Big Data As A Service (BDaaS): How Your Business Can Benefit From It
In the last few years, cloud-based innovations have accelerated at a phenomenal pace. There has been a proliferation of tools and services that facilitate efficient data storage and management for businesses and consumers, in addition to collaborative tools and services that allow organizations to engage and collaborate, and support collaborative work.
What is big data as a service (BDaaS)?
Big data as a service is a cloud-based service that can be used to rapidly manage and scale big data pipelines that are built using technologies such as Hadoop, Spark, and Solr.
According to Nick Cook, director of program management for Data Science, Cloud, and Big Data at Splunk, big data as a service is a platform for building, monitoring, and managing big data infrastructure. It can also be used to manage and scale the deployment of big data-related processes in enterprises.
“As organizations increase their investment in big data, they’re starting to see the complexity and cost of managing and scaling that big data infrastructure,” Cook said. “The idea behind big data as a service is to solve for both of those challenges.”
According to Cook, while big data has been in use for about 20 years, adoption and understanding have been on the rise in recent years.
The idea of what big data is can also be a challenge since there are numerous definitions, Cook said. Hadoop and Apache Spark are the two most widely recognized big data technologies, although Splunk has deep integration with both technologies, and both have independent product sets.
There are also many different big data initiatives that organizations can pursue, which also contribute to the complexity. Splunk solves a variety of use cases across different industries, Cook said. For example, Splunk works with financial services companies and retailers to help them detect fraud, monitor social media for customer sentiment, and prevent insider fraud.
The business case for big data as a service
One of the benefits of big data as a service is that it’s a platform that can help solve several common business challenges.
For one, you can bring data science skills in house without having to hire a team of data scientists, according to Angela Scaduto, senior director of product management at AppDynamics. “That’s extremely helpful for companies,” she said.
AppDynamics began as a big data play but is now a platform that “gets a whole lot bigger,” Scaduto said. The big data platform provides capabilities such as monitoring, alerting, and operational intelligence.
“You can have a whole bunch of data scientists and engineers running their own big data projects, but if you don’t have the monitoring, alerting, and operational intelligence capabilities, you can lose business,” Scaduto said.
Additionally, “it’s really challenging for companies to manage data infrastructure and the distributed nature of data” without bringing the entire operations team in house, Scaduto said. “BDaaS can help solve that problem,” she said.
The cost of big data is on the rise
The cost has impeded companies looking to expand their big data initiatives, Cook said.
“When you think about the cost of having your own data center, of having to buy and manage hardware and software, you can see how prohibitive that is, especially if you’re a small to medium-sized business or an enterprise,” he said.
Big data as a service, by providing a flexible cloud-based data environment, eliminates the cost barrier.
Cook said that for companies struggling to navigate big data, a big data platform is critical to achieving scalability and security requirements.
AppDynamics offers a variety of deployment options and pricing tiers for big data. In addition to the hosted data environment, the company also offers an on-premises version of its big data platform.
“At the end of the day, no one really wants to run big data on their own infrastructure anymore,” Scaduto said.
AppDynamics has a variety of pricing plans and a 99.99% uptime SLA, according to Scaduto.
What’s the BDaaS promise?
Since the very beginnings of data integration and management tools, companies have found themselves drawn to the promise of any tool that can make what should be relatively simple tasks simpler.
The promise has held through all the software’s iterations: transforming data for better visualization, giving a single source for reporting on progress, and improving the quality of dashboards.
The sheer simplicity of the promise makes it something nearly anyone could understand. But there’s a problem with the simple promise: most BDaaS products are a bad fit for the simple question, “is this tool that should do this already doing this?”
That’s because many BDaaS tools don’t provide the levels of control needed to use them. Indeed, the management tools many BDaaS tools provide are rudimentary compared to the more feature-rich reporting tools most companies use.
In a well-designed report, you want to see numbers, not charts, right? The answers to these questions are the starting point for a useful reporting tool, and it can make a lot of difference in how well a BDaaS will work for you.
How to answer the question: BDaaS or non-BDaaS?
To see how well a BDaaS is going to work, it’s helpful to start with a question we can answer in advance: will a tool you can use to integrate your cloud service with your data store be enough to make a reporting tool easy to use?
The answer to that question is no, at least not most of the time. As mentioned, many BDaaS tools are a poor fit for data integration work, so the question is, are you going to use a tool that’s a better fit for your goals, or will you invest in a tool with more options? And can you use that tool to integrate a tool that’s not well-suited to data integration?
The point is, it’s a mistake to think that BDaaS tools are simple, so if you find a tool that’s “better for your use” it will work well, even if it’s the only tool you’re going to use to integrate your cloud service with your data store.
It’s not too hard to see that BDaaS tools come into play with reporting. They’re simply data integration tools with a “write-through” interface for using third-party applications in many cases. Depending on what sort of report you’re making, you may be best off with a reporting tool. You could also be best off with something that provides more detailed reporting.
