The amount of data being collected — and then being made available for data analytics — is growing rapidly. So too is the need to employ effective pipelines that can take all that collected data and transform it into meaningful insights. Building these pipelines in the cloud comes with significant advantages. In addition to virtually unlimited scalability, the cloud offers managed services that eliminate the hassles and risks of managing your own infrastructure and hardware.
A typical data analytics pipeline starts with a data lake — a centralized repository for storing all your structured and unstructured data at scale in its raw form. The pipeline processes the data, cleaning, enriching, and transforming it to make it useful for downstream applications. A data warehouse stores the processed data in a relational format accessible via SQL. Analytics, business intelligence, and data science teams access the data in the data warehouse to create dashboards, machine learning (ML) models for predictions, and more.
This chapter covers the different steps involved in a typical data analytics pipeline, and options available for running them on Google Cloud in a scalable way.
Every application generates data, but what does that data mean? This is a question ...
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