Composable Data Processing with Apache Spark - Databricks

Composable Data Processing with Apache Spark

As the usage of Apache Spark continues to ramp up within the industry, a major challenge has been scaling our development. Too often we find that developers are re-implementing a similar set of cross-cutting concerns, sprinkled with some variance of use-case specific business logic as a concrete Spark App. The consequences of this anti-pattern are significant. Cross Cutting logic is re-implemented again and again. Each isolated Spark App is responsible for its own resiliency, scalability, monitoring, and error handling. Attempting to weave together data as it flows across these Apps is highly inefficient. Pipelining data through one or more of these apps requires multiple rounds of loading and saving data to disk increasing the overall cost and risk of failure.

In addition, there is no consolidated error handling when chaining multiple Spark Apps. In this talk we will walk through the problems that led us to an extensible plugin framework, SIP, implemented to address these issues. SIP is used extensively in Adobe’s Experience Platform (AEP) for data processing. The framework enables us to support a number of complex use-cases by composing one or more simpler data conversion and/or validation operations. SIP is hosted internally, allowing a community of engineers to plugin code and benefit from the resiliency, scaling, and monitoring invested in existing infrastructure. Finally, we will dive deep into SIP’s detailed error reporting and how it enables us to provide a much improved user-experience to our customers.

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About Miao Wang

Adobe, Inc.

Miao is an Engineering Manager at Adobe, where he works with a great team on platform engineering with Spark and other open source technologies. He used to be an active Spark contributor before changing to his manager role. His interests span on high speed networks, data center infrastructure, data processing and machine learning. Prior to joining Adobe, he worked at A10 Networks, IBM and Alibaba with various engineering roles. Miao holds a Ph.D in Computer Science from University of Nebraska. Lincoln with a focus on Peer-to-Peer (P2P) Streaming.

About Shone Sadler

Adobe, Inc.

Shone is a glorified plumber (aka Principle Scientist) at Adobe Systems responsible for siphoning data into Adobe's Digital Marketing Cloud. Back in the day, he was a chief architect at Q-Link Systems, a leader in Business Process Management. It was in 2004 Shone when joined Adobe as an Architect of its Livecycle Document Platform helping lead Adobe's initial foray into the enterprise. Shone received his Masters in MIS from Depaul University in 2000 and subsequently a Masters in Programming Languages from Georgia Institute of Technology.