Overview
Data Engineering
We help companies solve their data challenges, predict demand patterns improving end-user satisfaction and guide through business strategies based on intelligent insights. Our team can analyze structured, semi-structured and unstructured data with the right technology, processing tools and approach. We provide services for complete data lifecycle management, including data acquisition, storage, modelling, and consultation, ETL processing, building pipelines, migration, integration, visualization, and analytics.
Overview
Data Engineering
We help companies solve their data challenges, predict demand patterns improving end-user satisfaction and guide through business strategies based on intelligent insights. Our team can analyze structured, semi-structured and unstructured data with the right technology, processing tools and approach.
We provide services for complete data lifecycle management, including data acquisition, storage, modelling, and consultation, ETL processing, building pipelines, migration, integration, visualization, and analytics.
Approach
Our Approach towards Data Engineering
Loading Data
This process involves designing a way to monitor incoming data (file, batch, streaming, etc.). Data sources may include relational databases and data from SaaS applications, or a NoSQL based data source. Most pipelines ingest raw data from multiple sources, an API, a replication engine that pulls batch data at regular intervals that is a part of lambda architecture.
Transforming Data
Connecting to and transforming data from each source to match the format and schema of its destination. This process refers to operations that change data, which may include data standardization, sorting, deduplication, validation, and verification.
Transferring Data
Moving the data to the target database/data warehouse. A target destination may be a data store — such as an on-premises or cloud-based data warehouse, a data lake, or an analytics application.
Integrating Data
Adding and deleting fields and altering the schema as company requirements change. There are two data ingestion models: batch processing, in which source data is collected periodically and sent to the destination system, and stream processing, in which data is sourced, manipulated, and loaded as soon as it is created.
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