Spry Squared is a Minority and Woman Owned Small Business headquartered in Denver, Colorado with offices across the United States of America. We are an experienced federal government and commercial service provider with security cleared personnel working on various projects across the USA and the globe. Spry Squared provides organizations with Best in Class Enterprise Solutions, Managed IT Services, Cybersecurity Solutions, IT Professional Services, Recruiting Services, Project/Program Management and technology products. We are your strategic partner and value-added reseller, solving complex business challenges by leveraging technology solutions that reduce costs, optimize productivity and minimize risk. Spry Squared is looking for a strong technical Data Engineer to join a team of highly empowered System Administrators, Developers, and Engineers to support the ETL workflows on classified data source networks (NIPR, SIPR, JWICS).

KredX is an online Bill Discounting Platform established in 2015 by a clutch of bankers and techies from IIT Kanpur & Stanford to disrupt the trade receivables discounting space in India. It has offices in Bangalore, New Delhi & Mumbai with total team size of around 55 spread across these three locations. KredX is also a series A Sequoia Capital funded company with established business model & growth avenues across the entire trade receivables space in India and already boasts of several large corporate as its clients. We are looking for an experienced Apache NiFi developer, who is passionate about building applications in the Cloud to read vast variety of data from disparate systems and transform them into usable formats. Primary responsibilities of the candidate will include - Integrate various ERP endpoints and transactional/nosql database systems - Ensure data synchronization well within accepted SLA and TAT defined - Closely work with the Product Manager and Engineering Manager for product and tech enablement - Create, execute, monitor and maintain interfaces supporting multi-tenancy mode - Daily Monitoring of the applications running on production.

Tim Spann is a Developer Advocate for StreamNative. He works with StreamNative Cloud, Apache Pulsar, Apache Flink, Flink SQL, Apache NiFi, MiniFi, Apache MXNet, TensorFlow, Apache Spark, Big Data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark.

Improve your skills - "Apache NiFi Complete Master Course - HDP - Automation ETL" - Check out this online course - Apache Nifi Apache Nifi is next generation framework to create data pipeline and integrate with almost all popular systems in the enterprise. It has more than 250 processors and more than 70 controllers. As part of production maintenance, user may have to take cautious decision to improve the performance and handle the errors efficiently. To have seamless experience with data, handling of data latency and throughput and prioritizing the data is important. Its controlled with relationship, yield and back pressure. Various processors and controllers to process various type of data is demonstrated.

In this talk I will show data engineers and architects how to run real-time TensorFlow Inception Image Recognition on images captured by remote sensors and images in tweets. In the same flow I will also demonstrate how to apply real-time sentiment analysis and intelligent routing of data to Phoenix, Email and Slack. I will elaborate on a number of different sentiment analysis frameworks available for use within Apache NiFi including Python NLTK, Stanford CoreNLP, Python SpaCy and Python TextBlob. This talk will be a deep dive into how to manage complex dataflow pipelines ingesting from multiple streaming sources including social, public open data feeds, logs, drones, RDBMS and IoT with transformations, deep learning, machine learning and business rules. Data engineers will be shown the power of Apache NiFi for loading diverse sources of data, applying transformations in-stream, routing based on attributes, adding sentiment data to workflows, running deep learning algorithms in stream and storing data into Apache Phoenix on HBase.

Gluon and Apache MXNet have been great for deep learning, especially for newbies like me. They added a Deep Learning Toolkit that is easy to use and has a number of great pre-trained models that you can easily use to do some general use cases around computer vision. So, I have used a simple well-documented example that I tweaked to save the final image and send some JSON details via MQTT to Apache NiFi. GluonCV makes this even easier! Again, let's take a simple Python example, tweak it, run it via a shell script, and send the results over MQTT.

This article is featured in the new DZone Guide to Artificial Intelligence. Get your free copy for more insightful articles, industry statistics, and more! I have spoken to thought leaders at a number of large corporations that span across multiple industries such as medical, utilities, communications, transportation, retail, and entertainment. They were all thinking about what they can and should do with deep learning and artificial intelligence. They are all driven by what they've seen in well-publicized projects from well-regarded software leaders like Facebook, Alphabet, Amazon, IBM, Apple, and Microsoft.

In this talk I will show data engineers and architects how to run real-time TensorFlow Inception Image Recognition on images captured by remote sensors and images in tweets and facebook posts. In the same flow I will also demonstrate how to apply real-time sentiment analysis and intelligent routing of data to Phoenix, Email and Slack. I will elaborate on a number of different sentiment analysis frameworks available for use within Apache NiFi including Python NLTK, Stanford CoreNLP, Python SpaCy and Python TextBlob. This talk will be a deep dive into how to manage complex dataflow pipelines ingesting from multiple streaming sources including social, public open data feeds, logs, drones, RDBMS and IoT with transformations, deep learning, machine learning and business rules. Data engineers will be shown the power of Apache NiFi for loading diverse sources of data, applying transformations in-stream, routing based on attributes, adding sentiment data to workflows, running deep learning algorithms in stream and storing data into Apache Phoenix on HBase and Apache Hive as ORC tables.

TensorFlow has released a Java API, so I decided to write a quick custom processor to run TensorFlow Inception v3. It's easy to add the new processor NiFi. Once you restart NiFi, you can add the TensorFlow Processor. An example flow is to the use the very smart ListFile, which will iterate through a list of files and keep track of the timestamp of files it last accessed. I point to a directory of files and the NiFi processor gets fed a ton of images to very quickly process.

Deep Learning is growing in power, scale, availability and frameworks. Open Source tools for Neural networks are everywhere, you have so much choice. Interesting new developments like PaddlePaddle, Keras and Deep Water are showing up and updating frequently. Unfortunately a lot of them take some serious power in GPUs, number of nodes, RAM, disk space, network bandwidth and CPU cores like IM2TXT for TensorFlow. These are very good uses of your 100 node Hadoop clusters especially ones running on AWS with GPUs.