As an IT professional, I have been using LogMeIn Central for several months now to manage and monitor my organisation's endpoint infrastructure. Overall, I have found the software to be comprehensive in terms of its features, but average in terms of performance and advanced capabilities.
One of the standout features of LogMeIn Central is its wide range of capabilities for endpoint management. From remote control and asset management to patch management and software deployment, this software offers a range of tools that are useful for IT professionals. I particularly appreciated the security features, such as password protection and two-factor authentication, which help to protect against unauthorised access.
However, while LogMeIn Central is relatively easy to use and offers a user-friendly interface, I did experience some performance issues. The software can be slow at times, particularly when attempting to access remote devices, which can be frustrating. Additionally, while the software offers a good range of features, it lacks some advanced capabilities that are offered by other endpoint management solutions, such as advanced reporting and analytics.
In terms of pricing, LogMeIn Central is somewhat expensive compared to other endpoint management software on the market. While it may be worth the investment for larger organisations with more complex endpoint infrastructure, smaller businesses may find it too costly.
Based on our record, Google BigQuery seems to be more popular. It has been mentiond 35 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Using the Galaxy UI, knowledge workers can systematically review the best results from all configured services including Apache Solr, ChatGPT, Elastic, OpenSearch, PostgreSQL, Google BigQuery, plus generic HTTP/GET/POST with configurations for premium services like Google's Programmable Search Engine, Miro and Northern Light Research. - Source: dev.to / 8 months ago
Data Transformations: This phase involves modifying and integrating tables to generate new tables optimized for analytical use. Consider this example: you want to understand the purchasing behavior of customers aged between 20-30 in your online shop. This means you'll need to join product, customer, and transaction data to create a unified table for analytics. These data preparation tasks (e.g., joining... - Source: dev.to / 9 months ago
Introduction In today's data-driven world, transforming raw data into valuable insights is crucial. This process, however, often involves complex tasks that demand efficiency, scalability, and reliability. Enter dbt Cloud—a powerful tool that simplifies data transformations on Google BigQuery. In this article, we'll take you through a step-by-step guide on how to run BigQuery transformations using dbt Cloud.... - Source: dev.to / 9 months ago
You'll want to evaluate what BigQuery has to offer and see if it makes sense for you to move over. Source: 11 months ago
Watch the introductory videos on BigQuery on the Google Cloud Platform website (https://cloud.google.com/bigquery). Source: 11 months ago
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