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When it Comes to Big Data, What Does SaaS Really Mean, and Who Should Use it?

By on March 25, 2016

Suddenly, the Cloud has become a hot topic in the analytics software world and in particular big data analytics. Vendors are introducing new products they call “analytics-as-a-service” or “big-data-analytics-as-a-service” (the latter an acronym that is similar to “bad ass” so we we’ll just let that joke die right here).

When one thinks of the model for Software-as-a-Service (SaaS), Salesforce.com often comes to mind. When they turned the CRM world on it’s head, they went beyond just giving someone hosted computers with CRM software on it, they made it “fully managed”, meaning:

  • Only one thing to manage – all the infrastructure, including software such as databases, was included and hidden from the customer.
  • Backups & scaling went behind the scenes – many of the administrative tasks for the application were taken care of for you. You didn’t need to do backups, or determine how to scale the application.
  • True ease of use – you didn’t need to program anything. Yes, there were ways to extend the solution like adding new schemas item and UI screens, but it was all drag-and-drop.

Using that model as the gold standard, what should a SaaS or cloud-based big data analytics solution do for you?

  • Hide the infrastructure – not just the computers, storage and networks, but also software components like Hadoop need to be buried and optimally used by the solution.
  • Remove the administration – eliminate the need to configure and manage the underlying Hadoop cluster and automatically perform operational tasks such as backups, upgrades and executing jobs in an optimal manner.
  • Eliminate programming – remove the need to program in Hadoop or Spark in any stage of the cycle – integration, preparation, analysis and visualization.

How to determine if “as-a-service” is the right on-ramp to Big Data for you

Consider your current staff and budget. People with big data skills, specifically Hadoop, are scarce and expensive. Data from Indeed.com shows salaries for people with Hadoop skills are much higher:

  • Someone with technical Hadoop skills is 86% higher for an administrator ($123,000 versus $66,000) and 30% higher for an architect ($133,000 versus $102,000) than people with standard IT skills.
  • A data scientist (people that combine data analysis skills with big data programming ability) commands $123,000, a hefty premium to the average salary of $62,000 for a data analyst and $89,000 for a business analyst.Indeed.com data

Consider your infrastructure budget. While it runs on commodity hardware, Hadoop costs can add up, especially when considering the above staffing costs to set up and maintain it. For the 54% of the Gartner universe that has no plans to invest in Hadoop, finding a fully-managed SaaS big data analytics solution allows them the on-ramp to big data without the associated costs.

Big Data SaaS Evaluation Criteria 

When evaluating, ask your solution provider the following questions:

  • Are all infrastructure components (hardware and software) included?
  • Do I need to administer the infrastructure?
  • Do I need Hadoop administrators?
  • Do I need Hadoop knowledgeable data scientists?
  • Do I need to administer the solution?
  • Does the solution have everything I need to integrate, prepare, analyze and visualize my data?

There are a number of vendors that will tell you they are SaaS for big data analytics, but they can’t answer some of those questions. Datameer Cloud is a complete SaaS solution for big data analytics. No infrastructure. No administration. No Hadoop skills needed. Just load your data and go. Want to know more?

  • Analyst firm Ventana Research shares 5 Best Practices for Cloud-Based Analytics in this paper and this live webinar.
  • Learn more about Datameer Cloud in this datasheet or this whitepaper


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John Morrell

John Morrell

John Morrell is Sr. Director of Product Marketing at Datameer.