Apache Hadoop in Data Lake Management

| August 6, 2019

Apache Hadoop in Data Lake Management

Over the past decade, the big data industry has grown significantly, with firms heavily relying on analysis of the multiple data files to influence their strategic positioning. The major challenge in the progressively advancing data industry, however, remains the storage and management of the data. As such, different models including data lakes. By definition, a data lake is a system whereby the data is stored in its natural format and is subsequently collocated into various schemata and forms. It is then accessed whenever needed. The adoption of the data lake, therefore, facilitates creation of a single file system that meets the novel needs of the firm.

A major characteristic of the data lake is that the information stored can be strategically manipulated and transformed for strategic use in accomplishing novel organizational tasks such as reporting, visualization of trends, analytical dynamics, and the machine learning processes. The types of data integrated in a data lake vary significantly in form and origin. They can be binary, relational, semi-structured, or unstructured. Therefore, the data lake acts as a central store for all data systems used in an organization. Over the years, Apache Hadoop has proved to be a reliable tool in the management of Data Lake in different institutions (Reddy, 2016). This analysis thus focuses on the application of Apache Hadoop in management of Data Lake in selected corporations, and the benefits and challenges that have since been associated with its adoption.

Architecture of Apache Hadoop

The Apache Hadoop system has defined architectural framework that enables it to be successfully used in the management of Data Lake. It is a java level application system that can work with the MapReduce 1 or MapReduce 2 engines. In addition, Apache Hadoop has critical JAR systems that helps to start and run the scripts required for effective working of the application. Another key feature of the Apache Hadoop tool is the integrated location awareness system which helps to locate files stored in the Data Lake (HUANG, WANG, LIU, & KUANG, 2013). The location awareness is often integrated as a name rack with worker node. The information on the rack is used to execute codes and deliver instructions to the data systems. The execution node has also been seen to contribute towards the reduction of redundancy of data in the system. HDFS is also noted to advance data redundancy by replicating data across many racks. This helps to prevent total loss of information in case of technical challenges in one rack.

Apache Hadoop are further organized into clusters. A small cluster of the tool is often constituted of one master node and several worker nodes. This organizational framework is such that the master node has job tracker, task tracker, as well as name node and data node. The worker node is also called the slave node and is characterized with DataNode and TaskTracker. In some Apache Hadoop systems, the worker nodes can be data only or execute only, hence having either but not both of the above nodes. However, the unitary slave nodes are common in non-standard applications and are rarely used as part of the integrated data management system.

In larger clusters, HDFS nodes are integrated in such a way that they are managed through a dedicated name node. The dedicated name node has memory features that helps to identify frequently used file systems and develop snapshots of the memory system. The snapshots generated are important in preventing file corruption which can affect operational frameworks of a firm. The comprehensive systems also have standalone job tracker server which is essential in scheduling activities across the nodes. Also, the system has integrated Hadoop MapReduce which can be used with alternate file systems depending on the specific tasks and data types that the firm seeks to accomplish at a given time (HUANG, WANG, LIU, & KUANG, 2013). When used with the alternate systems, however, some features of the Apache Hadoop are changed to conform to the new system. The changed features include the Name Node, the secondary Name Node, and the Data Nodes that form the critical components of the HDFS architecture.

Figure 1: Hadoop Architecture

 

Benefits of Apache Hadoop to Large Firms

Speed is of essence in the management of Data Lake, and also in accessing specific files from the data base. The development of the Google MapReduce system highlighted a new dimension in the storage and management of files, subsequently inspiring the development of Hadoop in 2006. The new system has since been noted to hold a number of benefits for big firms, especially in the management of their data lakes. The strategic importance of Apache Hadoop has resulted into its adoption by many firms. Some of the key benefits that such firms draw from the technology-driven data management system include flexibility, efficiency, and cost effectiveness.

