4 reasons why managers should learn Hadoop
Big data is a popular topic these days, not only in the tech media, but also among mainstream news outlets. Managers feel big data can provide significant business benefits. It can help businesses gain insights in order to make faster & more informed decisions.
All major industries are now turning to big data, and Hadoop is the platform that makes big data easier to manage. Especially after April’s official release of big data software framework, Hadoop 2.7.3 is generating even more media buzz.
Basically, Hadoop is an open source software framework specifically built to process large amounts of data from terabytes to petabytes and beyond. Unlike relational databases, Hadoop doesn’t insist that you structure your data. Data may be unstructured and schemaless. Users can load their data into the framework without needing to reformat it. By contrast, relational databases require that data be structured and schemas be defined before storing the data.
There are four main reasons a business manager should learn about Hadoop. Let’s dive right into them.
Hadoop is scalable
Hadoop is a highly scalable platform because it can store and distribute very large volumes of data sets across hundreds of servers that operate in parallel. Unlike, traditional relational database systems(RDBMS) that can’t scale to process large amounts of data, Hadoop enables you to run applications on thousands of clusters. Scalability allows servers to be added on demand to accommodate growing workloads.
In other words, additional hardware could be easily and quickly added on as needed without having to pay extra because Hadoop is open source. That’s dramatically changed the way you can expand your computing power. You don’t want to spend millions of dollars on infrastructure.
Let’s say your marketing department is generating and storing three billion records a month, and that it’ll increase to 10 billion a month in 3 months. There are two main limitations for you in this scenario:
- Unstructured data such as video
- Amount of data to be processed
You can solve this problem using Hadoop by adding another server to the node. You could complete what your marketing department requires and scale immediately. It is not that this is impossible in RDBMS systems but it’ll be too costly in RDBMS. Hadoop makes it affordable.
Hadoop is cost efficient
Hadoop clusters delivers a cost effective solution to growing data sets. Previously, enterprises had to keep track of data sets – emails, sales data, customer data, internal data, etc -in an RDBMS, which was very expensive.
Typically, companies would down sample the data (reduce the data down to a smaller subset). This smaller data set would automatically be classified based on certain assumptions. The main assumption is that some data would always be more important than other data.
For example, the priorities for e-commerce data would be set on the assumption that debit card details would be more important than product data, and product data would be more important than analytics data.
But what would happen when the assumptions change? Companies would have only the reduced data to use, since all the raw data would have been lost while down sampling.
Whereas, Hadoop is designed as a scale-out architecture and can store all of a company’s data for later use at a low cost. The cost of a Hadoop data management system, including hardware, software, and other expenses comes about $1000 a terabyte — which is way lesser than other data management systems.
Analysis will be faster with Hadoop
Hadoop allows you to take in and process huge amounts of data in a short amount of time.
One big advantage of Hadoop is its ability to be able to analyze huge data sets to quickly find trends. For a company like Walmart, that could mean analyzing user data to learn what shirt colors were in fashion last season, compare that information with today’s hot color trends & determine what will sell this season.
Traditional databases can work for many sorting and analysis needs, but with very large data sets, Hadoop can be a much more efficient way to find things.
Hadoop eliminates data silos
Another drawback companies face with RDBMS is that the data is siloed within each department of the organization, to reduce storage costs. So the finance department would have their own data, as would HR, Operations and so on. As a result, it could get messy when you’re trying to gather all this data to make big business decisions.
Hadoop removes this bottleneck by enabling businesses to easily access new data sources and different types of data (both structured and unstructured) to generate value from that data. This means businesses can derive insights from various data sources such as web analytics, CRM, email conversations, or social media.
Companies considering Hadoop must be sure that it can integrate with their existing IT investments. The massive data aggregation enabled by Hadoop can raise concerns related to security, data access, data entitlement, monitoring, high availability and business continuity. Even though Hadoop saves costs and time, if it is poorly handled, it could explode the cost.
To conclude, a business manager must definitely learn Hadoop to address all these issues.
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