Bigstream Hyperacceleration boosts performance for almost any Spark application thanks to our platform approach. Here are some key solutions and use cases that particularly benefit from Bigstream Hyperacceleration.

Click on the different parts of the diagram for more information.

  • Real-time Analytics
  • Batch Analytics
  • Cybersecurity
  • Financial

While Data Ingest is a component of many data pipelines, it also stands alone as a key computing challenge that can greatly benefit from software and hardware acceleration. Fast Data Ingest is especially needed for high-volume Extraction, Transformation and Load (ETL) for Data Lakes, as well as Hadoop powered Enterprise Data Warehousing (EDW), IoT data capture, and a host of other similar applications. Read more

The disruption of traditional ELT and Enterprise Data Warehousing (EDW) has been one of the most notable impacts of Big Data platforms such as Hadoop, Spark, and NoSQL databases. Data Lakes now often provide many of the essential functions of EDW that remain relevant in the era of machine learning, and advanced analytics. Read more

IT departments everywhere are discovering that cybersecurity has become a Big Data problem. With a constantly evolving threat landscape, security professionals must increase both the sophistication and speed in which they can respond to these threats. Read more

A growing proportion of analytics in financial services is driven or influenced by Big Data. Specific areas of growth include trading, banking, investing and payments. Major Big Data platform vendors of the Hadoop and Spark ecosystems cite financial services as early innovators in predictive analytics, fraud detection, and a host of other use cases. But the new Big Data ecosystem also must process more fast data in finance for performance critical services and tasks. Read more

New Machine Learning (ML) and Artificial Intelligence (AI) workloads are pushing the limits of scale and processing power of distributed systems and enterprise data centers. There is considerable effort focused on increasing compute density with hardware accelerators such as GPUs, FPGAs, and even custom ASICs. Read more