Data Cleansing

Alooma has built-in data cleansing features to supercharge your data ingestion efforts. Whether enriching, deduplicating, removing errors, or integrating data from multiple sources, Alooma has what you need.

Data cleansing, without a catch

Alooma removes the guesswork, helping you automate and simplify your data cleansing.
Cleanse your mismatches

Cleanse your mismatches

Our Code Engine makes sure all your data arrives uniform and ready to query in your data warehouse by cleansing mismatches on the stream.
Automatically parse different formats

Automatically parse different formats

While managing your schema, our Mapper handles different date and timestamp formats, to make the focus on your data even sharper.
Enrich your data on the fly

Enrich your data on the fly

Augment your insights with added information like geolocation or user-defined fields as you stream events, using Alooma’s Code Engine.
Integrate your data into a single source of truth

Integrate your data into a single source of truth

Gain insights and visibility you never thought possible. Mapper lets you revise and update schemas on the fly.
All of your data is safe and accounted for

All of your data is safe and accounted for

When processing large volumes of data rapidly, some errors are to be expected. All errors and data-type mismatches will be caught and restreamed by the Restream Queue for exactly-once processing.
View your data in real time

View your data in real time

Alooma Live enables data scientists and engineers to monitor data streams in transit, and allows enterprises to monitor behavior and identify discrepancies. This lets you correct data integrity problems before they can impact your data warehouse and business intelligence.

Data cleansing FAQ

What exactly is data cleansing?

Data cleansing refers to the detection and removal of errors from collected data. As part of the ETL process, whenever large amounts of data from multiple sources require integration, there is a likelihood that some data will be redundant, erroneous, missing, or invalid. Also, data types and formats may need adjustment to match those in the destination data warehouse schema. Learn more about data cleansing.

What are the benefits of cleansing your data?

Data-driven decisions are crucial to modern business; however, if your data sources contain errors or if there are inconsistencies between them, your decision making will likely be impacted. Data cleansing is necessary when integrating heterogeneous data sources due to the benefits it provides: you can ensure that data arrives in your data warehouse uniform and ready to query, and give yourself the power to correct data integrity problems before they impact decision making.

What are some data cleansing examples?

Despite your best efforts, data from each of your sources may contain misspellings, mis-fielded information, duplicate records or even contradictory or incorrect information.

Some examples of data cleansing include:

  • Data record deduplication;
  • Data matching (Do records in a common dataset share a common format?);
  • Removing garbage or nonsense values;
  • Removing null values;
  • Removing errors;
  • Enrichment of data with supplementary data, e.g. geodata or timestamps;
  • Confirming data like addresses, urls, etc;
  • Fixing records according to a predefined ruleset.

Data cleansing should not occur in isolation, but in conjunction with other data transformations.

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