Best Big Data Hadoop Certification

1. Hadoop Tutorial – Learn Hadoop from Experts In this Hadoop tutorial on ‘What is Hadoop?,’ we shall be learning Big Data Hadoop in detail. We will also be looking at the problems that the traditional or legacy systems had and how Hadoop solved the puzzle of big data. Finally, we will see how Uber managed to handle big data using Hadoop. Big Data Hadoop Before talking about What is Hadoop?, it is important for us to know why the need for Big Data Hadoop came up and why our legacy systems weren’t ableto cope with big data. Let’s learn about Hadoop first in this Hadoop tutorial. Problems with Legacy Systems Let us talk about legacy systems first in this Hadoop tutorial and how they weren’t able to handle big data. But wait, what are legacy systems? Legacy systems are the traditional systems that are old and obsolete due to some issues. Why do we need Big Data solutions like Hadoop? Why are legacy database solutions, such as MySQL or Oracle, not feasible options now? First of all, there is a problem of scalability when the data volume increases in terms of terabytes. We have to denormalize and pre-aggregate data for faster query execution and as the data gets bigger, we’ll be forced to make changes in the process in terms of optimizing indexes that query extra. Legacy System Legacy System When our database is running with proper hardware resources, yet we see performance issues, then we have to make changes to the query or find a way in which our data can be accessed. We cannot add more hardware resources or compute nodes and distribute the problem to bring the computation time down, i.e., the database is not horizontally scalable. By adding more resources, we cannot hope to improve the execution time or performance. The second problem is that a traditional database is designed to process the structured data. Hence, when our data is not in a proper structure, the database will struggle. A database is not a good choice when we have a variety of data in different formats such as text, images, videos, etc. Another key challenge is that a great enterprise database solution can be quite expensive for a relatively low volume of data, when we add up the hardware costs and the platinum-grade storage costs. In a nutshell, it’s an expensive option. Traditional Solutions Next, we have distributed solutions, namely, grid computing, that are basically several nodes operating on a data paddler and hence quicker in computation. However, for these distributed solutions, there are two challenges:

2. • First, high-performance computing is better for computing-intensive tasks that have a comparatively lesser volume of data. So, it doesn’t perform well when the data volume is high. Second, grid computing needs good experience with low-level programming knowledge to implement it, and hence it wouldn’t fit for the mainstream. • So, basically, a good solution should, of course, handle huge volumes of data and provide efficient data storage, regardless of the varying data formats, without data loss. Next up in this Hadoop tutorial, let’s look at the differences between legacy systems and Big Data Hadoop, and then we will move on to ‘What is Hadoop?’ Differences between Legacy Systems and Big Data Hadoop While the traditional databases are good at certain things, Big Data Hadoop is good at many others. Let’s refer the below image: Hadoop vs. RDBMS RDBMS seems to work well with fewer terabytes of data. Whereas, in Hadoop, the volume processed is in petabytes. • Hadoop can actually work with changing schema, along with that it can support files in various formats. Whereas, when we talk about RDBMS, it has a schema that is really strict and not so flexible, and it cannot handle multiple formats. Database solutions scale vertically, i.e., more resources can be added to a current solution, and any improvements in the process—such as, tuning queries, adding more indexes, etc.—can be made as required. However, they will not scale horizontally. This means, we can’t decrease the execution time or improve the performance of a query by just increasing the number of computers. In other words, we cannot distribute the problem among many nodes. The cost for our database solution can get really high pretty quickly when the volume of data we’re trying to process increases. Whereas, Hadoop provides a cost-effective solution. Hadoop’s infrastructure is based on commodity computers implying that no specialized hardware is required here, hence decreasing the expense. Generally, Hadoop is referred to as a batch-processing system, and it is not as interactive as a database. Thus, millisecond response time can’t be expected from Hadoop. However, it writes the dataset as an operator and analyzes data several times, i.e., with hadoop, reading and writing multiple times is possible. • • • By now, you have got an idea about the differences between Big Data Hadoop and the legacy systems. Let’s come back to the real question now. What is Hadoop? In this Big Data Hadoop tutorial, our major focus is on ‘What is Hadoop?’ Big Data Hadoop is the best data frameworks that provide utilities, which help several computers solve queries involving huge volumes of data, e.g., Google Search. It is based on the MapReduce pattern, in

