Data Engineering | Data Transformation & Analysis | Data Architecture

Data Engineering


Data Engineering extracts and transforms raw, noisy Big Data from many sources into a clean, rationalized smaller Answer Set suitable for analytic modeling, to provide business decisions.

Our Data Engineering service supports client data scientists, engineers and analysts with a high-quality, economical and rapid data extraction and data storage solution.

Our experts deploy an extensive array of data tools such as Hadoop and Spark, to deliver high-quality data sets ready for further analysis or visualisation:

  • Acquisition and Recording
  • Extraction, Cleaning and Annotation
  • Integration, Aggregation & Representation



  • Companies fully loaded with concurrent projects and no time for data extraction

  • Businesses without their own qualified data scientists

  • Firms whose in-house data scientists are all busy

  • Companies with budget issues who need an economical data engineering solution

WITH Data Engineering YOU GET:

A dedicated team of Data Scientists

including four PhDs and consultants with more than 100 aggregate years of business and technology experience

Faster turnaround for data analysis and data presentation

for quicker answers to your questions

Extraction and cleaning performed by data scientists

with best of breed tools. This translates into more available time for data scientists to analyse/model the Answer Sets

More time for your data scientists

to analyse, in turn means higher quality business decisions

Reduced cost of data engineering

with data scientists in the cost-effective location

Clean data from raw resources

for better business decisions

Extract and clean the raw data that your data scientists hate

Help me with my data


  • Ciklum creates a dedicated team of highly-qualified professional Data Scientists to set up the project in consultation with you

  • The Data Engineering team performs iterative data transformation, cleansing, repair, aggregation, and representation of data from any kind of source

  • Relevant data is extracted and stored in appropriate storage solution. Extracted data is available for consumption by tools further along the data pipeline. We define method of collecting, storing, extracting and organising data (Data Architecture)

  • Clean data set is provided to your own data scientists and business users for use in business analysis