To sign up for our daily email newsletter, CLICK HERE
Hadoop is Apache’s software library that allows users to store, compute, and distribute large volumes of data. One of the most popular big data handling platforms, it is considered important for data scientists worldwide. This software provides many modules that allow easier and versatile handling of data, high-level analysis, parallel data processing, and more. Hence, a Hadoop course for an aspiring data scientist could be the right move in the right direction.
Benefits of Learning Hadoop
If you are trying to go for further studies in data science, then Hadoop would be quite an essential skill in your arsenal. Also, learning Hadoop is important to establish yourself as a data scientist or data analyst because it is deployed widely across industries for big data handling. Many online Hadoop courses are available that let you master this tool and use it to your advantage. Considered as an all-purpose solution for data-related issues, the Hadoop ecosystem has a plethora of options that allow you to collect, arrange, process, and visualize datasets.
Hadoop Projects for Beginners
As it is with any course, practice makes perfect. In the case of Hadoop, there is a multitude of projects that beginners can tackle to learn and apply the acquired concepts.
Data Migration –
It involves moving all your data from one platform or ecosystem to another. Before a company or a project deploys Hadoop for all its data needs, it is important to move all the relevant data to the Hadoop ecosystem. Since most businesses want to use the best tools available for data solutions, this is the first step in their plan.
Traditional solutions like relational database management systems (RDBMS) are considered legacy software. But, they have their limitations, especially regarding bulk volumes of data. This is where Hadoop comes in as its toolset is more efficient for big data handling. Data migration is a great project for people looking to familiarize themselves with Hadoop.
Scalability operations –
As time passes on any database, the growing volume of data leads to slower operations. Therefore, scalability is a major factor, especially in deployments done by growing firms. This can also be an activity-based study of how Hadoop tackles this issue. And, it also involves Apache Spark, which runs on top of Hadoop to process MapReduce tasks at nearly real-time speeds. This is a great way to introduce yourself to Spark, which is another major player in data handling.
Data Integration –
It is when data processed on multiple platforms using different technologies are brought under the same umbrella. When companies deploy data solutions locally, they may end up using different solutions. And, all this has to be brought together on one platform for data analytics. This is where Hadoop shines; thanks to its modular architecture. It has built-in data integration tools (Click) that allow users to configure and migrate datasets from different platforms via a GUI drag and drop interface.
Link Prediction –
Hadoop can also be applied in dynamic domains like social network analysis, and use algorithms to predict which nodes would be linked. User and search data from social media can be used to find user preferences and suggest friends and pages. Using Hadoop, nodes are stored, data is collected and parallelly processed to reveal links to pages and platforms that the user may be interested in. The same procedure is applied to create anomaly predictors as well. Detailed steps for the process are available on online resources.
Cloud Hosting –
Hadoop is also a great fit for cloud-based deployment. It can analyze data stored in the cloud. This also has many benefits over the physical procurement of the same data, including larger storage space, a virtual point that allows access from any location, and the requirement of less infrastructure. The benefits of this type of hosting can be studied and practiced via a project.
Specialized Analytics –
This is the use of Hadoop to solve the problems or fulfill the specialized needs of a specific sector or industry. This can be anything from finance, marketing, engineering or even entertainment. The aim of such a project is not only to test your skills in Hadoop, but also to give you experience in problem-solving, and applying learned principles to tackle the specific needs of the client.
Streaming Analytics –
Streaming is another area of data analytics. Unlike other forms where the data is in bulk and analysis is done by the bulk, data streams are formed and operated in real-time. Therefore, streaming analytics pose the challenge of collecting and processing this data in real-time. Of course, the amount of data flow is also a factor. Smaller streams can be solved easier and are less complicated to handle. But, a larger stream such as live engagements on a large social media page or a chunk of Wall Street is a different beast altogether. For this, Hadoop deployment is essential.
Speech Analysis –
Hadoop is a valuable tool in accurate speech analysis. Automated speech analysis is used on many occasions to not only study the content of oral speech but analyze the content to derive valuable insights as well. Places like call centers and customer services deploy Hadoop-based systems to identify, flag, and record calls for analysis.
Text Mining –
Hadoop can also be deployed for text mining purposes. This will help to accelerate and automate content curation and classification, summarization, and other text applications. These are especially useful in classifying user comments on posts, product reviews, and other forms of text-based content. The HTML data can be retrieved, stored in Hadoop, processed, and visualized.
Predictive Analysis –
Studying and analyzing trends is one of the biggest applications of data science. Companies, websites, even political parties, and the government use methods such as predictive analysis for figuring out trends. Whether it is suggesting a product or predicting the weather, Hadoop can be deployed to tackle the tasks.
Hadoop has many tools with each of them fulfilling a variety of applications for practical uses. Tackling these projects will allow you to learn and hone your skills as a data analyst, and excel at your tasks in the field.
