Today, every business centers around data. It makes the study of data mandatory. As soon as you hear the phrase ‘study of data’, words like data science, data engineering pops into your mind. Often, people use data science and data engineering interchangeably that is incorrect. So, let us try to draw a line between data science and data engineering service first.
Analysis of data is a quite challenging task as data is managed by different technologies in different structures. Data science tools assume that data coming from various sources are stored in the same format. But the reality is far away from this assumption. Here, data engineering solutions come into the picture. Data engineering services are designed to make data more accessible and interpretable for data consumers. They transform data into a uniform structure so that it can be easily comprehended by data scientists and analysts.
Data science is about consuming data curated by data engineers. They further analyze these data and communicate their insights using data visualization tools.
Responsibilities of Data Engineering Solutions
Crucial responsibilities of data engineering as a service are:
- Data requirement gathering: It answers the questions of how long data needs to be stored, what is its usage, who should access this data.
- Maintaining metadata: It is responsible for maintaining information about data such as the schema, the data size, the source of data, and the owner of the data.
- Ensure Security and governance: It talks about what central security protocols are used, how data is encrypted, and how to audit the data.
- Data Storage: It finds out the most optimum platform to store the data.
- Data processing: It transforms and enriches the data, summarizes it, and stores it in the storage system.
For upholding these responsibilities of data engineering as a service, data engineers perform the following tasks:
- Acquire data from different data sources
- Cleanse the data and remove the errors.
- Convert data from one format to another.
- Interprets data that has multiple meanings.
- Removes duplicates copies of the data
How can your organization benefit from Data Engineering as a Service?
- It will help in simplifying your data architecture that will reduce costs and increase profits.
- It will assist in speeding up the process of accessing insights.
- It will act as an unsung hero for improving your product quality and user experience.
- It helps in making better decisions based on the knowledge you gain from the data.
- Better aggregation of data will help you in identifying new business opportunities.
Applications of Data Engineering Solutions
1. Automating Data Processes:
Data engineering services will enable you to convert your old processes into the automated pipeline. These automated pipelines can manage from simple file transfer to complex data processing and modeling using various tools and technologies.
2. Serverless Data Processes:
Data engineering solutions will promote migration to the cloud by creating serverless data processes using cloud-based products. One can select the cloud services and platforms based on the requirements.
3. Data Processes in Dockers:
Data engineering as a service will help you in developing your data processes into dockers. It makes the deployment of data processes to the client’s production environment significantly easy. It helps in duplicating and deploying these processes to multiple systems readily.
4. NLP and Data Analytics:
Data engineering services create a master data set and help in building data pipeline-driven ML engines. Automated document tagging for knowledge management documents will aid in natural language processing.
Conclusion
Everyone is focusing on data science to draw insights and leverage the benefit from the data. What people forget is to optimize and improve the data science processes. Data engineering services let you simplify existing data science solutions and adds value to the business by saving costs and time.
Reach out to us at contact.us@virtuetechinc.com to find out the scope of data engineering in your business.
