Hadoop Has Many Limitations: Here’s a Solution

Hadoop promises to provide a robust and cost-effective data storage system that is currently deployed across industries, but is Hadoop still delivering on its promise? SQream CTO, Razi Shoshani, says that it is best to supplement Hadoop’s capabilities with other solutions so that analytics on large data sets can run smoothly.

Big data has become the holy grail of business success. Every organization is striving to be more data-driven to run operations more efficiently. However, the insights that can be generated depend on the quantity, quality, and variety of the analyzed data.

Research shows that enterprises are only analyzing a small percentage of the data that they have, and are therefore not realizing the full potential. Forrester reports that between 60 percent and 73 percent of all data within an enterprise goes unused for analytics. The sheer volume and variety of data have multiplied exponentially, making accessing all that data a challenge.

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Is Hadoop Still Delivering on Its Promise?

Hadoop provides a robust and cost-effective data storageOpens a new window system for various industries, including banking, telecom, e-commerce, healthcare, and government industries. When it was initially launched in 2006, Hadoop provided a cost-effective solution by enabling the storage of big data in a distributed fashion on commodity hardware. Originally designed for search engines, it enabled enterprises to scale up by storing data on thousands of nodes.

But now that the volumes of data have grown exponentially, running analytics using Hadoop has

become more time-consuming, significantly slowing down results and limiting productivity. Very often, insights are no longer relevant by the time they are generated.  

Additionally, there are technical limitations to the types of data that can be analyzed. Data is stored as raw files on the Hadoop cluster, making it difficult to manage structured data. You need to distribute and manage the data very carefully, or you need to copy it everywhere. It can sometimes take days or weeks to correctly partition the data so that it is useful for BI customers. Many analysts report they spend 80% of their time preparing data, and the other 20% of the time complaining about it. It is also tough to find the trained staff needed to perform data preparation.

Moreover, Hadoop is not designed for ad hoc queries using SQL, which is still the most popular query language used in business. For exploration purposes, it is always preferable that data consumers have direct access to data. Data analysts should be able to run queries whenever they want to accelerate the process of testing and generating insights.  

Hadoop also has limitations when it comes to executing more complex queries. While simple tasks like counting words in a document can be performed easily, more complex workloads like a joint operation with large datasets are difficult and sometimes infeasible. This is because the data needs to sit on the same machine, which, in some cases, can require copying data from a different node just to enable the query to be performed.

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Making Hadoop Work in Today’s Massive Data Reality

Despite the challenges with the time and resources invested in Hadoop, replacing it isn’t an easy option. The secret is to let Hadoop do what it does best, and then supplement its capabilities with other solutions so that analytics on large data sets can run smoothly. 

One possibility is to supplement the classic Hadoop stack with a data analytics acceleration platform. This platform should be designed for ad hoc SQL queries and reporting, with the ability to access data from Parquet files, Hadoop, and other databases. With this type of solution, each line of business can have dedicated access to the data, so that there are no silos, and everyone can access all the data at all times. 

There is no need for pre-aggregation or pre-modeling, which will reduce the time and resources required for data preparation. This, in turn, will enable data scientists and BI analysts to ask more questions about data from a wider variety of perspectives. In short, you get the scaling without the complexity, while still making use of HDFS, Hadoop’s file management system. 

To solve the problem of crunching through huge volumes of data, underlying GPU-powered servers can support a large enterprise’s data compute needs while delivering faster performance at a fraction of the cost of competing for CPU-only solutions. For example, by using GPU technology, an internet company reduced a query time from five hours to five minutes, which meant that account managers didn’t need to wait from morning to late afternoon to see the results of a new strategy to optimize earnings. 

They were even able to add more data and more dimensions, and still keep query time down to five minutes. This increased efficiency translates into significant potential revenues. Data analytics are critical for creating and maintaining a competitive advantage. Hadoop can still be a very secure and viable data store. And by combining it with a fast, powerful database that is SQL-based, BI data scientists can achieve the speed and greater accuracy they need to make an organization wiser and more competitive.

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