Data Engineering


After designing the architecture that will support the Big Data ecosystem, the next step is to intake the data lake with data as the sole repository for all the data related to your company and its environment, regardless of category, type or volume.

Big Data enables the swift incorporation and dynamic processing of new data sources without needing to develop the architecture.

Data Veracity, correct decisions

Data engineering establishes the standards needed by any company to present its data in a unified, clean and accessible manner, responding to the requirements of each business.

This is crucially important, because it´s the phase when the data is prepared so that models can be applied to precise data during the subsequent stage – advanced analytics – and provide reliable business conclusions. If the data is unreliable, business decisions will not be correct.

Data Engineering Services for Information Processing

At Synergic Partners, we offer all the services involved in data engineering; from data modelling to the migration and automation of data intakes through scheduled workflows.

Data modelling and organisation

  • · Data distribution model and replication for data security
  • · Organisation of the data for an agile access

Data quality

  • · Variable profiling and enrichment
  • · Definition of the data quality processes throughout the data life cycle of the same

Cleaning and standardisation processes

  • · Generation of variables, attributes and indicators in the data lake directly
  • · Definition of the necessary data transformations following extraction from their original sources (internal and external)

Data intake processes (batch and streaming)

  • · Integration and treatment of structured, semi-structured and non-structured data
  • · Definition of the data intake strategy and roadmap, with appropriate latency times (in batch or real time), based on the type of data available to each company

Process automation

  • · Design of automatic data cleaning and standardisation processes
  • · Framework for the automation of data integration, whether on-premises or in the cloud for an immediate availability
  • · Automation of other processes related to data security, migrations, etc.

To do all this, we support ourselves on cutting-edge technologies:

– Common Hadoop distribution tools: HDP and CDH, among others

–  Implementation of the latest technology tools for data intakes: Spark, Sqoop, Hive, Oozie, Flume, Kafka and Flink, among others

– Other distributed databases, such as MongoDB, Cassandra and HBase

– Cloud-based data engineering tools: Amazon Web Services. Microsoft Azure and GCP