Systems | Development | Analytics | API | Testing

If there was a theme around Appian 20.3, it is building on top of an already capable platform. For example, Appian records have been a customer favorite for several years. Records are easy-to-build, and they centralize data from multiple sources into a single actionable view. In this release, we build on the power of Appian records with record-powered grids.

Spark is known for its powerful engine which enables distributed data processing. It provides unmatched functionality to handle petabytes of data across multiple servers and its capabilities and performance unseated other technologies in the Hadoop world. Although Spark provides great power, it also comes with a high maintenance cost. In recent years, innovations to simplify the Spark infrastructure have been formed, supporting these large data processing tasks.

Everybody needs more data and more analytics, with so many different and sometimes often conflicting needs. Data engineers need batch resources, while data scientists need to quickly onboard ephemeral users. Data architects deal with constantly evolving workloads and business analysts must balance the urgency and importance of a concurrent user population that continues to grow.