Big-Data-As-A-Service (BDaaS): Sparking the Next Big Data Evolution

2017 may finally be the year for Big Data as a Service (BDaaS). Businesses have been capturing data frantically, but, with a few exceptions, have failed to reap the full benefit of the data they’ve gathered. But there are two converging trends in 2017 that make us believe more enterprise organizations will finally leverage their big data to reap big rewards.

First, according to IDG’s Enterprise Cloud Computing Survey, enterprise organizations will take their data to the private or public cloud in record numbers this year. Cloud computing represents a significant portion of enterprise-level IT budgets in 2017.

Second, BDaaS will allow more of these enterprise organizations to simplify deployment of data analytics and business intelligence tools, and scale their architecture as needed. One such BDaaS application is Spark, Apache’s open source distributed processing and analytics platform.

The benefit of BDaaS is that it allows businesses of all sizes to consider big data projects. Big data has been democratized, and projects leveraging BDaaS are no longer out of reach.

Making Big Data Useful—Spark-as-a-Service

A number of providers are offering Spark-as-a-Service. Let’s take a look at InfoWorld’s 2017 Technology of the Year winner, Databrick. Databrick is Apache’s Spark machine learning and analytics platform.

Spark has a robust platform for data scientists and can process data quickly from a number of repositories. Spark-as-a-Service allows organizations to load data into the cloud and manipulate it as needed. It streamlines many of the manual processes that have slowed down business intelligence efforts over the past few years. Spark was Apache’s answer to Hadoop’s MapReduce batch processing backbone, which didn’t do well with the real-time analytics found in cloud computing.

Spark’s ecosystem includes:

• -Spark SQL and DataFrames
• -Streaming
• -MLlib (Machine Learning)
• -GraphX (Graph Computation)

With machine learning and artificial intelligence the logical next step for big data, we must call out a few of the Machine Learning functions of Spark, including:

• -Summary statistics
• -Correlations
• -Sampling
• -Hypothesis testing
• -Classification and regression
• -Collaborative filtering
• -Cluster analysis
• -Dimensionality reduction
• -Feature extraction
• -Transformation functions
• -Optimization algorithms

Building each Spark cluster is labor intensive but Databricks optimizes the software by completing the configuration automatically—just set up the memory capacity and the platform does the work.

Big Data - All Cloud, no Bare Metal

Before the era of BDaaS, big data was bare metal on-premise. Today, even the most conservative enterprise organizations are seeking cloud convenience, including self-service and the agility of DevOps applied to big data. Container technology has allowed large data nodes to move to the cloud, signaling what might be the next iteration of big data. BDaaS options like Spark now allow enterprise organizations with the biggest datasets to leverage cloud convenience.

Bigstep’s BDaaS offering is the DataLab, a cloud-driven solution that merges data science with analytics. Bigstep DataLab integrates Apache Spark for data exploration and computation that can encompass functions from machine learning to graph processing. Bigstep DataLab has a self-service portal with data visualization tools that help make big data accessible to novice audiences. To get started, contact us today.