Read more about our open source commitment, projects, research and knowledge in our open source blogs.
In contrast to traditional data processing systems that provide one dedicated execution engine, Apache Wayang transparently and seamlessly integrate multiple execution engines and use them to perform a single task as a DAG, which enables AI across any connected data platform, be it JDBC, Spark, Flink or Kafka.
LSTEnergy is an LSTM model that enables users to better understand energy consumption, predict energy flows more accurately, and consequently, save energy and reduce CO2 emissions. LSTEnergy performs with a high probability after approximately 20 epochs, depending on the dataset used.
Apache Spark is an open-source distributed processing system used for big data workloads. It utilizes in-memory caching and optimized query execution for fast analytic queries against data of any size.
Apache Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale. It can process unbounded and bounded data streams.
Apache Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models, uniting multiple data tools.
Apache Impala is an open-source, native analytic database for Apache Hadoop. It provides low latency and high concurrency for BI/analytic queries on Hadoop, which is not delivered by batch frameworks such as Apache Hive.
TensorFlow is a platform for machine learning, TTF is made for federated TensorFlow. It supports distributed training, immediate model iteration and easy debugging with Keras, and much more.