Blog #1 in a series that will highlight the aspects of Pulsar that make it an attractive prospect for your messaging and data streaming needs.
Serverless ML, ML on AWS Lambda, ML on Google Cloud Functions, Scalable Serverless ML, Classify your dog for less than a penny!
Fist impressions of Astra, the new DBaaS from DataStax.
Netflix open-sourced Metaflow for performing data science and machine learning on cloud providers such as Amazon Web Services (AWS), Microsoft Azure and Google Cloud (GCP) - although optimized for AWS. What features does it provide?
Deep dimensionality exchange is a new deep learning protocol developed at Expero for performing domain transfer between domains of different dimensions.
Advances in automation of data analysis, graph analytics, data lineage, conversational queries and IoT analytics are widely predicted for 2020. Expero has capabilities and assets already in place.
Natural language processing is a powerful tool in building investigatory user interfaces for data exploration. See how conversation UI can be used to interrogate highly connected data to produce context driven data visualization and analytics.
Learn how Software Craftsmanship powers up Agile methods to move fast in complex projects.
JanusGraph tuning and performance best practices
Data products are productized versions of data science and machine learning initiatives that deliver value to end-users.
With Confluent and TigerGraph quickly emerging as high-quality enterprise software, learn how you can take your LDAP data, RBACs, and ACLs and quickly model and mirror them in a graph database using Kafka, a real-time streaming software.
Use Machine Learning to increase volume, maintain profits and meet the needs of customers while maintaining a simple business policy.
Given a large batch of healthcare data, we efficiently find similar patients to determine what remedies to recommend using traditional search methods and graph algorithms.
Learn how to avoid bugs and gain confidence when refactoring code by writing tests for your React code using Jest, React Testing Library and a Test Driven Development approach.
What makes Svelte a different UI framework and why you should give it a try. In this article you will learn the benefits of using Svelte, the new (and different) UI framework, as opposed to others like React, and Vue.
System Integration sourcing data for Graph Analysis.
Graph machine learning (graphML) is a subset of deep learning with much higher accuracy because big data records are linked together by their relationships.
Globalization (G11n) of an application involves more than just translating text. Internationalization (I18n) is the process of enabling your application to be used in different languages and culture. Localization (L10n) covers the work to provide the application in one specific language and culture. Selected locales can help in providing translated text, but some information needs to be converted (times, dates, currencies).
#NLP #MachineLearning #algorithm learns to tell #stories by summarizing #commercial #RealEstate #data, #earning #profits and spurring #CustomerRetention. #BINGO!
When localizing an application, treat the capabilities as features. Consider the specific use cases and work with the users to refine the approach. There may be design and layout adjustments needed per language. If the application is a CMS, content as well as application resources may need translations.
See why accumulators help TigerGraph’s GSQL query language standout among other native Graph Databases.
Reinforcement Learning at scale; Scheduling thousands of vehicles in multiple environments - how we made it work.
This can be like the one sentence description, but the more buzzwords the better. This is what google will pull from when people search.
Learn how to protect your resources by setting up serverless OAuth authentication with Auth0 and AWS Lambda@Edge.
Cassandra, DataStax, ScyllaDB, CosmosDB, RedShift - they all scale horizontally and with you will pay more as you add nodes - even if the SW is free, the compute time is not. Use Gatling to forecast your budget needs so you don’t surprise your CFO.
Digital Twins are the next logical step in an IoT implementation. Data storage for a digital twin can include a property graph database such as JanusGraph or DSE Graph, a time series database like TimescaleDB, and an analytical database like Redshift, Google Bigquery, or HP Vertica.
Building a data ingestion pipeline using Spark, Kafka, DataStax, Nifi, and Pentaho.
As of late Q2 of 2018, there’s a new entry into the graph database marketplace, Amazon Neptune.
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Using deep machine learning recommender system technology, Expero detects health care opioid fraud.
Deep learning, specifically recurrent neural networks, forecast nonlinear time series illness signals.
Wireframes are intended to call out key moments and interactions in software design in order to provide clarity into how something should look, feel, and function.
The property graph database space has been dominated by a handful of names who on balance are not that big in the software marketplace generally speaking.
A sneak peak of this year's Graph Day San Francisco talk on ACID & integrating JanusGraph with FoundationDB.
The graph database space is rapidly expanding as more and more companies identify potential use cases that require the traversal of highly connected network and hierarchical data sets in ways that are cumbersome with RDBMSs and NoSQL solutions.
