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More and more applications are using Flink for low-latency data processing. Flink unifies batch and stream processing using one computation engine. However in reality, in order to really unify batch and stream processing, it requires a data system offers one unified data representation for both batch and streaming data. Nowadays, streaming data is typically stored in a log storage or messaging system, while batch data is stored in distributed filesystem and object stores. That means that data scientists still need write two different computing jobs to access same data stored in different data systems. Apache Pulsar is the next generation messaging and streaming data system. It was originally built at Yahoo, and has graduated from Apache Incubator and become a Top-Level-Project. Pulsar separates messaging serving and data storage into two layers. Such layered architecture provides high throughput and low-latency while ensuring high availability and scalability. Pulsar’s segment centric storage design along with layered architecture makes Pulsar a perfect unbounded streaming data system, which can well fit into Flink’s computation model. In this talk, Sijie Guo from Apache Pulsar PMC, will introduce Pulsar and its layered architecture and segment-centric storage, detailing how this architecture can well integrate with Flink to provide elastic unified batch and stream processing.

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The event, held on 27th April 2019, was part of the Global Azure Bootcamp and covered Microsoft's Cosmos DB, more specifically: - Introduction to Cosmos DB, its features, internals, resource models, and request units. - DEMO: Create an SQL API. Download sample .NET app. Simple queries. - Covered Change Feed and showcased various use case scenarios. - Detailed Global Distribution and Consistency Models implications. - DEMO: Mongo - Lift and shift. Run simple .NET code against a MongoDB (in docker container) and cosmos. - Introduction to Tinkerpop graphs - DEMO: Graphs API. Download sample .NET app. Simple queries. https://techspark.mt/global-azure-bootcamp-27th-april-2019/

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The highest message delivery guarantee that Apache Pulsar provides is 'exactly-once', producing at a single partition via Idempotent Producer. Users are guaranteed that every message produced to a single partition via an Idempotent Producer will be persisted exactly once, without data loss. However, there is no 'atomicity' when a producer attempts to produce messages to multiple partitions. From the consumer side, acknowledgment is a best-effort operation, which results in message redelivery, hence consumer will receive duplicate messages. Pulsar only guarantees 'at-least-once' consumption for consumers. It creates inconvenience and brings in complexity when you use Pulsar to build mission-critical services (such as billing services). We introduce Transaction support in Pulsar 2.8.0 release, to simplify the process of building reliable and fault resilient services using Apache Pulsar and Pulsar Functions. It provides the capability to achieve end-to-end exactly-once for streaming jobs in other stream processing engines. This presentation deep dives into the details of Pulsar transaction and how Pulsar transaction is applied to Pulsar Functions and other processing engines to achieve transactional event streaming. We will cover how Pulsar transaction works and how Pulsar Functions offers transaction support using Pulsar transaction.

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Both Apache Pulsar and Apache Flink share a similar view on how the data and the computation level of an application can be “streaming-first” with batch as a special case streaming. With Apache Pulsar’s Segmented-Stream storage and Apache Flink’s steps to unify batch and stream processing workloads under one framework, there are numerous ways of integrating the two technologies to provide elastic data processing at massive scale, and build a real streaming warehouse. In this talk, Sijie Guo from Apache Pulsar community will given an overview of Apache Pulsar and how it provides the unified data view to fully leverage Apache Flink unified computation runtime for elastic data processing. He will share the latest integrations between Apache Pulsar and Apache Flink, especially around effectively-once processing and schema integration.

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At Librato, a Solarwinds company, we run hundreds of Cassandra instances across multiple rings and use it as our primary data store. In the past year, we embarked on a process to upgrade our fleet of Cassandra Amazon EC2 instances from instance store to instances using Amazon EBS and attached elastic network interfaces (ENIs). We find running Cassandra on EBS gives us the flexibility to choose the best instances for the best performance of our workload while saving us significant costs on infrastructure. In this session, we discuss how Librato operates Cassandra on EBS. Topics include how we chose the right instance for our workload, use detached EBS volumes and ENI mobility to reduce MTTR, use mixed EBS storage types for the best cost/performance tradeoff, debug performance issues, and continuously monitor Cassandra to get the most from AWS. We also look at performance tradeoffs made in the implementation of storage engines of large data systems like Cassandra.

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The last few years have seen the emergence of Serverless as a paradigm for event streaming. Its very simple programming model has attracted developers in droves. At the same time, its ability to elastically scale has simplified operations significantly. Combined together with the ubiquity of their presence across all cloud providers, serverless today has become the leading choice to do event processing at scale for a lot of companies. In this talk, Sijie Guo from StreamNative will explore how the serverless paradigm is applied to event streaming in Apache Pulsar, a next-generation event streaming system. Pulsar provides native support for serverless functions where the events are processed as soon as they arrive in a streaming manner and that provides flexible deployment options (thread, process, container). He will describe how these serverless functions make data engineering easier and share the real world usage of Pulsar Functions.

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