Data Engineering

Data Engineering is the process of building data pipelines to transform and transport data throughout an organization ensuring it is available in the correct format to enable maximum value extraction from downstream activities such as Data Analysis and Data Science. Data engineering supports one of the key elements in maturing your organization from the simple Descriptive stage to a Reactive stage of data usage is Business intelligence.  Data pipelines and information architecture play a key role in extracting value from your data and Cognitell can help your organization.

As the amount of data your organization produces or controls increases, different tools and techniques are required to build your data pipelines. Cognitell selects the right mix of technologies and applies them to your organization.

Regardless of where your organizational capabilities are today, Cognitell can help you design and implement your transactional, data warehouse, or data lake solution.

Cloud Data Engineering 

Many organizations are finding it beneficial to move their data engineering operations to the cloud. Cognitell has relationships with Microsoft and AWS and has capabilities to work in the Google Cloud Platform as well. If your organization is already a customer of one of the big three cloud vendors, we can help maximize the use of their specific set of tools. If you are not already using one of these cloud platforms, our team stands ready to help you select the vendor to fit your organization.   

Data Governance 

Once organizations start utilizing data for more than transactional systems, the need for data governance quickly comes to the forefront.  Data governance refers to the systems and processes in place to manage the availability, integrity, security, and usefulness of data. It consists of developing data standards and policies that span the organization so data from disparate sources within the enterprise can be combined to present a cohesive and coherent view of the business.  These policies are an important step towards breaking down data silos.  Cognitell can help your organizations develop policies, standards, and practices that match your organizational needs and technical capabilities.  

ML Ops 

Machine Learning Operations (ML Ops) is a practice that spans data engineering, machine learning, and development operations (DevOps).  The goal is to develop processes and practices to enable continuous delivery of value from your data. Advanced statistical and machine learning models consist of both code and data and ensuring the correct version of code is deployed alongside data that is compatible with the specific version of the code is a delicate (sometimes fragile) endeavor. From a data engineering perspective, coordinating with statistical data analysts and machine learning engineers to ensure data are delivered correctly and efficiently is a critical function of the data pipeline. There is more to ML Ops than just data engineering and you can read more about ML Ops on our Data Analytics and Data Science pages but regardless of your organization’s needs, Cognitell can help develop and mature your ML Ops.  

Why Choose Cognitell? 

Organizations still operating with simple data sharing and canned reporting are in the Descriptive data usage stage and can benefit from data engineering solutions to move to a Reactive mode of data usage.  By improving in this area, organizations can begin to inform tactical business decisions with enterprise-wide data. Cognitell will help your organization develop your data engineering capabilities regardless of your current organizational skill level.  

Data Engineering is the process of building data pipelines to transform and transport data throughout an organization ensuring it is available in the correct format to enable maximum value extraction from downstream activities such as Data Analysis and Data Science. Data engineering supports one of the key elements in maturing your organization from the simple Descriptive stage to a Reactive stage of data usage is Business intelligence.  Data pipelines and information architecture play a key role in extracting value from your data and Cognitell can help your organization.

As the amount of data your organization produces or controls increases, different tools and techniques are required to build your data pipelines. Cognitell selects the right mix of technologies and applies them to your organization.

Regardless of where your organizational capabilities are today, Cognitell can help you design and implement your transactional, data warehouse, or data lake solution.

Cloud Data Engineering 

Many organizations are finding it beneficial to move their data engineering operations to the cloud. Cognitell has relationships with Microsoft and AWS and has capabilities to work in the Google Cloud Platform as well. If your organization is already a customer of one of the big three cloud vendors, we can help maximize the use of their specific set of tools. If you are not already using one of these cloud platforms, our team stands ready to help you select the vendor to fit your organization.   

Data Governance 

Once organizations start utilizing data for more than transactional systems, the need for data governance quickly comes to the forefront.  Data governance refers to the systems and processes in place to manage the availability, integrity, security, and usefulness of data. It consists of developing data standards and policies that span the organization so data from disparate sources within the enterprise can be combined to present a cohesive and coherent view of the business.  These policies are an important step towards breaking down data silos.  Cognitell can help your organizations develop policies, standards, and practices that match your organizational needs and technical capabilities.  

ML Ops 

Machine Learning Operations (ML Ops) is a practice that spans data engineering, machine learning, and development operations (DevOps).  The goal is to develop processes and practices to enable continuous delivery of value from your data. Advanced statistical and machine learning models consist of both code and data and ensuring the correct version of code is deployed alongside data that is compatible with the specific version of the code is a delicate (sometimes fragile) endeavor. From a data engineering perspective, coordinating with statistical data analysts and machine learning engineers to ensure data are delivered correctly and efficiently is a critical function of the data pipeline. There is more to ML Ops than just data engineering and you can read more about ML Ops on our Data Analytics and Data Science pages but regardless of your organization’s needs, Cognitell can help develop and mature your ML Ops.  

Why Choose Cognitell? 

Organizations still operating with simple data sharing and canned reporting are in the Descriptive data usage stage and can benefit from data engineering solutions to move to a Reactive mode of data usage.  By improving in this area, organizations can begin to inform tactical business decisions with enterprise-wide data. Cognitell will help your organization develop your data engineering capabilities regardless of your current organizational skill level.