Apache Pig is recommended for data engineers and analysts who are working in Apache Hadoop environments and need to perform ETL (Extract, Transform, Load) operations on large datasets. It is also suitable for teams looking to leverage existing Hadoop infrastructures without delving into complex Java MapReduce programming or when migrating legacy processing scripts based on Pig Latin.
Helps my company solve many issues. We collect information from clients and employees. We conduct surveys and collect data. Very useful software. Helps form a common opinion, conduct analysis and analytics.
One of our customers said: Our small mining operation needed to go from paper based process to digital forms. At first, Google forms allowed us to use this Web-based platform that lets individuals and businesses of all sizes build customizable forms to conduct surveys and generate real-time response charts.
We saw that a small sample of our field workers quickly adopted the new way of working.
Step 1: accomplished.
Now unto step 2.
How do we deploy this unto our whole team? We needed email notifications, offline response collection when without wifi on the field. Our CIO and his director of operations needed deep data and trends analysis as well. Our inspectors, when doing their audits, needed to capture approx. 25 high definition pictures, some audio notes and a video which wasn't really possible with google forms.
So, we can 100% credit the use of google forms to our transition towards a paperless process, but as we navigated saashub.com a little more, we were able to discover a world of alternatives. We strongly suggest to start using google forms before undergoing a big implementation plan towards such enterprise level inspection tools like nspek or even cheaper solutions like prontoforms.
I am not sure if we would start with google's solution first if we would to do this digital transformation all over, but it did allow us to discover it's limits pretty quickly.
At some point, we needed custom fields and functions, and none of us was able to code, so the nSpek training that comes with the application definitely sets it's self apart, giving us full autonomy.
Based on our record, Apache Pig seems to be more popular. It has been mentiond 2 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.
Pig, a platform/programming language for authoring parallelizable jobs. - Source: dev.to / over 2 years ago
In the early days of the Big Data era when K8s hasn't even been born yet, the common open source go-to solution was the Hadoop stack. We have written several old-fashioned Map-Reduce jobs, scripts using Pig until we came across Spark. Since then Spark has became one of the most popular data processing engines. It is very easy to start using Lighter on YARN deployments. Just run a docker with proper configuration... - Source: dev.to / over 3 years ago
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