#why #hadoop #for #big #data
As the World Wide Web grew in the late 1900s and early 2000s, search engines and indexes were created to help locate relevant information amid the text-based content. In the early years, search results were returned by humans. But as the web grew from dozens to millions of pages, automation was needed. Web crawlers were created, many as university-led research projects, and search engine start-ups took off (Yahoo, AltaVista, etc.).
One such project was an open-source web search engine called Nutch – the brainchild of Doug Cutting and Mike Cafarella. They wanted to return web search results faster by distributing data and calculations across different computers so multiple tasks could be accomplished simultaneously. During this time, another search engine project called Google was in progress. It was based on the same concept – storing and processing data in a distributed, automated way so that relevant web search results could be returned faster.
In 2006, Cutting joined Yahoo and took with him the Nutch project as well as ideas based on Google’s early work with automating distributed data storage and processing. The Nutch project was divided – the web crawler portion remained as Nutch and the distributed computing and processing portion became Hadoop (named after Cutting’s son’s toy elephant). In 2008, Yahoo released Hadoop as an open-source project. Today, Hadoop’s framework and ecosystem of technologies are managed and maintained by the non-profit Apache Software Foundation (ASF), a global community of software developers and contributors.
Why is Hadoop important?
- Ability to store and process huge amounts of any kind of data, quickly. With data volumes and varieties constantly increasing, especially from social media and the Internet of Things (IoT), that’s a key consideration.
- Computing power. Hadoop’s distributed computing model processes big data fast. The more computing nodes you use, the more processing power you have.
- Fault tolerance. Data and application processing are protected against hardware failure. If a node goes down, jobs are automatically redirected to other nodes to make sure the distributed computing does not fail. Multiple copies of all data are stored automatically.
- Flexibility. Unlike traditional relational databases, you don’t have to preprocess data before storing it. You can store as much data as you want and decide how to use it later. That includes unstructured data like text, images and videos.
- Low cost. The open-source framework is free and uses commodity hardware to store large quantities of data.
- Scalability. You can easily grow your system to handle more data simply by adding nodes. Little administration is required.
What are the challenges of using Hadoop?
MapReduce programming is not a good match for all problems. It’s good for simple information requests and problems that can be divided into independent units, but it’s not efficient for iterative and interactive analytic tasks. MapReduce is file-intensive. Because the nodes don’t intercommunicate except through sorts and shuffles, iterative algorithms require multiple map-shuffle/sort-reduce phases to complete. This creates multiple files between MapReduce phases and is inefficient for advanced analytic computing.
There’s a widely acknowledged talent gap. It can be difficult to find entry-level programmers who have sufficient Java skills to be productive with MapReduce. That’s one reason distribution providers are racing to put relational (SQL) technology on top of Hadoop. It is much easier to find programmers with SQL skills than MapReduce skills. And, Hadoop administration seems part art and part science, requiring low-level knowledge of operating systems, hardware and Hadoop kernel settings.
Data security. Another challenge centers around the fragmented data security issues, though new tools and technologies are surfacing. The Kerberos authentication protocol is a great step toward making Hadoop environments secure.
Full-fledged data management and governance. Hadoop does not have easy-to-use, full-feature tools for data management, data cleansing, governance and metadata. Especially lacking are tools for data quality and standardization.
I believe that Hadoop has matured to a point that people can successfully build large and complex applications atop the platform. Hadoop has met our scalability requirements for handling large and varied types of data.
Bob Zurek, Senior Vice President of Products at Epsilon in Hadoop for the Enterprise. a TDWI Best Practices Report
Getting data into Hadoop
Here are just a few ways to get your data into Hadoop.
- Use third-party vendor connectors (like SAS/ACCESS ® or SAS Data Loader for Hadoop ).
- Use Sqoop to import structured data from a relational database to HDFS, Hive and HBase. It can also extract data from Hadoop and export it to relational databases and data warehouses.
- Use Flume to continuously load data from logs into Hadoop.
- Load files to the system using simple Java commands.
- Create a cron job to scan a directory for new files and “put” them in HDFS as they show up. This is useful for things like downloading email at regular intervals.
- Mount HDFS as a file system and copy or write files there.
Big Data, Hadoop and SAS
SAS support for big data implementations, including Hadoop, centers on a singular goal – helping you know more, faster, so you can make better decisions. Regardless of how you use the technology, every project should go through an iterative and continuous improvement cycle. And that includes data preparation and management, data visualization and exploration, analytical model development, model deployment and monitoring. So you can derive insights and quickly turn your big Hadoop data into bigger opportunities.
Because SAS is focused on analytics, not storage, we offer a flexible approach to choosing hardware and database vendors. We can help you deploy the right mix of technologies, including Hadoop and other data warehouse technologies.
And remember, the success of any project is determined by the value it brings. So metrics built around revenue generation, margins, risk reduction and process improvements will help pilot projects gain wider acceptance and garner more interest from other departments. We’ve found that many organizations are looking at how they can implement a project or two in Hadoop, with plans to add more in the future.