REPODS workflow & imports

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REPODS Customer Grafik

Features of our Data Pods

Inspired by Data Engineers and Data Scientists

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Import
Pipe
Model
Report
Workbook
Infograph
Monitor

Importing Data

Data comes in many shapes and sizes

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Everything starts with an import

In most cases the imported data has a relation to a certain time scope (e.g. October stock prices or 2008 economic growth rate, ...) That time scope is tracked so you always have a proper overview of what's there and what's missing. Incoming data will be analyzed with sophisticated statistical methods to derive it's structure and datatypes to minimize the effort of manual adjustments.

Load and transform data with Pipes

Connect data without gaps and duplications. In retrospect and in-between.

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mELT Data Load Architecture

If you have worked with data, you know that loading and transforming data is one of the most time consuming jobs when mining for information. A file is provided too late, corrections and new data are all contained in the same file or data content is overlapping. Our mELt processes ensures that imports of this kind of data will always correctly be merged with the existing stock. This is possible as in REPODS the target table has knowledge of the objects it contains. When a customer file is loaded twice, the mELT process will detect which customers are already present in the target table and will not make any change the second time. If a few corrections are contained in the second file only those few customers will be updated and put under proper version control by mELT.

Data Model

Model your context with data

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Interconnected Information

Relations between tables are an essential construct to describe information in larger contexts. The truly valuable insights are most often generated by combining many interconnected subjects into one analysis. Well designed relations between your table entities are a key ingredient for later being able to drill and slice to the data. There is no need to adhere to a certain modelling scheme like Star Schema or Snow Flake. Our Reporting engine can drill through an arbitrary ER-Model.

Create Reports

Discover new relationships and constellations

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OLAP reports directly on the data model

To create a report you navigate the Data Model along the defined relations and pick attributes for your report along the way. A suitable chart will be derived from the report result to give you a first visual impression. Specific fine tuned visualizations can be created in our infographics environment.

Create Workbooks

A Data Scientist's best friend

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Code, Results and Documentation

With a workbook style editor you can develop custom queries on your data model using the full power of the PostgreSQL 10 SELECT instruction. Inspect Query results in place and document your work with markdown alongside the code.

Create Infographics

Create visual presentations to the point

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Code your own interactive Infographics

Other than a simple report, infographics introduce viewers to a subject matter and explain facts using tailor-made visualizations, explanatory texts and interactive elements. With some code (d3.js), you can build up a visualization directly in the browser starting from existing templates. The data you prepared in your reports is made available as a convenient Javascript object.

Automate and Control

Monitor running processes and handle errors

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Automate your work

The Control Panel gives you a live view of current and past processes that modified data in your Data Pod. You can setup a Flow Manager that automatically resolves dependencies between processes and loads data packages fully automated from import all the way into a report. Here you can also control our custom versioning mechanism. You can keep data states fully live recoverable while avoiding version overkill at the same time.

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Get in contact with us

Need Support? Have a feature request?

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Record Evolution GmbH
Hanauer Landstraße 146
60314 Frankfurt am Main
Germany
Record Evolution GmbH
Hanauer Landstraße 146
60314 Frankfurt am Main
Germany

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This article was originally shared on our Medium channel