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 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.
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
Cleaning and standardisation processes
Data intake processes (batch and streaming)
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