Using big data analytics and predictive analytics through deep learning (DL) are essential strategies to make smarter, more informed decisions and provide competitive advantages for your organization. But these tactics are not simple to execute, and they require a properly designed hardware infrastructure.
There are several key factors to consider when designing and building an environment for big data workloads.
Without a properly designed infrastructure, bottlenecks in storage media, scalability issues, and slow network performance can become huge impediments to success. Here are some key considerations to keep in mind to ensure an infrastructure that is capable of handling big data analytics workloads.
While every organization is different, all must address certain challenges to ensure they reap all the benefits of big data analytics. One challenge is that data can be siloed. Structured data is typically highly organized and easy to decipher. Unstructured data is not as easily gathered and analyzed. These two types of data are often stored in separate places and must be accessed through different means.
Unifying these two disparate sources of data is a huge impetus for big data analytics success, and it is the first step to ensuring your infrastructure will be capable of helping you reach your goals. A unified data lake, with both structured and unstructured data located together, allows all relevant data to be analyzed together in every query to maximize value and insight.
But a unified data lake can lead to projects that tend to involve terabytes to petabytes of information. These massive amounts of data need infrastructure capable of moving, storing, and analyzing vast quantities of information quickly to maximize the effectiveness of big data initiatives.
Designing an infrastructure for DL creates its own set of unique challenges. You typically want to run a proof of concept (POC) for the training phase of the project and a separate one for the inference portion, as the requirements for each are different.
It’s essential to understand the infrastructure needs for each workload in your big data initiatives. These can be broken down into several basic categories and necessary elements.
Understanding the storage requirements for big data analytics and DL workloads can be challenging. It’s difficult to fully anticipate the application profiles, the I/O patterns, or the predicted data sizes before ever actually experiencing them in a real-world scenario. That’s why infrastructure performance for compute and storage can be the difference between success and failure for big data analytics and DL builds.
Software-defined storage (SDS) is a technology used in data storage management that intentionally separates the functions responsible for provisioning capacity, protecting data, and controlling data placement from the physical hardware on which data is stored. SDS enables more efficiency and faster scalability by allowing storage hardware to be easily replaced, upgraded, and expanded without changing operational functionality.
Your goals for your big data analytics and DL initiatives are to accelerate business decisions, make smarter, more informed decisions, and to ultimately drive more positive business outcomes based on data. Learn even more about how to build the infrastructure that will accomplish these goals with this white paper from Silicon Mechanics.
Silicon Mechanics, Inc. is one of the world’s largest private providers of high-performance computing (HPC), artificial intelligence (AI), and enterprise storage solutions. Since 2001, Silicon Mechanics’ clients have relied on its custom-tailored open-source systems and professional services expertise to overcome the world’s most complex computing challenges. With thousands of clients across the aerospace and defense, education/research, financial services, government, life sciences/healthcare, and oil and gas sectors, Silicon Mechanics solutions always come with “Expert Included” SM.
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