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A data pipeline, at its base, is a series of data processing measures that are used to automate the transport and transformation of data between systems or data stores. Data pipelines can be used for a wide range of use cases in a business, including aggregating data on customers for recommendation purposes or customer relationship management, combining and transforming data from multiple sources, as well as collating/streaming real-time data from sensors or transactions.

For example, a company like Airbnb could have data pipelines that go back and forth between their application and their platform of choice to improve customer service. Netflix utilizes a recommendation data pipeline that automates the data science steps for generating movie and series recommendations. Also, depending on the rate at which it updates, a batch or streaming data pipeline can be used to generate and update the data used in an analytics dashboard for stakeholders.

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Machine Learning For Time-series Forecasting

Machine learning is taking the world by storm, performing many tasks with human-like accuracy. In the medical field, there are now smart assistants that can check your health over time. In finance, there are tools that can predict the return on your investment with a reasonable degree of accuracy. In online marketing, there are product recommenders that suggest specific products and brands based on your purchase history.

In each of these fields, a different type of data can be used to train machine learning models. Among them, time-series data is used for training machine learning algorithms where time is the crucial component.

Source de l’article sur DZONE

At QuestDB we’ve had a UDP version of the InfluxDB Line Protocol (ILP) reader in QuestDB for quite some time, but we’ve had customers ask for a TCP version of it, so we delivered!

Using it, and configuring it, are relatively simple so don’t expect this to be a long post but I’ll walk you through the basics of how to set it up and use it.

Source de l’article sur DZONE