Follow these tips to speed up your JanusGraph queries when running against a variety of storage backends including Apache Cassandra, ScyllaDB, Apache HBase, and Google Cloud Bigtable.
Single precision vs Double precision on a CPU vs GPU in high performance computing simulation.
Angular, React, Ember, Vue… There are a lot of javascript frameworks out there. Discover why an Angular programmer would use Vue over Angular any day of the week.
Reinforcement Learning of a deep neural network has been applied to the problem of supply chain logistics: In a stochastic environment, how to optimize pickup and delivery schedules.
Graph machine learning finds dissatisfied customer cohorts an recommends optimal intervention measures.
So many options in fast moving the graph database world, which one should you choose?
Money laundering and credit card fraud detection by graph machine learning.
Graph convolutional networks exhibit optimal deep learning on big graph data to gain business insight.
Explore your tuning options for increasing JanusGraph write throughput and lowering latencies.
Machine learning entity resolution deduplication of FBI criminal records using supervised learning logistic regression and unsupervised learning clustering.
Messages coming into your Spark stream processor may not arrive in the order you expect. Learn how to handle the unexpected with Spark, Databricks, and JanusGraph, DataStax, Neo4j, or Microsoft Cosmos DB.
Neo4j, DataStax Graph and Janus property graph schema design decisions for vertex and edge definitions.
Connecting to Microsoft Cosmos DB with Apache TinkerPop Dropwizard.
Deep-learning convolutional generative adversarial neural network crushes web design.
Graphs and graph datasets are rich data structures that can be used uniquely to improve the accuracy and effectiveness of machine learning workflows. Some of the key interactions are graph analytics as features, semi supervised learning, graph based deep learning, and machine learning approaches to hard graph problems.
Learn how Graph Technology can help to identify risk and fraud patterns in order to quickly respond. Many new fraud rings use sophisticated measures for credit card and other methods of fraud. Utilizing Graph technology will allow you to see beyond individual data points and uncover difficult-to-detect patterns. Join us to learn how to maximize time and resources with Graph technology vs. traditional relational database platforms.
Dave and Ted discuss Graphday and their thoughts on DSE, Neo4j, Microsoft's CosmosDB, and JanusGraph.
In this post, we're going to dive into the client-side single-page application, commonly abbreviated as “SPA”. What is considered an SPA? What are important choices to be made when building one? How do you deploy it? When is an SPA a good choice or a bad choice?
Trying to modernize monolithic legacy applications is hard: these applications are core drivers of the business and the risk of messing them up is too great. However, as time goes on, the cost of maintaining these monoliths grows.
In this blog post, I'll discuss the process of building a micro service that is backed by a graph database and the technologies leveraged to accomplish it. I'll be building this microservice in Java using Maven for its declarative dependency management and build process and Dropwizard for its straightforward architecture and configuration, and then connect everything up to an Apache Tinkerpop enabled Graph Database.
We get asked that question a lot given our early customer work with Titan evaluations, participation in the JanusGraph project and usage of Apache TinkerPop while concurrently being a premier DataStax Graph partner.
DataStax released 5.1, Neo4j released 3.2, Microsoft announces CosmosDB; there’s a lot of stuff happening in the graph database world. Looks like a prime time for some Gremlin training.
Software and web developers often wear many hats, including the UX/UI hat. But some developers lack the knowledge to design UIs or to collaborate effectively with UX designers and researchers.
If you know anything about Expero, you know we specialize in solving “complex problems.” This means we’re not working on your average brochure website or e-commerce app. We’re tackling apps and softwares targeted to niche domains with expert end-users who have very specific needs and goals to solve their very complicated problems.
Webinar presentation on how to combine Graph databases with an iterative, user-driven approach and the latest UI tools to enable your team to start creating valuable user experiences with graph data.
What does the term “search” mean to you?
A quick recipe for bringing up your first web worker with Webpack in the mix and share some tips to keep your implementation clean.
Web application types include static website, traditional server-side rendering, client-side single-page application, and isomorphic single-page applications.
One of the major hesitations from product stakeholders regarding end-user engagement, specifically user testing, is that they often don’t want anyone to see it till it’s “perfect” or “ready” or “MVP.”
In a previous post, we talked about untangling multiple UI controls so that they could be developed independently, but react to user interaction in a synchronized manner. Let’s posit for a moment updates to a line on one map control should cause re-rendering of a cross-section control associated with that line, but that the two controls are in different browsers, or even on different machines.
Reactive Synchronized UI Components: how I learned to stop worrying and love synchronized maps and cross-sections.
