Flink Forward 2018: What You Want to Know and What You (Will) Need to Know.

Early September 2018, 8 EURA NOVA engineers travelled to Berlin to attend the Flink Forward Conference, dedicated to Apache Flink users and stream processing communities.

They came back with a lot to say about the hot topics in stream processing and the presented use cases! In this article, they will give you their opinion about data Artisans’ main announcement, the intakes of their favourite talks, and what they thought makes Flink Forward different from other conferences.

 

First keynote announcement:

During the keynote speech, data Artisans announced that they now bring ACID transactions directly on streaming data with data Artisans Streaming Ledger.

Charles Bonneau, our software architect, says: “This feature allows ACID transactions between multiple operators’ event-processing operations and internal states. This means that streaming applications can now update multiple states in one transaction. For example, an application that transfers money from one bank account to another can finally be implemented using Flink with strong consistency guarantees. Both bank accounts will have their balance updated at the same time as if there was a master data-management state”.

For Sabri Skhiri, our R&D director, this opens the doors to a brand new range of applications, especially in data-driven real-time services but also in streaming data management. He explains: “They are pushing forward the concept of streaming. Now, you could imagine a master data-management state that can be updated by operational streaming applications in real time. This will allow even more complex and advanced use cases of stream processing!”.

 

Favourite talks:

In 2 days, each Euranovian attended about 18 talks and use case presentations, with speakers from tech giants such as IBM, Netflix, Alibaba, and Uber as well as speakers from smaller companies.

Charles explains: “The conclusions are reassuring: most of them face the same issues that we see at our clients’ and our solutions are all valuable. They include a stream-first data architecture, a stream-first data pipeline product, and Flink developers skills. Even though a number of companies are at the very edge of the technology and their issues do not yet require continuous flows of a considerable amount of events, we are ready”.

For our R&D Director Sabri Skhiri, the keynote speech from Lightbend was one of the most interesting ones. He explains: “Viktor Klang, Lightbend deputy CTO, talked about the convergence between microservices and stream processing.  At EURA NOVA, we have been advocating for this convergence for more than a year in our architecture practice. The idea is simple: asynchronous microservices can be designed as stream processing stages. This is fantastic because it makes modern stateful stream processing frameworks the perfect target for implementing reactive microservices. With stateful deployment, exactly once semantics, high availability and ACID access to states, microservices can become stateful streaming apps.”

 

Vision-oriented Flink Conference:

Our colleagues came back with sparkles in their eyes. When we asked them how they felt about the event, Sabri Skhiri explained:

“Very often, this type of conferences tend to be business oriented. They are focused on how to make the framework easy to use and available to as many people as possible. By contrast, this year’s Flink Forward conference was all about innovation and vision. data Artisans shared their vision of what the Flink framework will be within 3 to 5 years and talked about what role stream processing and big data have within this vision.  In fact, almost all the talks were very technical. They were testimonies of big names in the industry, such as Alibaba, Netflix, and ING about problems encountered on the field and how they have been solved, which is often out of the box. The Flink-Alibaba partnership is a sharing one. Alibaba are way ahead with their technology. They keep their lead for 1 year and then they share their work and make their code open source. data Artisans have a great long-term vision of stream processing. I can see a lot of very interesting architecture discussions in the coming months!”

 

Stream Processing Technology:

When most frameworks cannot process considerable streams of live data and provide results in real time, Flink provides a single runtime for the streaming and batch processing while being highly scalable.

Cyrille Duverne, our Lead Data Architect, confirms: “Flink is definitely a real-time processor! We’re speaking about true real time, not only mini batches etc… Plus, the introduction of ACID transaction management in the new version of data Artisans’ Flink distribution creates a good marketing edge”.

Sabri Skirhi and our R&D engineer Florian Demesmaeker were at the Spark Summit this week. Stay tuned for part 2 with their feedback!

Installing TensorFlow with distributed GPU support.

Today, I wrote my first “Hello World” script using the freshly open-sourced version of TensorFlow with distributed GPU support. At the time of this writing, the binary releases of TensorFlow don’t come with the distributed GPU support therefore I had to build TensorFlow from sources. All the documentation to do this already exists but is a bit scattered on multiple websites. Here is a condensed version of the install process (on a Linux Ubuntu 14.04 platform).

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Graph Data Management: Status and Trends

Today’s social environments are getting more interconnected and the business market is becoming increasingly open and competitive. Organisations require a better awareness of their state and an accurate prediction of their evolution. To cope with this surging demand, new models and tools need to be developed. In my opinion, graph models are of a crucial interest for addressing these challenges.

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High Availability in RoQ

In the last year, we have worked with Benjamin Van Melle on implementing High Availability in RoQ, our proof-of-concept distributed pub-sub messaging system. As a consequence, we needed to expand our JUnit tests to cover individual component failure scenarios and prove they were handled as expected. This piece will show how we used Docker to achieve this.

Elastic Messaging for the Cloud
Elastic Messaging for the Cloud

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EURA NOVA Master Theses, 2013-2014 Season

Similar to the previous years EURA NOVA R&D has been supervising Master students either for their internship or for their Master Thesis during the 2013-1014 academic year. This year, 5 students have had the opportunity to work in the fields of Machine Learning, GPU compute, distributed processing, metabolic pathways and social graphs. This blog post summarizes their breakthroughs.

EURA NOVA Master Theses

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Projects Portfolio Management (PPM)

EURA NOVA - Projects Portfolio Management
(c) Can Stock Photo / Venimo

For meeting its strategic objectives, a company has to move from a situation A to a situation B. To achieve this move, it uses Projects as vehicles for performing Changes.

As you know, vehicles have specific purposes and therefore different sizes, styles, performance, costs, lifespan and sometimes also priorities.

As you also know, vehicles sometimes don’t arrive in destination B as initially foreseen: some may get out of fuel, others may use unexpected directions, or take more time than planned for arriving to destination. In worst cases, they may get damaged or even collide each others.

Let’s see how Projects Portfolio Management can select the right mix of vehicles, control them and increase success for vehicles to arrive all at the desired place, time & cost and without bad surprises.

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