Notes about Cutting-Edge Technologies and Everything

Implementing Streaming Use Case From REST to Hive With Apache NiFi and Apache Kafka Part 1 With Apache Kafka 2.0 and Apache NiFi 1.8, there are many new features and abilities coming out. It's time to put them to the test. So to plan out what we are going to do, I have a high-level architecture diagram. We are going to ingest a number of sources including REST feeds, Social Feeds, Messages, Images, Documents, and Relational Data. We will ingest with NiFi and then filter, process, and segment it into Kafka topics. Kafka data will be in Apache Avro format with schemas specified in the Hortonworks Schema Registry. Spark and NiFi will do additional event processing along with machine learning and deep learning. This will be stored in Druid for real-time analytics and summaries. Hive, HDFS, and S3 will store the data for permanent storage. We will do dashboards with Superset and Spark SQL + Zeppelin. We will also push back cleaned and aggregated data to subscribers via Kafka ...

Tools:  Python 3.6, PyAudio, TensorFlow, Deep Speech, Shell, Apache NiFi Why : Speech-to-Text Use Case:  Voice control and recognition. Series : Holiday Use Case: Turn on Holiday Lights and Music on command. Cool Factor:  Ever want to run a query on Live Ingested Voice Commands? Other Options: Voice Controlled with AIY Voice and NiFi We are using Python 3.6 to write some code around PyAudio, TensorFlow, and Deep Speech to capture audio, store it in a wave file, and then process it with Deep Speech to extract some text. This example is running in OSX without a GPU on Tensorflow v1.11. The Mozilla Github repo for their Deep Speech implementation has nice getting-started information that I used to integrate our flow with Apache NiFi. Apache NiFi Flow   Read full article >>>  

With the upcoming HDP 3.1 release, we also bring about some exciting innovations to enhance our Kafka offering – New Hive Kafka Storage Handler (for SQL Analytics) – View Kafka topics as tables and execute SQL via Hive with full SQL Support for joins, windowing, aggregations, etc. New Druid Kafka Indexing Service (for OLAP Analytics) – View Kafka topics as cubes and perform OLAP style analytics on streaming events in Kafka using Druid. HDF 3.3 includes the following major innovations and enhancements: Core HDF Enhancements Support for Kafka 2.0, the latest Kafka release in the Apache community, with lots of enhancements into security, reliability and performance. Support for Kafka 2.0 NiFi processors NiFi Connection load balancing – This feature allows for bottleneck connections in the NiFi workflow to spread the queued-up flow files across the NiFi cluster and increase the processing speed and therefore lessen the effect of the bottleneck. MQTT performance improvements inc...