For all the hype around Hadoop and other software on the same ecosystem, surprisingly little is mentioned about implementing it. Hadoop isn’t the easiest of technologies to get up and running, and enterprises often struggle with security issues and performance issues down the line because of mistakes made earlier down the road.
The end result is companies end up utilizing just a fraction of Hadoop’s true power and resilience. To avoid setbacks down the road, a series of actions have to be taken to mitigate any possible issues.
The problem with Hadoop
The first and most noticeable problem with Hadoop Development is its almost total lack of consideration for security, at least by default. The default installation of HDFS Admin, the interface used to manage Hadoop clusters, defaults to an IP address, 0.0.0.0.
Essentially, anyone, even unauthenticated users, have permission to perform super user functions on a Hadoop cluster such as destroying data nodes and clusters. The potential hacker doesn’t have to be extremely talented at penetration testing, either, since the super admin functions can be performed via a browser.
Another associated issue is maintaining high availability in a Hadoop cluster. It has just one NameNode by default, and this is where all the metadata about the cluster is stored. Being just one of them, there is a single point of failure – if the NameNode goes down, the whole system also fails.
These problems are possible to remedy before they become a problem, and even more steps can be further taken to mitigate the many other problems Hadoop enables. They are part of what has led to the great Hadoop vs Spark debate, for instance, with the latter technology being considered a viable replacement.
Dealing with Hadoop’s issues to maximize value
Use Hadoop together with the right set of tools
Data on its own doesn’t have any value. For instance, a company that collects coordinate location information won’t find much value in that data sitting idly in a Hadoop cluster. It gains its value depending on the kind of applications it can find. After collection, data needs to be manipulated, analyzed and stored.
The right tools are going to help you add value to the data. For instance, an analytics table will help you to gain valuable insights into the data or telemetry information could be used to ensure maximum uptime.
Data such as this could allow companies to predict where failures in a system are going to occur and produce a viable timeline of the same. Routine maintenance can then be done on the hardware to ensure uptime and save money.
The goal is to give data some value by processing it. If your data can be loaded and accessed in an optimal amount of time, you’re doing a good job at harnessing the platform’s true power.
The goal of improving performance is to ensure high efficiency within Hadoop, removing the necessity of moving the data to a separate analytics platform.
Determine the types and sources of data that will flow into Hadoop.
For all the excitement that Hadoop created, with its ability to store unstructured data, that ability is severely limited by the number of factors that go into data processing with Hadoop and how they are handled.
There are many types of data that aren’t suited for traditional relational databases and are typically offloaded into Hadoop. This process is unnecessarily expensive if they have to be forced to fit. In particular, two factors have to be put into consideration:
- Schema design: Hadoop is schema-less in nature by design, but that doesn’t completely exclude it from the discussion around structured data in Hadoop. The Hadoop administrator has to take into account the directory structures for data loaded into HDFS, including data processing and analysis output, and schemas used in object stores like Hive and HBase.
- Metadata Management: As with most systems, metadata is quite important, at times just as important as the data itself, but its value is criminally underrated. It’s important to understand what kind of metadata is available, why it’s important and how it can be used to guide decision-making.
Use Hadoop to complement, not replace, existing data warehouses and platforms
Hadoop isn’t a magic cure to handle all use cases it can be plugged into. It plays a certain role in the big data ecosystem that tends to overlap with existing data environments, but doesn’t necessarily replace them. It’s important to know when to use Hadoop as a complement to existing data platforms, which have their own role to play.
Different kinds of databases have evolved over the years in ways that are difficult to replicate, even for a piece of software as monolithic as Hadoop. Time series databases (TSDB) are often used in organizations because they are the optimal solution for storing time-stamped data and getting insights from them.
The SQL architecture is also very important in processing relatively small amounts of data extremely fast. Using Hadoop for small projects would be an expensive overkill.
It’s also important, however, to acknowledge that Hadoop has its place. After all, SQL databases can only be scaled vertically, which can end up being more expensive than even Hadoop itself. Hadoop doesn’t use the same cleaning and structuring that SQL users are used to (Extract, Transform and Load), ETL, making it significantly faster and less error-prone when dealing with large sets of data.
Use Hadoop for the right project
The truth of the matter is that Hadoop is not well-suited for all projects and environments. And for all the popularity Hadoop enjoys, surprisingly little is said about how poorly it handles some kinds of projects.
Relational databases have undergone years of re-design and maintenance to make them the ultimate solution for processing structured data. As such, they are extremely good at that task – entering, storing, querying and analyzing data predefined in schemas.
Hadoop is hopelessly outclassed, both in terms of speed and performance when it comes to such. That’s all besides the fact that implementing SQL in Hadoop, while possible, requires you to jump through a dozen or so hoops before it’s all up and running.
Most large organizations don’t simply deal with data that can be defined in a simple format, however. They have to scour social media posts, deal with images, process text documents and read data from sensors. SQL databases don’t perform nearly as well when they lose the edge they have, thanks to principles such as ACID. Even worse, once the size of the data reaches a certain threshold, SQL databases become unwieldy – too expensive to scale any further and terrible performance to boot.
For example, it’s essential to remember that relational databases and warehouses still handle structured data, much better than Hadoop. Projects that need SQL statements are a hassle to implement in Hadoop, despite the number of attachments that enables it. Hadoop is also pretty bad at handling mixed workloads such as combined historical and real-time queries.
Conclusion
Ultimately, being able to get the maximum value out of Hadoop comes down to being able to identify where it’s suitable for use and where it isn’t. This includes knowing the guiding principles behind Hadoop’s schema-less design and whether it works well for your data, being familiar with the tools that makes Hadoop so powerful and finding a place where Hadoop can properly fit within your organization and align itself with your goals.
