Data quality

With Anatella, you can easily perform any data quality and data cleaning tasks.
In opposition to other tools, the Anatella data cleaning procedures are optimized to work on large datasets (several billion rows).

You can (non-limitative list):

Check the validity of character fields

For example, check for the right formats using powerful regular expressions. You can use the following Anatella operator to perform this task.
regex

Check the validity of numeric fields

For example: you can compute means, number of unique values, look for the highest & lowest number, count the number of missings, etc.

aggregate

Check for missing values

You can use the following Anatella operator to perform this task:

rowFilter

Check dates

For example: is it the right format, is it in range? You can use the following Anatella operators to perform this task:
birthdaycake rowFilter

Remove duplicates

Anatella contains a “box” to remove duplicates. You can use the following Anatella operator to perform this task:
removeDuplicate

Check consistency

Insure consistency between a set of keys between different datasources. You can use the following Anatella operators to perform this task:
merge filterMerge

Compare 2 datasets

Compare a selection of the character fields & numeric fields inside the 2 dataset using this operator:
validate

Design complex test

Design any complex test that you want using the powerful R, Python or Javascript engine included in Anatella.

javascript-logo Rlogo pythonlogo

Fix misspelled words

The Anatella spelling-correction operator will detect and correct misspelled words.
checkSpelling

Fuzzy Join

Join tables based on a fuzzy match between “approximately” equal Keys using these operators:

  • Key is Numeric:     IntervalJoin   or   mergeCDR
  • Key is a string:     checkSpelling   or   fuzzyJoin   or   speak

If you have a composite key (i.e. a key composed of several columns) with complex matching rules, you can easily edit the JS code of the fuzzyJoin FuzzyJoin action to define you own complex rules.

Perform Complex Text-Mining Tasks

Some examples:

  • Automatic language detection (using the mouth box ),
  • Phonetic Encoding using the Metaphone 3 algorithm (wit the speak box),
  • translate text from any language (using the translate googleTanslate box),
  • Validate postal addresses (using either the google geocoding API or the Geocode farm geocoding API, with the globe geocode and revergeocode box),
  • Use the rosette API (using the rosette box)

Create Text-Mining Predictive Models for Business-Defined Entity Extraction

Use a predictive model to pin-point the location of the entity that you want to extract. This is done is 4 steps:

  1. Identify a set of candidates for the entities that you want to extract. For example, if you want to extract a specific price in your document, design a box the identifies all the numbers in the document
  2. Extract the “context” around each candidate using the _text extractSurroundingLines box) and structure this context using the bagofword BagOfWord box) to obtain a learning&scoring dataset.
  3. Create a predictive model (with “TIMi Modeler”) that detects the required entities
  4. Use your predictive model to detect the “right” entities.