Why Apache Spark is the Antidote to Multi-Vendor Data Processing

The big data open source landscape has evolved.

Organizations today have access to a whole gamut of tools for processing massive amounts of data quickly and efficiently. Among multiple open source technologies that provide unmatched data processing capabilities, there’s one that stands out as the frontrunner − Apache SparkTM.

Apache Spark is gaining acceptance across enterprises due to its speed, iterative computing, and better data access. But for organizations grappling with multiple vendors for their data processing needs, the challenge is bigger. They’re not just looking for a highly capable data processing tool, they’re also looking for an antidote to multi-vendor data processing.

Spark provides several advantages over its competitors that include other leading big data technologies like Hadoop and Storm. Enterprises have successfully tested Apache Spark for its versatility and strengths as a distributed computing framework that can handle end-to-end needs for data processing, analytics, and machine learning workloads.

Let’s find out what makes Apache Spark the enterprise backbone for all types of data processing workloads.


New Approaches to Real-time Anomaly Detection for Streaming Data

Detecting anomalous patterns in data can lead to significant actionable insights in a wide variety of application domains. Be it detecting roaming abuse and service disruptions in the telecom industry, identifying anomalous employee behavior that signals a security breach, or preventing out-of-pattern medical spends in incoming health insurance claims; big data anomaly detection has innumerable possibilities.

Anomaly detection has traditionally been driven using rule-based techniques applied to static data processed in batches, which makes it difficult to scale out as the number of scenarios grow. Modern data science techniques are far more efficient. Complex machine learning models can now be built using large amounts of unstructured and semi-structured data from disparate sources including business applications, emails, social media, chat messages, voice, text, and more. Moreover, the massive increase in streaming time-series data is leading to a shift to real-time anomaly detection, creating a need for techniques such as unsupervised learning and continuous models.

Following are some examples of how leading enterprises are using real-time anomaly detection to gain deeper insights and to swiftly respond  to a dynamic environment:

Real-time Anomaly Detection Use Cases Across Verticals