Learn about the capabilities of Cloud Dataflow for exploratory data analytics.
- [Instructor] Cloud Dataflow is essentially…a data processing and transformation tool,…but it can also be used for exploratory data analytics…at a cloud scale.…You can use it to cleanse, transform, and aggregate data.…You can also use it to extract meaningful information.…It supports both batch and realtime data processing.…It is built on the Apache Beam programming model,…hence the code built on data flow is easily portable…to other platforms like Apache Spark and Flink.…
It can be used to build extract, transform, load pipelines.…You can build in the pipeline data exploration capabilities.…It has excellent integrations with other GCP products.…Finally, it supports multiple runners,…including Apache Spark and Flink.…This allows for experimentation and choice…for the developer.…What are the strengths of Cloud Dataflow?…It is built on Apache Beam.…
This provides for abstraction and portability.…You have other runner alternatives to Cloud Dataflow,…like Apache Spark.…You can perform complex processing and analysis of data…
- Setting up Cloud DataLlb for exploratory data analytics
- Segmentation and profiling
- Reading and writing data from BigQuery
- Managing cloud storage buckets
- Creating visualizations of BigQuery data with the GCP Charting API
- Managing Datalab instances
Skill Level Intermediate
Predictive Customer Analyticswith Kumaran Ponnambalam1h 37m Intermediate
1. Exploration Options in GCP
2. Cloud Datalab Basics
3. Datalab: BigQuery
4. Datalab: Cloud Storage
5. Datalab: Visualizations
6. EDA with GCP: Use Case
7. Managing Datalab
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