Enterprises have been collecting ever-larger amounts of data with the goal of extracting insights and creating value. Yet despite a few innovative companies who are able to successfully exploit big data, the promised returns of big data remain elusive beyond the grasp of many enterprises.
One notable and rapidly growing open source technology that has emerged in the big data space is Apache Spark.
Spark is an open source data processing framework that was built for speed, ease of use, and scale. Much of its benefits are due to how it unifies critical data analytics capabilities such as SQL, machine learning and streaming in a single framework. This enables enterprises to simultaneously achieve high performance computing at scale while simplifying their data processing infrastructure by avoiding the difficult integration of many disparate and difficult tools with a single, powerful, yet simple alternative.
While Spark appears to have the potential to solve many big data challenges facing enterprises, many continue to struggle. Why? Because capturing value from big data requires capabilities beyond data processing; enterprises are finding out that there are many challenges in their journey to operationalize their data pipeline.
First there is the infrastructure issue requiring data teams to pre-provision, setup and manage on-premise clusters that are both costly and time consuming. After solving the imminent infrastructure challenges, enterprises still have to contend with primitive tools that are difficult to use where working with data, code, and visualization requires switching between different software. These tools also force individuals to work in silos, stifling collaboration and making the sharing of work and communication of insights to the rest of the organization difficult.
In this typical scenario, enterprises are forced to take on the difficult task of building custom capabilities on top of Spark in order to operationalize it as an effective data platform. This severely reduces the productivity of data analytics teams, degrades their ability to focus on core tasks, and renders every big data project highly susceptible to failure. Indeed Gartner states that, “through 2017, 60% of big-data projects will fail to go beyond piloting and experimentation and will be abandoned.”
Instead of attempting to operationalize Spark in-house, enterprises can benefit from obtaining Spark and the capabilities necessary to operationalize it in a single package that is easy to deploy, simple to learn, and provides the rich set of tools out-of-the-box. One of the key attributes of Spark as-a-service providers, such as Databricks, is their ability to provide Spark as-a-service to enterprises in a unified cloud-hosted data platform.
These services provide fully managed Spark clusters that can be dynamically scaled up and down in a matter of seconds. This frees enterprises to focus on extracting value out of their data instead of spending their valuable resources on operations. In addition to Spark as-a-service,many of these vendor software platforms include other critical components required by enterprises to fully develop, test, deploy and manage their end-to-end data pipeline from prototype, all the way to production with no re-engineering required. These include:
- An interactive workspace for exploration and visualization so teams can learn, work and collaborate in a single, easy to use environment;
- A production pipeline scheduler that helps projects go from prototype to production without re-engineering;
- An extensible platform that enables organizations to connect their existing data applications with Spark to disseminate the power of big data.
With these critical components, enterprises could seamlessly transition from data ingest to exploration and production while leveraging the power of Spark. They can overcome the existing bottlenecks that impede their ability to operationalize Spark, and instead, focus on finding answers from their data, building data products, and ultimately capturing the value promised by big data.