The Google Maps Platform enables access to vast amounts of accurate, comprehensive, and up-to-date geodata relating to maps, places, and routes, but it can sometimes take many lines of code and programmer hours to display this data in well-styled UI elements. In this post we’ll explore both the capabilities and the advantages of the new Google Maps Web Components for web application development. What are Google Maps Web Components? The Google Maps Platform team recently launched Web Components as an innovative way for web developers to utilize the Google Maps Platform functionality in a simple ..read more

The challenge of getting sufficient cost visibility into your Kubernetes spend is a tale as old as Kubernetes itself.  Dynamic resource allocation and the short-lived nature of containers make it hard to attribute costs to specific applications or services. Added to this is the fact that resources are shared among various components. Despite this, achieving cost visibility in Kubernetes is crucial for effective resource and cost management.  To alleviate these challenges, Google Cloud released GKE cost allocation late last year, which helps customers view a detailed breakdown of thei ..read more

Google Maps Platform resources are provisioned and managed via the Google Maps Platform interface on the Google Cloud Console. There are multiple activities and functions that can be implemented via this interface, and their number and level of sophistication are increasing over time. This functionality often can have multiple consequences for any organization in terms of productivity, interactivity, and costs - and therefore there is often a need for an approach that will enable effective governance by providing fit-for-purpose access permissions to the various functions and activities. Examp ..read more

When planning new workloads, it’s important from the get-go to make sure you’re picking the right instance given your needs. Choose the wrong instance, and you’ll wind up with an underperforming application and/or overspending. However, up until now, the process of comparing instance types on Google Cloud has been complex and time-consuming, leading to suboptimal instance selection, overspending, and/or underperforming applications.  That’s why we’re excited to announce the Google Cloud Instance Comparison Tool, which simplifies the once-daunting task of comparing instances, making it inc ..read more

Generative artificial intelligence (AI) is changing both organizations’ and society's perception of the impact that AI technologies can have on daily life, as well as how businesses operate. Research from KPMG reveals that 65% of U.S. executives at companies with at least $1 billion in revenue anticipate generative AI to have a “high or extremely high impact” on their organization in the next three to five years. However, 60% feel they are at least a year (or two) away from deploying their first generative AI solution.  Today, DoiT International and Google Cloud are proud to announce the ..read more

An edited excerpt from a series on optimizing BigQuery costs and performance The first part of this series outlined what you need to have in place to run queries in BigQuery, and the second part covered the most common querying mistakes you are likely to encounter when optimizing your BigQuery usage. In this article, we explore how to identify the costliest queries in your environment. All of the code used in the series is referenced in this GitHub repository. Most expensive queries overall The first query you need to address is in the top_costly_queries.sql file. Arguably the most important q ..read more

Primer on BigQuery Cost and Performance Optimizations Overview Below is a list of all queries that are contained in the GitHub repository for this series of articles along with a short description of them. Note that there will be a query for using the INFORMATION_SCHEMA views and then two for using the BigQuery audit log sinks. In the latter there will be a general query, labeled by “_general” in the filename, that will search all projects in the sink and then a query that searches on a per-project basis, which will not have any extra suffixes in the filename. I will ..read more

Primer on BigQuery Cost and Performance Optimizations Previous Articles in this Series Part 1 Part 2 The “Doing” Introduction The past two sections have covered a lot of topics on prerequisite knowledge, general good-knowledge-to-have, and setting up some infrastructure for analyzing the data. GitHub Repository All of the code used throughout this series is located in the following GitHub repository: https://github.com/doitintl/bigquery-optimization-queries Since the two methods of monitoring generate vastly different schemas I have broken up the SQL files into two folders (audit_log and info ..read more

Primer on BigQuery Cost and Performance Optimizations Other parts in this series: Part 1 Part 3 Continuing On In the first part of this series I outlined some prerequisite knowledge and some operational items that need to be done in order to run queries and do the process of optimization. In this part of the series I am going to cover the most common querying mistakes that end up costing our customers more money or cause performance issues. Consider this a primer for referring to later on ways to fix what you find in the later parts of the series. If you are thirsting to do some “doing” on th ..read more

Keeping up with BigQuery cost optimization best practices is daunting – but what if you had a tool that simply told you what to do? Your BigQuery costs are going up — now what? Sure, you could dig through audit logs for clues, or read through Google Cloud’s BigQuery Best Practices to better understand what might be optimizable.  But faced with a backlog of other projects and fires to put out, can you or your team really afford to devote the time required? From our experience, most of the time the answer is a resounding “No.”  Most companies we work with have just too many higher-prio ..read more