Resources

451 Research Brief

The complexity of establishing and maintaining stream processing architectures is widely acknowledged. As the costs of real-time data have become less prohibitive, skillsets are increasingly the bottleneck to leveraging the technology. Decodable is seeking to address this bottleneck directly by letting teams establish capabilities that can filter, route, enrich or transform data streams using SQL, and easily build streaming applications.

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Achieve Results Faster with Apache Flink SQL

Flink SQL provides the ability to process real-time data using Structured Query Language, the de facto standard language used to access and process data, and one that is familiar to a very wide range of developers. It provides an optimized implementation that is difficult to achieve with Java, resulting in the clear advantage of being able get to market faster with efficient real-time applications when using Flink SQL.

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Whitepaper

Introduction To Flink And Stream Processing

In this whitepaper, we'll explore what Flink and stream processing are, what differentiates it from traditional batch processing, how it's used to drive modern applications and services, why it matters in the context of today's business needs, and touch on how it works.

Video & Podcasts

Benefits Of Real Time Stream Processing

Join us as we explore the features and benefits of leveraging Decodable for your real-time stream processing needs. As a fully managed stream processing service, Decodable provides pre-built connectors to external systems and leverages SQL to provide a familiar development experience so you can be up and running in minutes, not months.

Video & Podcasts

Decodable in 90 Seconds

Decodable in 90 Seconds

eBook

Build The Future With Decodable And Stream Processing

Learn why every business should consider switching to stream processing, and how it's easier and cheaper than ever before with the Decodable platform.

Video & Podcasts

Introduction to Apache Flink and Flink SQL

Join Gunnar Morling for a ten minute introduction to Flink and FlinkSQL, as you see him build a Flink pipeline to process data from one Kafka topic to another Kafka topic. In this example, he'll be using RedPanda's Kafka API compatible offering to stream data into and from Flink.

Use Cases

Use Case Walkthroughs

In this eBook, we'll walk through five common use cases of real-time stream processing, including Customer 360, food delivery tracking, fraud detection, shipping logistics tracking, and website clickstream analytics. Starting with a sample data schema for each scenario, we'll take a look at how quickly and easily SQL statements can be used to create a series of one or more pipelines to clean, transform, enrich, and aggregate raw incoming data into its desired form.

Video & Podcasts

Webinar - The Top 5 Mistakes Deploying Flink

Learn about the 5 most common mistakes deploying Apache Flink, and how you can avoid them from Flink co-creator and PMC member Robert Metzger. Robert will be joined by Decodable CEO and streaming industry veteran Eric Sammer who'll demo some of the most common stream processing patterns using SQL in a form that you can reproduce yourself in minutes.

News

TechCrunch: Decodable wants to take real-time stream processing mainstream

TechCrunch coverage of the announcement of Decodable's enterprise capabilities and enhancements.

Datasheet

Achieve Results Faster with Apache Flink SQL

Flink SQL provides the ability to process real-time data using Structured Query Language, the de facto standard language used to access and process data, and one that is familiar to a very wide range of developers. It provides an optimized implementation that is difficult to achieve with Java, resulting in the clear advantage of being able get to market faster with efficient real-time applications when using Flink SQL.

Video & Podcasts

TFIR: Decodable's 2023 Predictions

Eric Sammer talks to TFIR with his predictions for 2023 and Data.

Video & Podcasts

Deploying Flink With The New Kubernetes Operator

Apache Flink PMC chair, Robert Metzger, in conversation with Gyula Fóra and Mátyás Örhidi, the main contributors of the new Flink Kubernetes Operator launched earlier this year. The Flink Kubernetes Operator is an abstraction layer on top of Kubernetes that makes deployment and operation much easier than apply Flink directly to Kubernetes components. In this discussion we cover the motivation for creating a kubernetes operator as part of the Flink project and dig into the details of using it.

Video & Podcasts

Change Stream Processing With Apache Flink

In this demo-heavy webinar Gunnar Morling and Sharon Xie answer common questions on change stream processing: - What is Change Data Capture (CDC) and why should I care? - What do I gain when I integrate CDC with Apache Flink? After this introduction, they switch to code to show how it works in a demo that includes CDC sources, stream processing and delivery to an Elastic database for searching.

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