Orchestrate all the Things

GraphQL is a specification that came at just the right time to address an age-old issue in software engineering: service integration. Apollo's implementation is seeing lots of traction, and it just got more gas in the tank for its grand vision that goes well beyond integration.

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Machine learning is eating the world, and spilling over to established disciplines in software, too. After MLOps, is the world ready to welcome MLGUI?

Philip Vollet is somewhat of a celebrity, all things considered. Miley Cyrus or Lebron James he is not, at least not yet, but if data science lives up to the hype, who knows.

As the senior data engineer with KPMG Germany, Vollet leads a small team of machine learning and data engineers building the integration layer for internal company data, with access standardization for internal and external stakeholders. Outside of KPMG, Vollet has built a tool chain to find, process, and share content on data science, machine learning, natural language processing, and open source using exactly those technologies, which makes for a case of meta, if nothing else.

There is a flood of social media influencers sharing perspectives on data science and machine learning. While most influencers direct their attention solely toward issues of model building and infrastructure scaling, Vollet also looks at the user view, or frameworks for building user interfaces for applications utilizing machine learning. We were intrigued to discuss with him how building these user interfaces is necessary to unlock AI's true potential.

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Photo by Kelly Sikkema on Unsplash

Open source software used to be poorly understood by commercial forces, and it's often approached in a biased way. A new generation of investment funds goes to show that things are changing.

Open source software is a lot of things to a lot of people. For some people -- engineers, mostly -- it's a way to work on their passion, be involved in a community, and give something back to the world. For others -- business people, mostly -- it's a way to grow projects organically, and sell software without actually investing too much in sales.

It's a nuanced topic, and we've tried to explore it from many angles. From the business angle, exploring open source vendors relationships with hyperscalers, mostly Google and Amazon. From the contributor and license engineering angle, exploring different models for commercial open source projects. And from the community angle, exploring metrics for community health and value generation evaluation.

Today, we explore open source software (OSS) and its commercialization from yet another angle - the investment angle. There are a couple of venture capitals out there that seem to be ahead of the curve in terms of their understanding of, and investment in, commercial open source companies. Runa Capital is one of them, and we caught up with Konstantin Vinogradov, Runa Capital Principal, who shared views, findings, and outlook for commercial OSS.

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It's a data terminology mess out there. Let's try and untangle it, because there's more to words than lingo.

Hopefully technology investment decisions in your organization are made based on more than hype. But as technology is evolving faster than ever, it's hard to keep up with all the terminology that describes it. Some people see terminology as an obfuscation layer meant to glorify the ones who come up with it, hype products, and make people who throw terms around appear smart.

There may be some truth in this, but that does not mean terminology is useless. Terminology is there to address a real need, which is to describe emerging concepts in a fast moving domain. Ideally, a shared vocabulary should facilitate understanding of different concepts, market segments, and products.

Case in point - data and metadata management. Have you heard the terms data management, data observability, data fabric, data mesh, DataOps, MLOps and AIOps before? Do you know what each of them means, exactly, and how they are all related? Here's your chance to find out, getting definitions right from the source - seasoned experts working in the field.

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A $325 million Series F funding round, bringing Neo4j's valuation to over $2 billion. A social network of 3 billion people, distributed across 1000 servers. The latter is a demo, the former is not. But both are real signs that the graph market and Neo4j are getting seriously big.

If you're into the market and investment side of things, how does a Series F funding round as part of a $325 million investment led by Eurazeo and GV (formerly Google Ventures), bringing Neo4j's valuation to over $2 billion sound? Pretty impressive, probably.

If you're into the technology and applications side of things, how does a Neo4j demo of a social network application with 3 billion people, running queries designed to test the limits of graph query languages and databases across a 1000 node cluster sound? Equally impressive, probably.

Graph database vendor Neo4j​ CEO and co-founder Emil Eifrem is announcing the former and showcasing the latter today, at the company's annual virtual conference NODES. We caught up with Eifrem to get a taste of things to come.

