The Challenge of Managing Essential Big Data Infrastructure in a Complex IT Landscape

As data becomes the new fuel, there is growing investment in Big Data infrastructure. But deploying large data centres or cloud environments to manage your Big Data requirements is not enough. The infrastructure also has to be managed and maintained effectively – and continuously.

Unfortunately, for your in-house data engineers, managing Big Data infrastructure is extremely cumbersome; for them to efficiently solve daily Big Data operational challenges without losing focus on business objectives often gets too overwhelming.

To that end, they need proven Big Data Managed Services that streamline Big Data operations and empower the business to focus on what they do best and not worry about managing their complex Big Data infrastructures.

Continuous data growth, ever-evolving user needs, and the demand for 100% application uptime have every organization scouting for ways to manage their Big Data infrastructure efficiently. Since poor monitoring and support of Big Data infrastructure can jeopardize the overall business objectives and put the organizations behind their competition, proper and efficient management via a competent team is crucial – not just to successfully operate the Big Data implementation project but also to scale it to meet growing needs.

The right approach to Big Data infrastructure management can deliver several benefits: it can help tame big data storage, streamline and secure access, and improve provisioning. It can also help ensure data infrastructure’s seamless scalability, improve network connectivity, and enable optimal data transformation. Moreover, when infrastructure is efficiently managed, it can help IT teams find and fix data quality issues in time, address data integration requirements, and keep costs out of control.

The significance of Big Data is being realized globally. With the pace of technological innovation, Big Data significantly transforms modern-day data centers by delivering advanced business intelligence for timely and effective decision-making. However, the technology also poses various challenges related to infrastructure, resource constraints, and several security and privacy issues.

Here’s an overview of the top challenges IT teams face while managing their Big Data infrastructure:

• Storage issues: Limited or inflexible storage is another bottleneck that leads to performance and scalability challenges. The rapidly increasing volume of data requires a highly flexible and scalable storage system. However, traditional file systems cannot support massive volumes of data. At the same time, they are also incapable of automatically updating data, which brings in high latency levels.
• Non-availability of the right data: The success of any Big Data initiative depends a lot on its ability to act on the right data at the right time. But IT teams often face frequent incidents of MapReduce or Spark job failure – primarily due to the non-availability of “actual available” data – which impacts the project’s overall efficiency.
• Data security breach incidents: IT teams face the challenge of preventing them. Without the right security controls or authorization measures, there is a high likelihood of unauthorized users accessing, manipulating, and sharing data. This can put the entire Big Data project and the business as a whole in jeopardy.
• Failed jobs: For Big Data projects to deliver the right outcomes, systems and tools must run efficiently 24×7. But there is always the probability of jobs failing. At the same time, there is also the possibility of downstream system jobs failing due to the non-completion of Hadoop jobs on time. When such issues occur during non-working hours, timely analysis or rectification cannot happen due to the sheer unavailability of resources.
• Missed SLAs: Another challenge that is commonly faced by IT teams is the high likelihood of missed SLAs. Despite Hadoop clusters having the best configuration, most teams need help to meet their SLAs because they cannot efficiently maintain their infrastructure.

The key reason for all the challenges affecting IT teams when managing their complex Big Data infrastructure is the growing skills gap. Big Data requires the skilled oversight of highly proficient and trained resources to effectively manage the ever increasing volume, velocity, and variety of data.

In-house IT teams often need more skills and find themselves needing help to drive the right results from their Big Data Projects. To that end, outsourcing Big Data infrastructure management to skilled resources can resolve these challenges and give your team the time and space to focus on business objectives and innovation. Opting for Big Data Managed Services from organizations like Emergys can help:

• Implement the right tools, techniques, and strategies to improve efficiency and reduce costs.
• Streamline Big Data operations and ensure minimal downtime.
• Deliver transparent and real-time insights into cluster health.
• Predict infra failure before it happens instead of taking steps after incidents.
• Design, install, and configure the right Hadoop clusters and perform necessary upgrades, patches, and migration.
• Drive continuous improvement across availability and total cost of ownership (TCO).
• Enable access to the latest industry best practices and reusable methodologies.
• Stay current on technology advances, market trends, and customer needs.
• Efficiently deal with growing volumes of structured and unstructured data and enable tactical decision-making – in real-time.

Big Data is revolutionizing how organizations use data to make informed decisions and steer the business to success. But as Big Data requirements grow, sustaining and optimizing the underlying infrastructure is critical to driving the right outcomes.

Contact us today for 24/7 support for your Hadoop cluster, operating system, and cloud platform. Ensure continuous performance monitoring, incident management, reporting, and backup and recovery.

Get in touch to learn more.