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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
Let’s imagine that you are a Python developer or have a well-trained team specialized in Python, but the deadline you got to analyze some data in IRIS is tight. Of course, InterSystems offers many tools for all kinds of analyses and treatments. However, in the given scenario, it is better to get the job done using the good old Pandas and leave the IRIS for another time. For the abovementioned situation and many other cases, you might want to fetch tables from IRIS to manage data outside InterSystems’ products. However, you may also need to do things the other way around when you have an external table in any format, namely CSV, TXT, or Pickle, that you need to import and use the IRIS tools upon it. Regardless of whether you have to deal with an issue described above or not, Innovatium taught me that knowing more ways to solve a coding problem can always come in handy. The good news is that you do not need to go through the tedious process of creating a new table, transferring all the rows, and adjusting every type when bringing a table from IRIS. This article will show you how to quickly transform an IRIS table into a Pandas data frame and backward with just a few lines of code. You can check out the code on my GitHub, where you can find a Jupiter Notebook with every step for this tutorial.
If you need more information to solve this problem, check the SQL Alchemy Documentation and the sqlalchemy-iris GitHub Repository. Alternatively, you can message me or leave a comment, and we will try to uncover the secret together.