PlusOne

Graal’s native image generation has been the focus of a lot of attention recently, despite being in the very early stages. This session will look at the Tomcat embedded packaging, how to handle your webapp, and will demo use of Graal and Tomcat in a container environment. The session will mostly be tutorial/demo oriented, after a brief overview of the concept and tools used.

The world’s data and the speed with which we expect to access it continues to grow rapidly. Apache Cassandra was built for hybrid cloud implementations and that makes it easy for companies to fully exploit their cloud strategies without having to re-architect their applications or change their systems for data management. Sounds too good to be true? A lot of big companies have been doing it for years and you can too! In this presentation, we will cover best practices and strategies for deploying your app to Cassandra on any cloud that you want. We will discuss: * Uses cases that require the power of Cassandra for hybrid cloud * Data model considerations for your active anywhere applicatio * Deployment strategies on any cloud. We do not care which cloud 🙂

Apache Cassandra is a database product that is most notably known for being highly scalable and fault tolerant. Since these characteristics are something that is desirable in almost every use case, it attracts attention from developers up through executives when faced with decisions on how to architect mission critical systems. If these traits are so desirable, and Apache Cassandra makes these capabilities available to end users, then shouldn’t we use Cassandra for all our data storage needs? What is the catch? When considering Apache Cassandra for one’s database needs, it is critical for users to understand when it is appropriate for use, and what are the trade-off’s one needs to consider. A person must consider the priorities of their non-functional requirements, the required investment in knowledge acquisition, the increased complexity of distributed systems, and the operational needs of maintaining such a database system among other concerns. If used incorrectly, the results could be disastrous, but when used appropriately Apache Cassandra can produce the expected rewarding results. In this presentation, [redacted name] will share his experiences operating one of the largest Apache Cassandra deployments in the financial services industry. As such, specific topics will be detailed that are relevant to both the largest and smallest of use cases.n* When is it appropriate to use Cassandra? What has been successful/unsuccessful?n* What have been the largest barriers to adoption?n* How to operate at any scalen* Risk and Control considerationsn* Knowledge acquisition and proliferation. Please join us for this candid discussion in hopes of easing the journey to successful adoption of Cassandra.

As a mature operational database with a 10+ year history as an Apache project, Cassandra has a long history with many twists and turns. In this talk, we’ll look at common misgivings and misunderstandings that we encounter in conversations with developers and get to the bottom of the story:

– Cassandra is a “columnar” (or “column-oriented”) database.
– Porting my relational data model to Cassandra will work well.
– Cassandra is a niche database that is only good for narrow set of use cases.
– My application can’t tolerate eventual consistency.
– Cassandra counters are unreliable.
– Never use materialized views or secondary indexes in production

At Instagram, our mission is to capture and share the world’s moments. Our app is used by over 1 billion people monthly; this creates a lot of challenging data needs. We use Cassandra behind a lot of different product features. In this presentation, I will talk about how we use Cassandra to serve our critical use cases; the improvements we made in last several years to make sure Cassandra can meet our low latency, high scalability requirements; and future plans.

In this talk, we will review the new and upcomming tools for Apache Cassandra operations. This talk will go over some of the new and soon to be tools for operational visibility. Specific areas covered will include:n- Different operational tasks will be exposed with an HTTP interface with the sidecarn- Virtual tables provides visibility to the state of a node with cqlshn- New commands added to nodetool and JMX See how these improvements and additions will give operation teams faster diagnostics to assist in discovering problematic clients and data models.

At almost two years in the making Apache Cassandra 4.0 is almost here. With a focus on performance and stability, it is full of interesting features. This talk takes you through a tour of the new features and performance improvements. From Zero Copy Faster Streaming support to Virtual Tables and Audit Logging, learn how Apache Cassandra 4.0 has departed from previous major releases to be focused on the features required by the most demanding users. Attendees will not only gain an understanding of new features in Apache Cassandra, but understand what drove their creation and therefore have a better idea of how to best use these features in their own systems.

Apache Beam is a unified data processing framework, allowing you to write batch and streaming pipelines that run anywhere, including Apache Flink, Apache Spark, and Google Cloud Dataflow. With the SQL extension you can now write a pipeline in pure SQL. If you need more, you can write user defined functions in Java or even embed SQL into your existing Java pipeline. This talk will start with a demo pipeline written in pure SQL. We will review how streaming SQL came from collaboration between the Apache Beam, Apache Calcite and Apache Flink communities. Finally, we will deep-dive into the architecture of Beam’s implementation and the work we are doing to make Apache Beam SQL the default choice for writing new streaming pipelines.

One of the key components in any data processing system is IO connectors. These fundamental blocks allow to read and write data, which is stored in different type of sources, in a unified and distributed way. In this sense, Apache Beam is not an exception – it provides a rich API to develop a new connector with your favorite SDK and easily integrate it with Beam runners. In this talk we are going to show you how to write your own IO connector (in Java). We will see what are the differences between bounded and unbounded sources, how to implement efficient sources and sinks and where we need to pay more attention during the development, what kind of API your connector should provide to users and how it can be tested. To achieve this we will rely on examples from existing Beam connectors. We will also give a brief overview of a rather recent feature/pattern in Beam IO – Composable IO connectors and the Splittable DoFn API. We will discuss the advantages of modular IO API design and some new IO design patterns allowed by this style. This talk will be interesting for people, who consider to write their own IO connectors or want to contribute to existing ones, as well as for Beam users, who wish to understand how existing Beam connectors work under the hood.

Apache Beam provides a unified programming model to execute batch and streaming pipelines on all the popular big data engines. The translation layer from Beam to the chosen big data engine is called a runner. The current runner for Apache Spark is based on the RDD/DStream framework. However, there is an ongoing work to move it to Spark next generation framework a.k.a structured streaming. This talk will present why structured streaming is a good fit for Apache Beam, why it is worth the effort, and will give some feedback on how Apache Beam has solved the challenge, what the tough points and the sweet points were.