Data Engineering Services
Channelizing disparate data into meaningful form
Data, Data everywhere!
There is so much data that it is impossible to collect and manage it all. This can get very discouraging for organizations as they end up missing a great opportunity to collect, segregate and mine data into its most useful and meaningful form. Data is available but is also lost and that is the irony. Most of the data is not used at all.
As organizations move towards digitalization, data will only pile up and may eventually end up as waste, if it is not properly engineered and made use of. But how can business analysts and decision makers make sense of the vast amounts of data that is thrown all over and in all formats? This is where Data Engineering comes into play.
What is Data Engineering?
Data Engineering is that part of Data Science which is associated with transforming disparate data into meaningful pipelines. It is about bringing sense to raw data that is spread all over. Organizations have data in different formats and across different data bases, this makes it very difficult for them to collect and segregate data let alone analysing it.
Data engineers are the specialists who ensure raw data is ultimately processed into data that makes sense. A data engineer is entrusted with creating data pipelines from the vast data that is stored. It takes skill to do so. At GYTWorkz, we have trained data engineers skilled at building data pipelines that later allow data scientists with the help of tools to process and analyse the same.
Our approach to data engineering capabilities goes beyond helping clients merely ‘’structure’’ data; we engineer data to help clients with access to powerful insights that will aid in smart decision making resulting in great business value. We help build Data Lakes, Data Pipelines and Data Analytics to help our clients make sense of all unused and unstructured data that can be finally put to good use.
Data Pipeline – an overview
The operations process in a data pipeline –
- Ingestion – Gather the required data
- Processing – Process the data to get insights and end results
- Storage – Store the insights for a quick retrieval
- Access – Enable quick access to the insights and end results through software tools
GYTWorkz approach to Data Engineering
Study and understand Organization’s data
Conceptualize a Big Data Strategy
Deploy data through big data assessment
Defining solution architecture
Develop a sound technical approach for deploying big data Address security, governance and compliance requirements
Maintain & Support
Deploy and integrate big data Operationalize big data infrastructure Provide the required support
Data Engineering to Business Goals
Important decisions need to be taken to enable organizations to move ahead and grow. But these important decisions need to be based on intelligence and experience from the market. Data engineering is the first step towards taking intelligent decisions based on relevant insights that otherwise would not have been possible.
At the CXO level, the picture looks different. Important decisions need to be taken and there is no luxury of time and no option for failure. This calls for a more strengthened and collaborative partnership between the CIOs and other functional heads. The IT teams have a major role to play as far as structuring the humongous amount of data so the business or strategic teams make sense of it all. Data engineering is the first step towards turning data into relevant strategies.
Data Engineering helps achieve business goals
Execute Data Engineering – The IT teams take lead in efficiently addressing the data challenges through collection, processing, cleaning and storing of data
Data Analysis – The analytics teams make sense of the data and uncover intelligence from it through various useful insights
Business Decisions – The decision makers are now left with the most refined data in the form of intelligent insights to enable them to take smart business decisions
Achieve Goals – All of these efforts must translate to achieving the set objectives and data engineering just about ignites the entire process
Staying Ahead – Achieving the desired business goals helps organizations stay well ahead of the curve and the competition