NICHE CONSULTING SERVICES
DATA ENGINEERING SERVICE
Data engineering is the practice of designing and building systems for data collection, storage, and analysis. This is an extensive field of application in almost all industries. Data engineers provide access to data and run analysis of raw data to provide predictive models and show short-and long-term trends. Without data engineering, it would be difficult to make sense of the huge amounts of data available to companies.
Once the environment is developed, we can help businesses manage the environment and make sure your environment is up-to-date, jobs are running fine. Experts in reducing the errors, auto-healing and overall manage the environment.
Being the experts in data engineering we design and build systems for collecting, storing, and analyzing data at scale. We have the ability to collect massive amounts of data and make it highly usable state by the time it reaches data scientists and analysts. We make data more useful and accessible for consumers of data. To do so, engineering must source, transform and analyze data from each system. Big data is changing the way we do business and creating a need for data engineers who can collect and manage large quantities of data.
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Acquire datasets that align with business needs
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Develop algorithms to transform data into useful, actionable information
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Build, test, and maintain database pipeline architectures
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Collaborate with management to understand company objectives
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Create new data validation methods and data analysis tools
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Ensure compliance with data governance and security policies
OUR SERVICE
Why Is Data Engineering Important?
Companies of all sizes have huge amounts of disparate data to comb through to answer critical business questions. Data engineering is designed to support the process, making it possible for consumers of data, such as analysts, data scientists and executives, to reliably, quickly and securely inspect all of the data available.
Data analysis is challenging because the data is managed by different technologies and stored in various structures. Yet, the tools used for analysis assume the data is managed by the same technology and stored in the same structure. This rift can cause headaches for anybody trying to answer questions about business performance.
QUALITY POLICY
Exceed Client Expectations in all aspects of service delivery.
For example, consider all of the data a brand collects about its customers:
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One system contains information about billing and shipping
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Another system maintains order history
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And other systems store customer support, behavioral information and third-party data
Together, this data provides a comprehensive view of the customer. However, these different datasets are independent, which makes answering certain questions — like what types of orders result in the highest customer support costs — very difficult.
Data engineering unifies these data sets and lets you find answers to your questions quickly and efficiently.
