Amazon Kinesis Streams processes multiple terabytes of log data each day, yet events show up in our analytics in seconds.
Reading List Key Takeaways There are many decisions and tradeoffs that must be made when moving from batch ETL to stream data processing. Engineers should not "stream all the things" just because stream processing technology is popular The Netflix case study presented here migrated to Apache Flink.
Aroraa senior data engineer at Netflix, began by stating that the key goal of the presentation was to help the audience decide if a stream-processing data pipeline would help resolve problems they may be experiencing with a traditional extract-transform-load ETL batch processing job.
In addition to this, she discussed core decisions and tradeoffs that must be made when moving from batch to streaming. The Netflix system uses the microservice architectural style and services communicate via remote procedure call RPC and messaging.
At a high level, microservice application instances emit user and system-driven data events that are collected within the Netflix Keystone data pipeline — a petabyte-scale real-time event streaming-processing system for business and product analytics.
Batch-processed data is stored within tables Netflix case solution indexers like Elasticsearch for consumption by the research team, downstream systems, or dashboard applications. There are clear business wins for using stream processing, including the opportunity to train machine-learning algorithms with the latest data, provide innovation in the marketing of new launches, and create opportunities for new kinds of machine-learning algorithms.
There are also technical wins, such as the ability to save on storage costs as raw data does not need to be stored in its original formfaster turnaround time on error correction long-running batch jobs can incur significant delays when they failreal-time auditing on key personalization metrics, and integration with other real-time systems.
A core challenge when implementing stream processing is picking an appropriate engine. The first key question to ask is will the data be processed as an event-based stream or in micro-batches. If results are simply required sooner than currently provided, and the organization has already invested heavily in batch, then migrating to micro-batching could be the most appropriate and cost-effective solution.
The next challenge in picking a stream-processing engine is to ask what features will be most important in order to solve the problem being tackled.
This will most likely not be an issue that is solved in an initial brainstorming session — often a deep understanding of the problem and data only emerge after an in-depth investigation. Each engine supports this feature to varying degrees with varying mechanisms.
Another question to ask is whether the implementation requires the lambda architecture. This architecture is not to be confused with AWS Lambda or serverless technology in general — in the data-processing domain, the lambda architecture is designed to handle massive quantities of data by taking advantage of both batch-processing and stream-processing methods.
It may be the case that an existing batch job simply needs to be augmented with a speed layer, and if this is the case then choosing a data-processing engine that supports both layers of the lambda architecture may facilitate code reuse.
Several additional questions to ask when choosing a stream-processing engine include: What are other teams using within your organization? If there is a significant investment in a specific technology, then existing implementation and operational knowledge can often be leveraged.
What is the landscape of the existing ETL systems within your organization? Will a new technology easily fit in with existing sources and sinks?Detectives and crime lab technicians use the latest and most fascinating procedures to solve crimes in this documentary-style show.
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