Accelerating Time to Value of Big Data with Apache Spark
Boosting the power of Apache Spark to exceed the demands of enterprise workloads
Enterprises are capturing and processing ever-increasing amounts of data as networked devices, instruments, and applications proliferate globally. Data creation is exploding at exponential rates and companies are finding new ways to drive data-driven business strategies.
Distributed data processing engines such as Hadoop and Spark have emerged to serve this critical need. Apache Spark has been widely adopted for machine learning, stream processing, batch processing, ETL and complex analytics due to its high-performance distributed data processing capabilities.
Qubole hosts a version of Apache Spark that has been optimized for big data workloads. This ebook gives you a deeper look at how Apache Spark on Qubole:
- Improves the performance of Apache Spark for big data workloads
- Reduces costs by as much as 50%
- Delivers unmatched scale and reliability for enterprise big data workloads