没有合适的资源?快使用搜索试试~ 我知道了~
资源推荐
资源详情
资源评论
Future-proof, portable batch
and streaming pipelines
using Apache Beam
Malo Deniélou
Senior Software Engineer at Google
malo@google.com
Big Data Apps Meetup, May 2017
Apache Beam: Open Source data processing APIs
● Expresses data-parallel batch and streaming
algorithms using one unified API
● Cleanly separates data processing logic
from runtime requirements
● Supports execution on multiple distributed
processing runtime environments
Why use Apache Beam?
1. Unified: The full spectrum from batch to streaming
2. Extensible: Transform libraries, IO Connectors, and DSLs -- oh my!
3. Portable: Write once, run anywhere
4. Demo: Beam, Dataflow, Spark, Kafka, Flink, ...
5. Getting Started: Beaming into the Future
A free-to-play gaming analytics use case
● How many players made it to stage 12?
● What path did they take through the stage?
● Team points and other stats at this point in time?
● Of the players who took the same route where a certain
condition was true, how many made an in-app purchase?
● What are the characteristics of the player segment who
didn’t make the purchase vs. those who did?
● Why was this custom event so successful in driving in-app
purchases compared to others?
You need Key indicators specific to your game in order to increase adoption, engagement, etc.
The Solution
Collect real-time
game events and
player data
Combine data in
meaningful ways
Apply realtime
and historical
batch analysis
=
Impact engagement,
retention, and spend.
剩余58页未读,继续阅读
资源评论
过往记忆
- 粉丝: 4318
- 资源: 278
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功