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Planetly: Scaling companies' carbon management with data

Planetly uses technology to simplify carbon management for companies at scale. Their data-driven software solution helps companies reach net-zero emission targets in four steps: The entire carbon management life cycle is powered and fueled by data. We talked to Cari Davidson, VP of Engineering and Patricia Montag, the Engineering Lead Analytics, to better understand what role Keboola (and data as a whole) play in the company’s operations and what that means for the engineering team.

Build data apps with Streamlit + ThoughtSpot APIs

I’ve been following the Streamlit framework for a while, since Snowflake announced that they would acquire it to enable data engineers to quick spin up data apps. I decided to play around with it and see how we could leverage the speed of creating an app along with the benefits that ThoughtSpot provides, especially around the ability to use NLP for search terms. Streamlit is built in Python.

How to Fix TypeError in Python: NoneType Object Is Not Iterable

The Python TypeError: NoneType Object Is Not Iterable is an exception that occurs when trying to iterate over a None value. Since in Python, only objects with a value can be iterated over, iterating over a None object raises the TypeError: NoneType Object Is Not Iterable exception.

Build limitless workloads on BigQuery: New features beyond SQL

Our mission at Google Cloud is to help our customers fuel data driven transformations. As a step towards this, BigQuery is removing its limit as a SQL-only interface and providing new developer extensions for workloads that require programming beyond SQL. These flexible programming extensions are all offered without the limitations of running virtual servers.

Unlocking the value of unstructured data at scale using BigQuery ML and object tables

Most commonly, data teams have worked with structured data. Unstructured data, which includes images, documents, and videos, will account for up to 80 percent of data by 2025. However, organizations currently use only a small percentage of this data to derive useful insights. One of main ways to extract value from unstructured data is by applying ML to the data.

Cybersecurity: A Big Data Problem

Information technology has been at the heart of governments around the world, enabling them to deliver vital citizen services, such as healthcare, transportation, employment, and national security. All of these functions rest on technology and share a valuable commodity: data. Data is produced and consumed in ever-increasing amounts and therefore must be protected. After all, we believe everything that we see on our computer screens to be true, don’t we?