Data comes in all shapes and sizes – both structured and unstructured forms that include systems of engagement, systems of record, and social media. Hidden in this data are the answers to many issues that arise within business units. Businesses around the world are harnessing the endless potential of data they have collected over the years. But trying to make sense of large volumes of data the traditional way is least effective and time-consuming. In the past, companies had to deploy a costly enterprise data warehouse, along with data marts, to handle their vast inflows of data, as well as to store, process, and analyze that data. Along with high upfront costs, in-house data warehousing requires distributed, scalable, and reliable IT infrastructure and expertise.

Data engineering service has trumped over traditional methods with its capability to create and use processing systems to separate and organize a massive inflow of information. With such readily available data categorized based on trends and patterns, organizations can quickly identify profit-draining areas and uncover underlying problems within systems. It gives a simple and clear overview of information so that businesses can reduce the occurrence of flaws or defects in their decision-making.

Liberin’s Data Engineering Services transform organizational knowledge into insights for more informed and timely business decisions with the best possible TCO. We help companies replace their costly, burdensome in-house data infrastructure and turn their big data pipelines into robust systems prepared for business analytics. We work to ensure your data is in the right place at the right time and in the right format. With the growing audience for analytics, both within a given business and across industries, data engineering will become the linchpin that enables the extraction of value from data.

Liberin’s Data Engineering Services include developing complete end-to-end Data Pipelines, ingesting data from various sources into desired destinations, managing various file format conversions, performing Data Transformations and Data Cleansing, maintaining Data Integrity, developing Data Models, performing ETL and/or ELT jobs, Data Analytics, enriching Data for downstream Analytical Purposes and Performance Tuning.