hire Apache Hadoop developers

Apache Hadoop entered the landscape in 2006, and it’s a highly popular framework in the era of big data. But, like all technologies, there are advantages and disadvantages to the framework.

Pros

• Affordable: as an open-source solution that relies on commodity hardware, it is cost-effective.
• Flexible: It can handle nearly any kind of dataset, including structured, semi-structured, and unstructured, with a high degree of efficiency.
• Scalable: large amounts of data are dividable between multiple low-cost machines to create clusters with parallel processing, leading to scalability.
• Speed: parallel processing increases speed, leading to a high degree of performance over traditional database management systems.
• Continuity: data is replicated across various data nodes within the cluster, providing continuity if a single piece of commodity hardware fails.

Cons

• Small file issues: handling a large number of small files is challenging in Hadoop, as that’s not what it’s designed to do specifically. However, if you hire a Hadoop developer or programmer with ample experience, it’s possible to address the issues.
• Highly complex: It is a complex framework with a steep learning curve, which is why it’s critical to find Hadoop developers with prior experience of similar projects.
• No real-time data processing: while it’s excellent at processing massive quantities of data, it doesn’t provide real-time outputs without supplemental solutions.
• Security challenges: It relies on Java, which is widely known. Additionally, it uses Kerberos authentication, which is challenging to manage. Since that creates security issues, you tap highly skilled Apache Hadoop programmers for hire when forming your team.
• Significant processing overhead: It relies on a series of read/write processes, not in-memory calculations, which leads to substantial processing overhead

When to use Apache Hadoop and when not to use it

Before you hire Hadoop engineers to assist with your project goals, it’s critical to know when to use the framework and when not to use it. Here’s an overview of situations where Hadoop is a solid choice:

• Working with massive quantities of data
• Dealing with a mix of structured, semi-structured, and unstructured data
• When you need to combine affordability and scalability
• Requiring integration with a range of analytics tools
• Getting access to parallel processing capabilities
• If continuity is a concern

As for when using Hadoop isn’t a great idea, there are several situations that qualify. If you need real-time analytics, it isn’t ideal. Similarly, it isn’t designed with small files or tiny datasets in mind. There are also security issues to navigate, but those are potentially overcome if you hire Hadoop experts or find Hadoop developers to add to an already security-oriented team.