In part one, we’ll talk about how they built the engagement activity Delta Lake to support Einstein Analytics for creating powerful reports and dashboards and Sales Cloud Einstein for training machine learning models.
Abstract:
At Salesforce, their customers are using High Velocity Sales ([ Ссылка ]) to intelligently convert leads and create new opportunities. To support it, we built the engagement activity platform to automatically capture and store user engagement activities using delta lake, which is one of the key components supporting Einstein Analytics ([ Ссылка ]) for creating powerful reports and dashboards and Sales Cloud Einstein ([ Ссылка ]) for training machine learning models.
We will include: 1. Ingest the data. 2. Incremental Read. 3. Support exact once write across tables. 4. Handle mutation with cascading changes. 5. Normalize tables in data lake.
-----------------
Speakers
-----------------
Zhidong Ke, Software Engineer PMTS, Salesforce
Zhidong is passionate in designing distributed systems, real-time/batch data processing and building applications.
Yifeng Liu, Software Engineer LMTS, Salesforce
Yifeng is a software engineer who has extensive experience in big data processing and distributed system, and interested in high volume, high complexity, low latency data pipeline and framework building.
Aaron Zhang
Title: Software Engineering PMTS, Salesforce
Aaron is an experienced software engineering leader with interests and areas of focus in engineering secure, fault-tolerant, high volume systems built on micro services.
Heng Zhang, Software Engineering PMTS, Salesforce
Heng is a software engineer who is interested and specialized in micro services, distributed systems and big data. Databricks is proud to announce that Gartner has named us a Leader in both the 2021 Magic Quadrant for Cloud Database Management Systems and the 2021 Magic Quadrant for Data Science and Machine Learning Platforms. Download the reports here. [ Ссылка ]
