TimeLake.io
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TimeLake.io's answer
We use time tracking data and AI to continuously improve team productivity over time. By analyzing work patterns through data, companies can pinpoint areas that need attention and refine time management strategies. AI comes into play by automating mundane tasks, allowing more time for essential work and aiding in task prioritization.
TimeLake.io's answer
Software development companies and Design Agencies
TimeLake.io's answer
TimeLake.io's answer
SpectroTime stands out due to:
For more details, visit SpectroTime.
TimeLake’s real-time dashboard has improved our QA team's coordination testing activities. I love having live visibility into each engineer’s workload and progress. It’s like having an ever-updating status board. The automated timesheets also save hours by converting recorded activity into timesheet entries, which sync with payroll and invoicing systems.
SpectroTime is a top recommendation for Windows users seeking a sophisticated time tracking tool that supports enhanced team functionality and insightful analytics. Ideal for managing both remote and in-office teams, it combines a user-friendly interface with powerful features to boost productivity
It's as easy to use as any familiar app you already know, but with the added benefit of tracking your time and managing your tasks. You can start tracking your tasks right after it's installed - no complex setup is required.
Based on our record, TABLUM.IO seems to be more popular. It has been mentiond 3 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.
For the data loaded from the raw and unstructured data sources (RESTful APIs, files, feeds) I'd recommend checking tablum.io. The data first gets into the staging SQL DB where you can apply transformations in SQL (ClickHouse dialect with hundreds of ready-to-use functions for aggregations, transformations, string and array processing, etc). After that, you can set up a pipeline to external DWH or DB. Or just... Source: over 2 years ago
Fairly easy to parse it with Python (or any other scripting languages that support regular expressions). Besides, there're no-code parsers that can do that. E.g. tablum.io, it has a regexp parser that turns poorly formatted text into an SQL table. All you need it is to provide the regular expression (regexp) to split the text (per-line basis or the entire text) into chunks that become SQL rows and columns, after... Source: over 2 years ago
You're right. There should be some way to safely pass the authentication token without storing it in the query. BTW, to keep all data within the company network perimeter and no share any keys with the cloud service, one can install tablum.io as a self-hosted version on a Linux server (it runs in Docker). Source: over 2 years ago
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