Data Engineering and Big Data Consulting

Data Engineer plays a key role in building a data-driven solutions by maintaining data pipelines and databases for ingesting, transforming, and storing data. Data Engineer supports data science by connecting data from one source to another and transforming data from one format to another. Data Engineer builds data infrastructures connecting data ecosystems to make a data scientist’s work possible.

The core hard-skills in day-to-day work:

  • Data platforms - relational databases, NoSQL, Hadoop, Spark, Kafka, RabbitMQ , Flink, etc…
  • ETL - tools to extract data, move data around and transform them for ingestion into data platforms (eg., NiFi).
  • Languages: Python, SQL, C/C++
  • Connectors - various ways to connect to systems in order to build pipelines (eg., HTTP, REST, ODBC, FTP)

Data Engineering and Big Data consulting covers the following areas:

  • Reviewing your current data architecture.
  • Extract data from various sources.
  • Mine various data formats and protocols.
  • Cleaning, curating, processing, and transforming data into usable formats for data science and machine learning model development.
  • Storing preprocessed data for further use.
  • Data modeling for easier analysis.
  • Exposing the stored data to analytical and AI applications .
  • Building data pipelines that gather, process, store data.
  • Consulting on selecting Big Data processing solutions and tools for your projects.
  • Designing Big Data processing architectures.
  • Managing your Big Data infrastructures.
  • Database configuration and tuning (MySQL, PostgreSQL, MongoDB, Redis, Riak, Casandra, InfluxDB, Neo4j, ask for more)
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Marcin Wylot, PhD
Data Scientist & Machine Learning Engineer

20 years of experience in data processing from A to Z