Recently I’ve come across the need to use an ambient logical context that would automatically flow with my program logic as it jumps from thread to thread. The .NET Framework has support for this when you are working with Threads and Tasks. It is not well documented, but it Just Works…mostly.
In the last post, we used ZooKeeper as a service registry. When services started, they registered with ZooKeeper at a pre-agreed place. (/services/{dataset-name}). Clients could list the data servers available and decide which ones to connect to, or request that new ones could be launched. Thanks to ephemeral nodes, servers can crash and their registry entries are automatically deleted. Today we’ll talk about three use cases for watching changes in ZooKeeper.
Zookeeper is a distributed database originally developed as part of the Hadoop project. It’s spawned several imitators: Consul, etcd, and Doozerd (itself a clone of chubby.) A lot of the material out there about Zookeeper describes just how it works, not necessarily what you’d use it for. In this series of posts, we’ll cover how we used it at one client — and it how it also got abused.
It starts innocently enough. You need a database connection string and to know which tables are safe to cache, and there’s just no sense in putting that in your source code. Right? I mean, why put hard-coded stuff in your programming language?
A good development team will already have a battery of tests in place to ensure their code is correct and can be refactored safely.
Especially early in product development, small custom-built datasets are the order of the day.
Most development is feature driven. A developer is on the line to complete a user story or functional requirement, and even if the application gets a little slower, she’d rather have a demo to show during sprint review, instead of watching every one else’s demo.
Every distributed system eventually requires messages to be written on the wire to be transmitted from one machine to another. In many cases these messages are hidden magic. Using WCF web services, or Thrift RPC, code-generated proxies make remote calls look like function calls.
How many times has a customer come and told you your product was “slow”? In this multi-part series, we will discuss how “slow” happens, and how you can fix it.
We were working with a potential client a few weeks ago, trying to figure out if we could help them improve some seismic processing software. The software had excellent science under the covers, but the visual interface was old and tired. Could Palladium help rejuvenate their user experience? Old looking software can imply old or out of date capabilities. Could we make it, well, better?
Implementing this new logic “the declarative way” is an interesting and typical example of how we use Knockout and Rx to capture the logic and business rules in declarative statements that just work and avoid the complex logic you’d need to implement this functionality imperatively.
When writing a complex single page web application, you find yourself dealing with A LOT of asynchronous activities. The user is busy clicking on UI elements and typing in text boxes. Asynchronous requests and responses are flitting back and forth between the server and client. Perhaps you’ve even got asynchronous messages coming from web workers.
Up until recently, Firefox was the only Windows 7 browser which had support for multitouch. In a recent build, Chrome has also added experimental support. Unsurprisingly, they’ve used the same touch model used by mobile safari.
There’s an old standby which tells us that A supercomputer is a device for turning compute-bound problems into I/O-bound problems.
In the kind of programming we do — scientific simulations and decision support — modeling is usually the first task, and often the hardest. Structuring your problem the right way can make all the difference in determining whether future code is graceful or spaghetti-like.
I had an interesting discussion with a coworker while planning a new feature for our current project. We are practicing Domain Driven Design and maintain a strict separation between the GUI and the domain logic. We actually treat them as separate applications.
We are meeting more people who are interested in looking into the world of graph databases. Palladium has executed proofs–of–concept for clients to help them explore this world. In this post we summarize what sorts of questions we feel like a proof of concept project can answer, and how we typically tackle them. For our presentation at Graph Day, we’ll be walking through one in particular, but really there are a variety of answers you may want.
A supernode is a vertex with a large number of edges. In graph theory, these high-degree vertices are known as hubs.
When designing and developing software, it is critical to take into account the limitations of the technology employed, especially hardware—things like computers, boxes and other physical devices. But there’s another aspect to hardware that should be taken into account and is often overlooked: the user.
OrientDB is one of several popular graph data stores on the market today. It provides a multi-model approach with the powerful nature of a graph database and the flexibility of a document data store. If you have decided to build out your multi-tenant application on top of OrientDB, you are in luck as it has several built-in, out-of-the-box methods for handling multi-tenancy.
How do you handle customer #2? You delivered an MVP of some hosted software for customer #1. Your brother-in-law knows a guy who has a similar problem and after a lunch meeting, now you need to add customer #2 to your incubating SaaS tool. Of course customer #1 and customer #2 shouldn’t be able to see each other’s data, but you don’t necessarily want to install and configure everything all over again just because you added another customer.
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