Notes about Cutting-Edge Technologies and Everything

Modern analytics and business intelligence platforms represent mainstream buying, with deployments increasingly cloud-based. Data and analytics leaders are upgrading traditional solutions as well as expanding portfolios with new vendors as the market innovates on ease of use and augmented analytics. Details >>   (Provided by Looker here )

To gather insights on the current and future state of open source software (OSS), we talked to 31 executives. This is nearly double the number we speak to for a research guide and believe this reiterates the popularity of, acceptance of, and demand for OSS. We began by asking, "What Open Source software do you use?" As you would expect, most respondents are using several versions of open source software. Here's what they told us: Apache Apache Cassandra, Elassandra  (ElasticSearch + Cassandra) , Spark, and Kafka  (as the core tech we provide through our managed service) are the big ones for us. We find that the governance arrangements and independence of the Apache Foundation make a great foundation for strong open source projects. 95% of what we do with big data is open source. We use  Apache Hadoop  and contribute back to grow skills and expertise. We use so much that it would be impossible to list. The core of our software is based on  Apache Solr and Apache S

Type designers work with a diverse clientele, and yet common themes always seem to emerge in our conversations. This seems to be the season of complex typography, in which designers everywhere are faced with the challenge of presenting different and competing kinds of information to readers. An agency we’re working with is designing a demanding identity for a fast-moving consumer goods brand; an in-house art department is creating a responsive website for complex financial disclosures; a freelance graphic designer is doing the identity for a local coffeeshop, and discovering the joys and perils of digital menu boards. As always, the wrong fonts can lead designers into sticky dead ends, but the right ones can be immeasurably helpful. Here are some of the things our clients consider when faced with complex typography, and some of the typographic strategies that can be the quickest routes to success. Details:  https://www.typography.com/blog/fonts-for-complex-data

Data science and machine-learning platforms enable organizations to take an end-to-end approach to building and deploying data science models. This Magic Quadrant evaluates 16 vendors to help you identify the right one for your organization's needs. Details >> (Provided by Alteryx here )

CIOs in utilities and other asset-intensive organizations can use this research to support the development of enterprise APM strategies. APM is a key element of the foundational technology that can help their organizations achieve higher levels of operational reliability, safety and efficiency. Key Findings Asset performance management (APM) solutions are widening in scope and decreasing in deployment cost due to market acceptance, increasing competition and maturation of enabling technologies such as advanced analytics, algorithms, cloud and the Internet of Things (IoT). As APM solutions mature and cloud deployment increases, asset management will become a more collaborative process. Activities will be shared among asset owners, operators, service providers and OEMs. Asset management strategies are beginning to shift from preventive to predictive — driven by innovation in enabling technologies and streamlined access to consistent operational technology (OT) data resulting from I

The resource below describes the following frameworks: TensorFlow  Theano Caffe MXNet Apache SystemML (incubator project)  BigDL  DistBelief Details >>>

Big data (also called NoSQL) technologies facilitate the ingestion, processing, and search of data with no regard to schema (database structure). Web technologies such as Google, LinkedIn, and Facebook use big data technologies to process the tremendous amount of data from every possible source without regard to structure, and offer a searchable interface to access it. Modern NoSQL technologies have evolved to offer capabilities to govern, process, secure, and deliver data, and have facilitated the development of an integration pattern called the operational data hub (ODH). The Centers for Medicare and Medicaid Services (CMS) and other organizations (public and private) in the health, finance, banking, entertainment, insurance, and defense sectors (amongst others) utilize the capabilities of ODH technologies for enterprise data integration. This gives them the ability to access, integrate, master, process, and deliver data across the enterprise. Traditional mode

Comparing Top Deep Learning Frameworks: Deeplearning4j, PyTorch, TensorFlow, Caffe, Keras, MxNet, Gluon & CNTK:  https://deeplearning4j.org/compare-dl4j-tensorflow-pytorch

The analytics center of excellence has a new mandate for making the entire organization proficient in generating and leveraging automated insights. Data and analytics leaders should include a broad spectrum of organizational, project, data, educational and technological capabilities in their ACE. Key Challenges Data and analytics leaders often struggle to define, establish and communicate the range of analytics capabilities their teams can offer the organization. IT leaders such as CIOs regularly contend that they want to "get out of the report writing business" and expand the notion of analytics from BI application development to enable the entire organization to benefit from data and analytics. Tactical business intelligence competency centers (BICCs), having formed within and emerged from IT organizations, are too limited in scope and technology-focused to provide broad-spectrum analytic enablement. Leading enterprises in most industries generally are more successf

How to build a multilayered LSTM network to infer stock market sentiment from social conversation using TensorFlow: https://www.oreilly.com/ideas/introduction-to-lstms-with-tensorflow