3. which you can distribute a big data problem into various nodes and then consolidate the results of all these nodes into a final result. Big Data Hadoop is written in Java programming language. Because of the robustness of Java, Apache Hadoop ranks among the highest level Apache projects. It is designed to work on a single server with thousands of machines each one providing local computation, along with storage. It supports a huge collection of datasets in a computing environment. Hadoop is basically licensed under Apache v2 license. It was developed based on a paper presented by Google on the MapReduce system, and hence it applies all the concepts of functional programming. Since the biggest strength of Apache Hadoop is its scalability, it has upgraded itself from working on a single node to seamlessly handling thousands of nodes, without making any issues. Hadoop: HDFS and YARN Several domains of Big Data indicate that we are able to handle data in the form of videos, text, images, sensor information, transactional data, social media conversations, financial information, statistical data, forum discussions, search engine queries, e-commerce data, weather reports, news updates, and many more Big Data Hadoop runs applications on the grounds of MapReduce, wherein the data is processed in parallel, and accomplishes the whole statistical analysis on the huge amount of data. As you have learned ‘What is Hadoop?,’ you must be interested in learning the history of Apache Hadoop. Let’s see that next in this Hadoop tutorial. History of Apache Hadoop Doug Cutting—who created Apache Lucene, a popular text search library—was the man behind the creation of Apache Hadoop. Hadoop got introduced in 2002 with Apache Nutch, an open-source web search engine, which was part of the Lucene project. Now that it is clear to you ‘What is Hadoop?’ and a bit of the history behind it, next up in this tutorial, we will be looking at how Hadoop has actually solved the problem of big data. What is Hadoop? Enroll in the Big Data Hadoop training now and learn in detail! How did Hadoop solve the problem of Big Data? Since you have already answered the question, ‘What is Hadoop?,’ in this Hadoop tutorial, now you need to understand how it becomes the ideal solution for big data. The proposed solution for the problem of big data should: • • • • • Implement good recovery strategies Be horizontally scalable as data grows Be cost-effective Minimize the learning curve Be easy for programmers and data analysts, and even for non-programmers, to work with And, this is exactly what Hadoop does!

4. Hadoop can handle huge volumes of data and store the data efficiently in terms of both storage and computation. Also, it is a good recovery solution for data loss and, most importantly, it can horizontally scale. So, as your data gets bigger, you can add more nodes and everything will work seamlessly. It’s that simple! Hadoop: A Good Solution Hadoop is cost-effective as you don’t need any specialized hardware to run it. This makes it a great solution even for startups. Finally, it’s very easy to learn and implement as well. I hope, now you can answer the question ‘What is Hadoop?’ more confidently. Let’s now see a use case that can tell you more about Big Data Hadoop. How did Uber deal with Big Data? Let’s discuss how Uber managed to fix the problem of 100 petabytes of analytical data generated within its system due to more and more insights over time. Identification of Big Data at Uber Before Uber realized the existence of big data within its system, the data used to be stored in legacy database systems, such as MySQL and PostgreSQL, in databases or tables. In the company, the total data size back in 2014 was around a few terabytes. Therefore, the latency of accessing this data was very fast, accomplished in less than a minute! Here is how Uber’s data storage architecture looked like in the year 2014: SQL/MySQL As the business started growing rapidly, the size of the data started increasing exponentially, leading to the creation of an analytical data warehouse that had all the data in one place easily accessible to the analysts all at once. To do so, the data users were categorized into three main groups: 1.City Operations Team: On-ground crews responsible for managing and scaling Uber’s transport system. 2.Data Scientists and Analysts: A group of Analysts and Data Scientists who need data to deliver a good service regarding transportation. 3.Engineering Team: Engineers focused on building automated data applications. Data warehouse software named Vertica was used as it was fast, scalable, and had a column-oriented design. Besides, multiple ad-hoc ETL jobs were created that copied data from different sources into Vertica. In order to achieve this, Uber started using an online query service that would accept users’ queries based on SQL and upload them on to Vertica. Beginning of Big Data at Uber It was a huge success for Uber when Vertica was launched. Uber’s users had a global view, along with all the data they needed in one place. In just a few months later, the data started increasing exponentially

5. as the number of users was increasing. Since SQL was in use, the City Operators Team found it easy to interact with whatever data they needed without having any knowledge of the underlying technologies. On the other hand, the Engineering Team began building services and products according to the user needs that were concluded from the analysis of data. Though everything was going well and Uber was attracting more customers and profit, there were still a few limitations: • The use of data warehouse became too expensive as the data compilation had to be extended to involve more and more data. So, to free up more space for new data, older and obsolete data had to be deleted. Uber’s Big Data platform wasn’t scalable horizontally. Its prime goal was to focus on the critical business needs for centralized data access. Uber’s data warehouse was used like a data lake where all data were piled up, even multiple copies of the same data, that increased the storage costs. When it came to data quality, there were issues related to backfilling as it was laborious and time-consuming and the ad-hoc ETL jobs were source-dependent. Data projections and data transformations were performed during the time of ingestion and, due to the lack of standardized ingestion jobs; it became difficult to ingest new datasets and data types. • • •