Choose a solution that can deliver deep insights into data without reinventing the wheel. Standardization can help you move large quantities of data across multiple systems, allowing you to take advantage of data no matter its source.
What are your big data requirements? Determine which type of Apache Hadoop user you fit most closely. Take a short quiz to see what kind of Hadoop user you likely are and what you likely need from Hadoop to be successful.
Data scientists may be of a different breed from other analytics team members, but they are essential for bringing to the table curiosity about data and an unquenchable thirst for finding patterns and relationships in that data. Discover how combining the roles of data scientist, business analyst,
Big data has shown itself to be an illuminating force for sourcing the insight that is powering a tremendous transformation in modern life. To keep pace with the rapid changes, today’s organizations are seeking to improve their capabilities, competencies and culture to turn data into business value
Why are people talking about Apache Spark? It’s because many organizations are using the myriad features of this open source engine to boost their predictive analytics processing. The result? Better, deeper and faster data analyses with reduced coding time and effort.
Hadoop is great for storing and processing large data volumes, but its limits become clear when integrating ever-increasing volumes of data. A new solution—described in detail at the upcoming Strata+Hadoop World conference—can help organizations overcome this limitation.
While some observers may argue that Apache Spark is causing the relevance of the Apache Hadoop community to wane, the fact of the matter is innovative Spark development depends on Hadoop platforms. Discover why Hadoop is stronger than ever as an open source information refinery that is expected to
When customers or other key stakeholders expect to be able to connect with an organization instantaneously, extremely low latency, high throughput data and analytics flows and execution are absolutely essential. The advent of the Internet of Things is among several key drivers of the emergence of
Streaming analytics is becoming ubiquitous as data streams from a range of sources, including the Internet of Things, are now mainstream. Although streaming analytics is not a new technology, it is well suited for today’s real-time, low-latency business and consumer applications. And today’s data
An ever-changing business environment is forcing data professionals to rethink their work methods—but fortunately, help is available. Here are five trends that are making life easier for data professionals: the emergence of Apache Spark, opportunities for greater skill reuse, growth in online and
Apache Spark will become a core technology in the logical data warehouse (LDW), and its sweet spot is as the workbench of choice for data scientists who interactively and iteratively explore, build and tune statistical models for machine learning, graph and streaming analytics.
Big data without context is pretty much useless, especially when that context can fluctuate so widely—which is why the role of Hadoop in creating accurate analytics is crucial for deriving value from big data.