ArticDB

In this article, we will create a solution to use any type of data within a zipline pipeline. We will create an algorithm that queries the datalake, performs the appropriate transformations, and subsequently ingests the data into zipline within the so-called bundle.

In this article, we will explore the creation of an algorithm to manage financial data in our datalake, using ArcticDB as the backend and the End Of Day Historical (EODH) data provider.

In the previous article, we introduced NoSQL databases and their advantages, use cases, and relevant justifications.

This article explains the characteristics and qualities of a datalake, while also explaining an execution plan for its creation based on Python and ArcticDB.