A notable outstanding feature of Apache Hadoop is its scalability and performance. This means that the tool can be adopted for small organizational tasks as well as the big tasks. Apache Hadoop has a distributed data processing model whereby local data in each node can be processed independent of the data stored in other nodes. This form of data processing in a cluster enables the Apache Hadoop tool to work at petabyte speed which is significant of the performance of the system (Li, Shen, Ligon III, & Denton, 2016). It explains why companies that rely on this system are able to access critical information stored in the Apache Hadoop systems with great efficiency and speed.

Another benefit of the data management tool is its reliability. This relates to the strategic replication of data files and their storage in different nodes. Also, the system stores snapshots which are impactful when it comes to the reliability of the software. Again, the current era of computing technology has been characterized with occasional and at times frequent system failures (Gupta, Kumar, & Gopal, 2015). Such developments not only impede the organizational processes, but can also work to the aid of rival firms. Based on this background, it is notable that the Apache Hadoop system prevents such unprecedented occurrences since failure of one node does not affect another. As such, the data stored in the affected node can easily be accessed from the snapshots or the replica data stored in the other nodes. The data management tool automatically redirects input prompts to the remaining nodes that are unaffected. The data in the remaining nodes is also automatically replicated in preparation for future failures. As a result, the chances of total failure in accessing vital data is negligible.

Thirdly, flexibility has been realized in data management in firms using the Apache Hadoop. While traditional data management systems required one to develop integrated schemas for data storage, the new Apache Hadoop tool works independent of the schemas and is thus more effective than traditional models. The fact that data storage can be done in any format; structure, unstructured, binary, or unitary, also makes this model more flexible tool for data lake management in big organizations (Li, Shen, Ligon III, & Denton, 2016). Schema can be introduced to the data when it is being read, after retrieval from the data lake.

Finally, the low cost associated with the data management tool is a reason why it has been adopted in many large MNEs that manage large volumes of data. The tool is open source and does not require advanced hardware to run. Therefore, the organizations are not forced to commit large revenue towards data management. The low cost involved is also associated with the limited treatment of data stored in the system. In many cases, such treatments are costly in terms of time and money. The elimination of the schematic coding of the data as a principle requirement for data storage has effectively made it possible to manage critical data using this tool in a more cost-effective manner (Li, Shen, Ligon III, & Denton, 2016).

Application at Yahoo Inc.

The firm is considered as one of the largest consumers of the Apache Hadoop technology. Yahoo noticed the potential of the invention in advancing its growth goals and promoting its global business positioning. To realize these prospects, the firm had to integrate its data management strategies in such a way that it harnessed efficiency and reliability as competitive tools. The introduction of Apache Hadoop thus proved to be a game changer for Yahoo Inc., enabling it to manage its growing volumes of data. According to the firm, the integration of Apache Hadoop as part of its strategic frameworks enabled it to capture a larger segment of the market (Lam, 2011). In particular, yahoo used the technology as part of its Yahoo Search tool.

The firm runs thousands of Apache Hadoop machines in the various data centers which are able to manage in excess of 600 petabytes of information. Closer analysis of the Yahoo Data Centers shows that the Apache Hadoop technology is used in a variety of ways in line with the desired data processing patterns by the firm. Yahoo infrastructure harnesses the ability of the HDFS to ultra-scale its data storage needs (Sun, Chen, Guan, & Lin, 2013). In addition, the company has significantly benefited from the ability of the Apache Hadoop MapReduce system to conduct batch processing of data. This has enabled the firm to process its bulky data and give relevant responses to input queries on Yahoo Search.

The Hive and Pig function of Apache Hadoop has been widely used for database analytics at the company, a prospect that has further promoted the competitive positioning of the company. Other major features of the Apache Hadoop system and their uses in Yahoo Inc. include; HBase tool for the storage of key-values, the Storm feature for processing stream, and Zookeeper which plays a fundamental role in the coordination of data lake-related activities in the firm (Gupta, Kumar, & Gopal, 2015). The positive impacts of Apache Hadoop in the firm has since led to the intensive involvement of Yahoo Inc. in the improvement of the tool.

