Introduction to Apache Hadoop
Nowadays, people are living in the data world. It’s not easy to measure the total volume of data stored electronically, but an IDC estimate put the size of the “digital universe” at 0.18 zettabytes in 2006, and is forecasting a tenfold growth by 2011 to 1.8 zettabytes. A zettabyte is 〖10〗^21 bytes, or equivalently one thousand exabytes, one million petabytes, or one billion terabytes. That’s roughly the same order of magnitude as one disk drive for every person in the world [1].
So people see there is a lot of data out there. The storage capabilities of hard drives have increased massively over the years, but the access speeds, the rate at which data can be read from drives have not kept up. One typical drive from 1990 could store 1370 MB of data and had a transfer speed of 4.4 MB/s [2], (1370 MB)/(4.4 MB/s) = 311 s = 5.1 minutes, so the time for reading all the data from a full drive was around 5 minutes at that time. After 20 years, one terabyte drives are the norm, but the transfer speed is around 100 MB/s, so it takes more than two and a half hours to read all the data off the disk.
This is a very long time to read all data on a single drive, and writing is even slower. The obvious solution to reduce the time cost is to read from and write to multiple disks at once. Imagine if we had 100 drives, each holding one hundredth of the data. Working in parallel, we could read the data in less than two minutes.
Apache Hadoop is one of the solutions; it is an open-source software framework for storage and large scale processing of data-sets on clusters of commodity hardware [3]. Also, Apache Hadoop is a scalable fault-tolerant distributed system for data storage and processing. The core of Hadoop has ...
... middle of paper ...
...type constraints of the destination database, then errors will occur and the data being transferred will be rejected. So this is what we call Schema-on-Write.
On the contrary, Hadoop has a Schema-on-Read approach. When we write data into HDFS, we just copy the data in without any gatekeeping rules. Then when we read the data, we just simply apple rules to the code that reads the data rather than preconfiguring the structure of the data ahead of time.
Now the concept of Schema-on-Write versus Schema-on-Read has profound implications on how the data is stored in Hadoop versus RDBMS. Additionally, in RDBMS, the data is stored in a logical form with interrelated tables and defined columns. In Hadoop, the data is a compressed file of either text or any other data types, and the data will be replicated across multiple nodes in HDFS when the data enters into Hadoop.
Kerberos provides a secure authentication scheme. Authentication is needed to restrict the intruders and malicious users. The major security issues discussed are privacy of the data, integrity of data and authentication mechanism which is not there in Hadoop. Hadoop supports Kerberos for authentication and many security features can be configured with the Hadoop to restrict the accessibility of the data. The data can be associated with the user names or group names in which data can be accessed. Kerberos is a conventional authentication system, improved authentication systems can be used which are more secure and efficient than
Internal schema at the internal level to describe physical storage structures and access paths, typically uses a physical data model.
Files location and operation are hidden to clients. Clients don’t need to know how the system is designed, how data is located and accessed, and how faults are detected. The logic name of the file should not be changed even when relocate the file. Client sends requests to handle files without thinking about the complex mechanisms of the underlying system which performs operations. The DFS server just provide an access to the system with some simple tools. DFSs also use local caching for frequently used files to eliminate network traffic and CPU consumption caused by repeated queries on the same file and represent a better performance. And the local caching also give a fast access to those frequently used files. So caching has a performance transparency by hiding data distribution to users. DFSs have their own mechanisms to detect and correct the faults, so that users do not be aware that such fault
It presents a novel approach by providing a parallel two-way integration with Hadoop. All writes from the real-time tier make it into Hadoop and output of analytics inside Hadoop can emerge in the in-memory “operational” tier and distributed across data centres. The idea is to leverage distributed memory across a large farm of commodity servers to offer a very low latency SQL queries and transactional updates. 6) Strategic Direction Pivotal’s road map has given a strategic direction to its Hadoop solution and has made it significantly more competitive; its innovations focuses on improving the HAWQ SQL engine and integration with other Pivotal
Google File System (GFS) was developed at Google to meet the high data processing needs. Hadoop’s Distributed File System (HDFS) was originally developed by Yahoo.Inc but it is maintained as an open source by Apache Software Foundation. HDFS was built based on Google’s GFS and Map Reduce. As the internet data was rapidly increasing there was a need to store the large data coming so Google developed a distributed file system called GFS and HDFS was developed to meet the different client needs. These are built on commodity hardware so the systems often fail. To make the systems reliable the data is replicated among multiple nodes. By default minimum number of replicas is 3. Millions of files and large files are common with these types of file systems. Data is more often read than writing. Large streaming needs and small random needs are supported.
In the modern age, numerous grievances effect the lives of the modern adult. One such grievance is the enormous cost of internet. Non-legitimate fee after fee the wallets of customers shrivel from the greed of the Broadband Industry. Cent after cent, dollar after dollar, the colossal and cruel companies’ green has no limit unlike their data, which usually caps off at one terabyte. They provide a subpar service for a premium price.
The cloud storage services are important as it provides a lot of benefits to the healthcare industry. The healthcare data is often doubling each and every year and consequently this means that the industry has to invest in hardware equipment tweak databases as well as servers that are required to store large amounts of data (Blobel, 19). It is imperative to understand that with a properly implemented a cloud storage system, and hospitals can be able to establish a network that can process tasks quickly with...
Currently the world has a wealth of data, stored all over the planet (the Internet and Web are prime examples), but it is needed to be understand that data. It has been stated that the amount of data doubles approximately
In the modern era known as the “Information Age,” forms of electronic information are steadily becoming more important. Unfortunately, maintenance of data requires valuable resources in storage and transmission, as even the presence of information in storage re-quires some power. However, some of the largest files are those that are in formats re-plete with repetition, and thus are larger than they need to be. The study of data compres-sion is the science which attempts to advance toward methods that can be applied to data in order to make it take up less space. The uses for this are vast, and algorithms will need to be improved in order to sustain the inevitably larger files of the future. Thus, I decided to focus my research on the techniques that successful methods use in order to save space. My research question:
“ In 2010 the amount of digital information created and replicated worldwide was nearly 1,203 exabytes, (an exabyte is billion gigabytes or 1018 bytes)” IDC [1]
...and stored and recorded for the person that is storing in it for whatever reason. Which is an improvement because I knew that databases were important but I didn’t properly understand how much work went in to performing one action in a system from entry to the final stage of the information be stored in a violent medium and then transfer to a more stable server And how databases are usually thought of as not important part of how a system works. But they are and it is
Big data will then be defined as large collections of complex data which can either be structured or unstructured. Big data is difficult to notate and process due to its size and raw nature. The nature of this data makes it important for analyses of information or business functions and it creates value. According to Manyika, Chui et al. (2011: 1), “Big data is not defined by its capacity in terms of terabytes but it’s assumed that as technology progresses, the size of datasets that are considered as big data will increase”.
Big Data is a popular phrase used to describe a massive amount of both structured and unstructured data. Big data is difficult to process with traditional database and software techniques because of large quantity of data. Volume, velocity, variability and variety are three characteristics of Big Data.
The computer evolution has been an amazing one. There have been astonishing achievements in the computer industry, which dates back almost 2000 years. The earliest existence of the computer dates back to the first century, but the electronic computer has only been around for over a half-century. Throughout the last 40 years computers have changed drastically. They have greatly impacted the American lifestyle. A computer can be found in nearly every business and one out of every two households (Hall, 156). Our Society relies critically on computers for almost all of their daily operations and processes. Only once in a lifetime will a new invention like the computer come about.
This white paper identifies some of the considerations and techniques which can significantly improve the performance of the systems handling large amounts of data.