Building Distributed Pipelines for Data Science Using Kafka, Spark, and Cassandra
Learn how to introduce a distributed data science pipeline in your organization
This event has ended.
Building a distributed pipeline is a huge—and complex—undertaking. If you want to ensure yours is scalable, has fast in-memory processing, can handle real-time or streaming data feeds with high throughput and low-latency, is well suited for ad-hoc queries, can be spread across multiple data centers, is built to allocate resources efficiently, and is designed to allow for future changes, join Andy Petrella and Xavier Tordoir for this immensely practical hands-on course.
What you’ll learn and how you can apply it
By the end of this course, you'll have a solid understanding of:
- The most important technologies for a distributed pipeline, when they should be used—and how
- How to integrate scalable technologies into your company’s existing data architecture
- How to build a successful, scalable, elastic, distributed pipeline using a lean approach
This live event is for you because…
You’re a data scientist with experience with data modeling, business intelligence, or a traditional data pipeline and need to deal with bigger or faster data
You’re a software or data engineer with experience in architecting solutions in Scala, Java, or Python and you need to integrate scalable technologies in your company’s architecture
Intermediate knowledge of an object-oriented language and basic knowledge of a functional programming language, as well as basic experience with a JVM
Understanding of classic web architecture and service-oriented architecture
Basic understanding of ETL, streaming data, and distributed data architectures
Intermediate understanding of Docker and UNIX, as well as some basic knowledge about networks (IP, DNS, SSH, etc.)
For the online training class, we'll be using as the simplest environment to run most of the pipeline.This environment will be available from a single docker image. Please click the link below and follow the setup instructions.
The timeframes are only estimates and may vary according to how the class is progressing.
- Introduction, Spark, Spark Notebook, and Kafka
- Assignment #1
- Streaming: Spark, Kafka, and Cassandra
- Data analysis and external libraries
- Assignment #2
- Microservices, cluster management, job orchestration, and live demo of end-to-end distributed pipeline
- Final discussion & wrap up
Andy is an entrepreneur with a Mathematics and Distributed Data background focused on unleashing unexploited business potentials leveraging new technologies in machine learning, artificial intelligence, and cognitive systems.
In the data community, Andy is known as an early evangelist of Apache Spark (2011-), the Spark Notebook creator (2013-), a public speaker at various events (Spark Summit, Strata, Big Data Spain), and an O'Reilly author (Distributed Data Science, Data Lineage Essentials, Data Governance, and Machine Learning Model Monitoring).
Andy is the CEO of Kensu, bringing the Data Intelligence Management (DIM) Platform for data-driven companies to leverage AI sustainably, combining AI Observability with Data Usage Catalog.
Xavier Tordoir started his career as a researcher in experimental physics, focused on data processing. He took part in projects in finance, genomics, and software development for academic research, working on time series, prediction of biological molecular structures and interactions, and applied machine learning methodologies. He developed solutions to manage and process data distributed across data centers.
Xavier founded and works at Data Fellas, a company dedicated to distributed computing and advanced analytics, leveraging Scala, Spark, and other distributed technologies.