Within the modern era characterized by cybercrimes and unauthorized access to classified data, Nokia relies on the Apache Hadoop update to meet its security needs. The new model has integrated security features that protects stored data from unauthorized access. In addition, the evolutionary tool has user authentication frameworks. According to Nokia, this feature helps to restrain sharing sensitive information by users. It thus contributes towards the protection of users such as minors from manipulative online frameworks. Moreover, the competitive frameworks require that a firm keeps in touch with its clients and also focus on competition to understand market trends. Yahoo’s use of the Apache Hadoop was fundamental in collecting data from applications and hence analyzing the data to develop a comprehensive understanding of their clients and competitors, and to remodel their products in such a way that they can win in a competitive market (Lam, 2011).

Microsoft

Considered as one of the world’s most vibrant technology ventures with millions of users globally, Microsoft has dire need to effectively and efficiently manage its data lake. As a result, the firm has developed the Hadoop-based Azure HDInsight which has since been noted to have multiple merits. Firstly, the technology has been instrumental in promoting efficiency and reliability in the firm. Azure HDInsight exhibits automatic replication of data, a prospect that helps to prevent deleterious data loss which could cripple the operations of the firm (Mrozek, Daniłowicz, & Małysiak-Mrozek, 2016). In addition, the technology is user friendly, a feature that makes Microsoft systems one of the most used globally. The company has equally strived to reach more clients across the globe. With the integration of the Azure HDInsight system within its business models, this goal has been achieved significantly.

Critical evaluation of the Azure HDInsight service shows that it is reliant on the Hortonworks HDP. The integration of the HDI component in the system allows for integration with .NET and Java programs. Moreover, the modified version can be effectively used for programing functions using the Ubuntu Linux tool. Through the deployment of the HDInsight in cloud systems, it is much easier to spin nodes needed at a specific time (Mrozek, Daniłowicz, & Małysiak-Mrozek, 2016). It is also possible to effect selective charging of the nodes for computational and storage functions. Another key use of the Azure HDInsight is the movement of data from the on-premise datacenters to cloud based datacenters for backup. Again, this is important in protecting the critical data from loss through node failures. Occasionally, Microsoft uses the Apache Hadoop-based Azure HDInsight to facilitate development and testing of systems, as well as the bursting of scenarios. Hadoop can thus be run on virtual azure machines to aid effectiveness in data management.

Amazon Inc.

This is another technology firm that has actively adopted the Hadoop technology to tevelop assistive data management technologies. The Amazon EC2 is a strategic example of the application of Apache Hadoop in the firm. This tool is used as a solution to the elastic web-scale computing needs of different firms and individuals. It can be used to increase or decrease the capacity of computing system within short timelines, often minutes. In addition, Amazon notes that the Hadoop-based technology allows for simultaneous commissioning of thousands of servers (Amazon Web Services, 2016). A notable strength of the EC2 application is its ability to be scaled up or down during web-based interactions, thus ensuring that tasks are completed within defined time limits.

Hadoop is also used by amazon to create cloud-based hosting services. This means that users have the options of choosing between multiple models. It is equally important in enhancing the quality of interactions between the individuals and their computing devices. Another major importance of the new model is its role in the advancement of cloud computing services which have grown in significance in the modern era. It allows users to select their preferences during cloud storage. These settings can be done on both Windows and Linux OS (Amazon Web Services, 2016). Amazon has equally highlighted that the Hadoop system helps in integration of the product and service lines offered by the firm. For instance, it is characterized with reliability and security which makes it a prime choice for large corporations in their data lake management needs.

A notable example is the use of the Hadoop-based Amazon E2 for advanced data processing in New York Times. The case involved the firm processing millions of images into PDF versions in a limited span of 24 hours, the cost of the project being a paltry $240. Another Amazon tool that uses the Hadoop system is the S3 object storage which has since been integrated into the apache system as a supported function of the Apache Hadoop.

Google Inc.

