed service providers also help enterprises with easy and quick integration of Hadoop tools into an existing BI solution, although we have not yet found a perfect BI tool that provides seamless integration completely. 
• Evolution of Hadoop analytics:  The present Hadoop architecture has undergone extensive testing to handle huge volume of data sets with efficiency and at an affordable price. However, many tools are still in the prototype stage or are in the phase of applications testing. The future holds good for Hadoop as it is bound to become one of its kind turnkey solution for capturing, organizing, and analyzing data; it will take some more years though.

Taming big data with Hadoop tools

• MongoDB: It is a modern approach to database management, an alternative to traditional databases. This Hadoop analytics tool manages unstructured or semi-structured data along with data that keeps changing frequently. 
• OpenRefine: Known as GoogleRefine earlier, this data analytics tool is an open-source Hadoop tool that works on raw data. Users can explore huge sets of unstructured data easily without spending much time. It enjoys great community support including several contributors which means that the software tool is constantly updated. 
• Cloudera:  A high-quality branded Hadoop tool offers additional services. It develops a central enterprise data center such that your employees get better access to the stored data and examine it carefully to report valuable insights. 
• RapidMiner: This predictive analytics tool is supported by many major companies like Deloitte, Cisco, and eBay. The open-source data analytics tool enjoys great community support and it is easy and effective to use. The best thing about this BI tool is that users can integrate their specialized algorithms through exclusive APIs. The graphical user interface is similar to the appearance of Yahoo! Pipes’ even a non-technical user can operate this tool.
• Qubole: This easy-to-use Hadoop tool quickens and scales up big data analysis compared to data stored on Google, Azure, and AWS clouds. It is easy to install and does not require extensive infrastructure. If you have your IT policies in place, you can include any number of big data analysts in your team who will collaboratively develop solutions generated by different analytical tools in various data processing engines. 

Spark Vs Hadoop: which is a better big data tool

• Utility value: Spark includes user-friendly APIs for its native language, Scala and other software languages like Java, Python, Spark SQL etc. Spark SQL has features similar to SQL 92; hence developers do not require additional training to use Spark. In addition, Spark has an interactive mode which gives immediate feedback for development queries. On the contrary, Hadoop MapReduce does not have interactive user interface; however, it comes with additional plugins like Hive and Hadoop PIG for easy adoption of Hadoop analytics. 
• Performance: Spark is quick in processing data than Hadoop. However, it is unlikely to compare both as they follow different processing methods. Inbuilt memory in Spark uses the disk space for data storage thereby providing real-time data insights. Hadoop MapReduce implements batch processing, gathering data from websites continuously and hence cannot deliver real-time insights.
• Cost: Both are open-source software analytics tools. Although there is no development cost, enterprises incur costs of running these platforms. Spark and MapReduce operate on commodity hardware which costs less. Both platforms run on same hardware, the difference arises in the cost incurred on storage capacity. MapReduce is a standardized solution; it uses disk-based storage and hence requires more systems for distribution of disk space across multiple servers. Spark uses a lot of memory, but it can also run at a standard speed when accessing data stored on a disk. Disk space is an inexpensive option; Spark data storage systems can be leveraged to SaaS. However, the installation of Spark systems is expensive due to the huge RAM to run the entire data stored in memory. The good thing though is that you require only few systems for a successful implementation of Spark analytics systems.
• Data Processing: Batch processing in Hadoop MapReduce follows a sequential order of deciphering data from clustered data sets, performing necessary data operations, reporting results back to the cluster, collecting updated data form cluster, performing further data operations, reporting results back to cluster etc. Spark performs single-step operations on data stored in the in-built memory. The data is read from cluster, operational performance, and report it back to cluster. It uses GraphX, an in-built graph computation library that allows users to view data in the form of graphs. Users can transform and join graphs in Resilient Distributed Datasets. 
• Compatibility: Both platforms are compatible with each other. Compatibilities are shared for data sources, file formats, and BI tools.
• Fault Tolerance: Spark and MapReduce deal with fault tolerance in two different methods. Hadoop users use TaskTrackers which provide heartbeats to JobTracker; in case of a missed heartbeat the Jobtracker reschedules pending as well as running operations to another Tasktracker. It significantly increases project completion times for operations having a single failure as well. Resilient Distributed Datasets in Spark performs fault tolerance actions; it collects elements to be operated in parallel. Spark creates RDDs from any storage source that is supported by Hadoop involving local files.  
• Security: Hadoop is certified by Kerberos; third-party solutions vendor enable enterprises to leverage the directories. The Hadoop Distributed File System works well with access control lists (ACLs). Hadoop also provides Service Level Authorization ensuring that users have accepted the terms of service. Spark’s level of security supports authentication through secret password authentication process.

Your Big Data Plan

Planning to use Hadoop tools for big data analytics? Remember the following points:

• Business Objective: The starting point is to identify business needs and plan a strategy to approach them successfully. 
• Advanced Analytics: Go further than text analytics. Advanced systems help in developing predictive analytics model determining how to use the big data.
• Awareness of data: The world of analytics is experiencing a cultural shift. Business leaders are understanding the importance of using data analytics to enhance businesses. IT will be a major data support for analytics in future.