In the most recent season of BigQuery Spotlight, we discussed key concepts like the BigQuery Resource hierarchy, query processing, and the reservation model. This blog focuses on extending those concepts to operationalize workload management for various scenarios.
So far in this series, we’ve been focused on generic concepts and console-based workflows. However, when you’re working with huge amounts of data or surfacing information to lots of different stakeholders, leveraging BigQuery programmatically becomes essential. In today’s post, we’re going to take a tour of BigQuery’s API landscape - so you can better understand what each API does and what types of workflows you can automate with it.
If the COVID-19 pandemic has taught us anything, it is that speed and intelligence are of the essence when it comes to making business decisions. Organizations must find ways of keeping ahead of competitors and disruptions by continually leveraging data to make smart decisions. The problem? Data may be everywhere, but it’s not always available in a form that businesses can use to generate analytics in real time.
As enterprises seek to accelerate the process of getting insights from their data, they face numerous sources of friction. Data sprawl across silos, diverse formats, the explosion of data volumes, and the fact that data is spread across many data centers and clouds and processed by many disparate tools, all act to slow the progress.
In digital transformation projects, it’s easy to imagine the benefits of cloud, hybrid, artificial intelligence (AI), and machine learning (ML) models. The hard part is to turn aspiration into reality by creating an organization that is truly data-driven.
The manufacturing industry, like any other industry, is not immune to data challenges. Sourcing data, wrangling it and ensuring it’s being used in a governed, standardized way are not uncommon problems. Particularly in manufacturing, issues surface with inventory management, within the supply chain and with logistics.