The technology firm has also adopted Apache Hadoop as part of its operational systems. A positive correlation and mutual complementation has since been established between Google Cloud and Hadoop. In this case, the American firm exploits the security and reliability feature of Apache Hadoop to provide its Google cloud clients with impeccable, reliable and efficient services. The Hadoop-Google ecosystem can be managed by individuals or by Google. Some of the Hadoop-based Google-run services include Google Cloud Dataprotic, which is a managed spark and a Hadoop service, and Command line tools called the bdutil (Gemayel, 2016). The bdutil is a collection of shell scripts used for the creation and management of Hadoop clusters. These clusters, consequently, help in the management of novel organizational data. Besides, Google helps in the distribution of third party Hadoop services such as Cloudera, Hortonworks, and MapR. Another important observation is the use of Hadoop systems to connect with Google Cloud services, enabling users to harness the dynamic benefits of the two software applications (Gemayel, 2016). Some of the connectors used to link the Google cloud services and Apache Hadoop services include the Google Cloud Storage Connector, and the Google Big Query connector. 

Conclusion

In summary, Apache Hadoop has evolved into a fundamental tool in the management of data lake in large corporations. Multinationals such as Google Inc., Yahoo Inc., Amazon Inc., and Microsoft have effectively integrated the tool as part of their strategic data management systems. Through the integration, the firms have realized improved efficiency, reliability in service delivery, as well as advanced user interphase. Consequently, the firms have adopted strategic measures to integrate Hadoop features in the conjugation of different product lines. One of the largest application of the Hadoop tool, however, remains is effectiveness in the management of cloud based data, a prospect that has led to the development of multiple Google Connectors to link it to the firm’s products. It is thus conclusible that Apache Hadoop will continue to play a central role in the management of organizational data lakes, a critical feature in the contemporary world which is characterized with increased cases of cyberattacks and data theft.

 

 

References

Amazon Web Services,. (2016). Elastic Compute Cloud (EC2) Cloud Server & Hosting – AWSAmazon Web Services, Inc. Retrieved 9 December 2016, from https://aws.amazon.com/ec2/

Gemayel, N. (2016). Analyzing Google File System and Hadoop Distributed File System. Research Journal Of Information Technology8(3), 66-74. http://dx.doi.org/10.3923/rjit.2016.66.74

Gupta, P., Kumar, P., & Gopal, G. (2015). Sentiment Analysis on Hadoop with Hadoop Streaming. International Journal Of Computer Applications121(11), 4-8. http://dx.doi.org/10.5120/21582-4651

HUANG, C., WANG, L., LIU, X., & KUANG, Y. (2013). Tasks assignment optimization in Hadoop. Journal Of Computer Applications33(8), 2158-2162. http://dx.doi.org/10.3724/sp.j.1087.2013.02158

Lam, C. (2011). Hadoop in action (1st ed.). Greenwich, Conn.: Manning Publications.

Li, Z., Shen, H., Ligon III, W., & Denton, J. (2016). An Exploration of Designing a Hybrid Scale-Up/Out Hadoop Architecture Based on Performance Measurements. IEEE Transactions On Parallel And Distributed Systems, 1-1. http://dx.doi.org/10.1109/tpds.2016.2573820

Mrozek, D., Daniłowicz, P., & Małysiak-Mrozek, B. (2016). HDInsight4PSi: Boosting performance of 3D protein structure similarity searching with HDInsight clusters in Microsoft Azure cloud. Information Sciences349-350, 77-101. http://dx.doi.org/10.1016/j.ins.2016.02.029

Reddy, G. (2016). Big Data Processing Using Hadoop in Retail Domain. International Journal Of Engineering And Computer Science. http://dx.doi.org/10.18535/ijecs/v5i9.65

Sun, Y., Chen, Y., Guan, X., & Lin, C. (2013). Approach of large matrix multiplication based on Hadoop. Journal Of Computer Applications33(12), 3339-3344. http://dx.doi.org/10.3724/sp.j.1087.2013.03339

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