Online Courses: Big Data Projects and Data Science Pipelines

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

Building Distributed Pipelines for Data Science using Kafka, Spark, and CassandraLearn how to introduce a distributed data science pipeline in your organization

• WHAT YOU’LL LEARN
• INSTRUCTOR
• SCHEDULE
• REGISTER
  

[size=1.5]

[size=1.4]September 19, 21 & 23, 2016

9:00AM – 11:00AM PDT
(5:00PM – 7:00PM BST)

[size=1.25]Register now

Sign up before this course sells out!

Tweet  


[size=1.5]

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 course is for you if…

• 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
  

Prerequisites:

• 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.)
  

About your instructors

Andy Petrella is a mathematician turned into a distributed computing entrepreneur, in addition to being a Scala and Spark trainer. Andy participated in many projects built using Spark, Cassandra, and other distributed technologies, in various fields including geospatial, IoT, automotive, and smart cities projects.

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.

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

关键词：Data Science Pipelines Projects pipeline Big data Online

谢谢分享