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Being able to deploy machine learning applications at the edge is the key to unlocking a multi-billion dollar market. TinyML is the art and science of producing machine learning models frugal enough to work at the edge, and it's seeing rapid growth.

Edge computing is booming. Although the definition of what constitutes edge computing is a bit fuzzy, the idea is simple. It's about taking compute out of the data center, and bringing it as close to where the action is as possible.

Whether it's stand-alone IoT sensors, devices of all kinds, drones, or autonomous vehicles, there's one thing in common. Increasingly, data generated on the edge are used to feed applications powered by machine learning models.

There's just one problem: machine learning models were never designed to be deployed on the edge. Not until now, at least. Enter TinyML.

Tiny machine learning (TinyML) is broadly defined as a fast growing field of machine learning technologies and applications including hardware, algorithms and software capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of always-on use-cases and targeting battery operated devices.

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Graphs are everywhere. That has been the motto of graph afficionados for years, and now it seems that the world is waking up to this.

How would you feel if you saw demand for your favorite topic, which also happens to be your line of business, grow 1000% in two-years time? Vindicated, overjoyed, and a bit overstretched in trying to keep up with demand, probably.

Although Emil Eifrem never used those exact words when we discussed the past, present and future of graphs, that's a reasonable projection to make. Eifrem is the CEO and co-founder of Neo4j, a graph database company which lays claims to having popularized the term "graph database", and to leading the graph database category.

Eifrem and Neo4j's story and insights are interesting because through them we can trace what is shaping up as a foundational technology stack for the 2020s and beyond: graphs.

"Graph Relates Everything" is how Gartner put it, when including graphs in its top 10 data and analytics technology trends for 2021. Interest is expanding as graph data takes on a role in master data management, tracking laundered money, connecting Facebook friends and powering Google, in search and beyond. 

Think Panama Papers researchers, NASA engineers, and Fortune 500 leaders: they all use graphs. Here's why, and how.

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Image: Getty Images

Ensuring data quality is essential for analytics, data science and machine learning. Superconductive's Great Expectations open source framework wants to do for data quality what test-driven development did for software quality

Technical debt is a well-known concept in software development. It's what happens when unclear or forgotten assumptions are buried inside a complex, interconnected codebase, and it leads to poor software quality. The same thing also applies to data pipelines, it's called pipeline debt, and it's time we did something about it.

That's the gist of what motivated Abe Gong and James Campbell to start Great Expectations in 2018. Great Expectations is an open-source tool that aims to make it easier to test data pipelines, and therefore increase data quality.

Superconductive, the force behind Great Expectations, has announced it has received $21 million in Series A funding led by Index Ventures with CRV and Root Ventures participating. We caught up with Gong to learn more about Great Expectations.

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Tuning databases is key to application performance and stability, but it's a hard job. Auto-tuning helps, but it was reserved for the Oracles and Microsofts of the world till now. OtterTune wants to democratize this capability

Databases are the substrate on which most applications run. Although different applications have different needs served by different databases, they all have one thing in common: they are complex systems that need continuous fine tuning to work optimally.

Databases come with a plethora of parameters that can be tuned by "turning knobs". Traditionally, this has been the job of Database Administrators (DBAs). Their job is a hard one, as they need to know the specifics of the database, the hardware it's running on, and the workloads it serves.

Some database vendors like IBM, Microsoft and Oracle have taken steps to automate this work. OtterTune is a startup that wants to democratize this capability. Today OtterTune is announcing the private beta of its new automatic database tuning service, as well as an initial $2.5 million seed funding round led by Accel.

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NeuReality targets deep learning inference workloads on the edge, aiming to reduce CAPEX and OPEX for infrastructure owners

The AI chip space is booming, with innovation coming from a slew of startups in addition to the usual suspects. You may never have heard of NeuReality before, but it seems likely you'll be hearing more about it after today.

NeuReality is a startup founded in Israel in 2019. Today it has announced NR1-P, which it dubs a novel AI-centric inference platform. That's a bold claim for a previously unknown, and a very short time to arrive there -- even if it is the first of more implementations to follow.

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